<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">medalphabet</journal-id><journal-title-group><journal-title xml:lang="ru">Медицинский алфавит</journal-title><trans-title-group xml:lang="en"><trans-title>Medical alphabet</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2078-5631</issn><issn pub-type="epub">2949-2807</issn><publisher><publisher-name>ООО «Альфмед»</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.33667/2078-5631-2020-20-21-29</article-id><article-id custom-type="elpub" pub-id-type="custom">medalphabet-1649</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>Статьи</subject></subj-group></article-categories><title-group><article-title>Радиогеномика рака молочной железы - новый вектор междисциплинарной интеграции лучевых и молекулярнобиологических технологий(обзор литературы)</article-title><trans-title-group xml:lang="en"><trans-title>Radiogenomics of breast cancer as new vector of interdisciplinary integration of radiation and molecular biological technologies (literature review)</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Рожкова</surname><given-names>Н. И.</given-names></name><name name-style="western" xml:lang="en"><surname>Rozhkova</surname><given-names>N. I.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Доктор медицинских наук, проф., заслуженный деятель науки России, президент Российской ассоциации маммологов, рук. Национального центра онкологии репродуктивных органов МНИОИ им. П. А. Герцена,  проф. кафедры клинической маммологии, лучевой диагностики и лучевой терапии РУДН.</p><p>Москва</p></bio><bio xml:lang="en"><p>Moscow</p></bio><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Боженко</surname><given-names>В. К.</given-names></name><name name-style="western" xml:lang="en"><surname>Bozhenko</surname><given-names>V. K.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Доктор медицинских наук, проф., заслуженный врач России, зав. научно-исследовательским отделом молекулярной биологии и экспериментальной терапии опухолей.</p><p>Москва</p></bio><bio xml:lang="en"><p>Moscow</p></bio><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Бурдина</surname><given-names>И. И.</given-names></name><name name-style="western" xml:lang="en"><surname>Burdina</surname><given-names>I. I.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Кандидат медицинских наук, старший научный сотрудник Национального центра онкологии репродуктивных органов.</p><p>Москва</p></bio><bio xml:lang="en"><p>Moscow</p></bio><xref ref-type="aff" rid="aff-3"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Запирова</surname><given-names>С. Б.</given-names></name><name name-style="western" xml:lang="en"><surname>Zapirova</surname><given-names>S. B</given-names></name></name-alternatives><bio xml:lang="ru"><p>Кандидат медицинских наук, старший научный сотрудник Национального центра онкологии репродуктивных органов.</p><p>Москва</p></bio><bio xml:lang="en"><p>Moscow</p></bio><xref ref-type="aff" rid="aff-3"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Кудинова</surname><given-names>Е. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Kudinova</surname><given-names>E. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Кандидат медицинских наук, зав. клинико-диагностической лаборатории.</p><p>Москва</p></bio><bio xml:lang="en"><p>Moscow</p></bio><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Лабазанова</surname><given-names>П. Г.</given-names></name><name name-style="western" xml:lang="en"><surname>Labazanova</surname><given-names>P. G.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Младший научный сотрудник Национального центра онкологии репродуктивных органов.</p><p>Москва</p></bio><bio xml:lang="en"><p>Moscow</p></bio><xref ref-type="aff" rid="aff-3"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Мазо</surname><given-names>М. Л.</given-names></name><name name-style="western" xml:lang="en"><surname>Mazo</surname><given-names>M. L.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Кандидат медицинских наук, ген. секретарь Российской ассоциации маммологов, старший научный сотрудник Национального центра онкологии репродуктивных органов МНИОИ им. П. А. Герцена, доцент кафедры клинической маммологии, лучевой диагностики и лучевой терапии ФПК МР РУДН.</p><p>Москва</p></bio><bio xml:lang="en"><p>Moscow</p></bio><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Микушин</surname><given-names>С. Ю.</given-names></name><name name-style="western" xml:lang="en"><surname>Mikushin</surname><given-names>S. Yu.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Кандидат медицинских наук, научный сотрудник Национального центра онкологии репродуктивных органов.</p></bio><bio xml:lang="en"><p>Moscow</p></bio><xref ref-type="aff" rid="aff-3"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Прокопенко</surname><given-names>С. П.</given-names></name><name name-style="western" xml:lang="en"><surname>Prokopenko</surname><given-names>S. P.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Кандидат медицинских наук, зав. отделением комплексной диагностики и интервенционной радиологии в маммологии Национального центра онкологии репродуктивных органов МНИОИ им. П. А. Герцена, зав. кафедрой клинической маммологии, лучевой диагностики и лучевой терапии ФПК МР РУДН.</p><p>Москва</p></bio><bio xml:lang="en"><p>Moscow</p></bio><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Якобс</surname><given-names>О. Э.</given-names></name><name name-style="western" xml:lang="en"><surname>Yakobs</surname><given-names>O. E.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Доктор медицинских наук, старший научный сотрудник Национального центра онкологии репродуктивных органов МНИОИ им. П. А. Герцена, доцент кафедры клинической маммологии, лучевой диагностики и лучевой терапии ФПК М Медицинский институт ФГАОУ ВО РУДН.</p><p>Москва</p></bio><bio xml:lang="en"><p>Moscow</p></bio><xref ref-type="aff" rid="aff-4"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>МНИОИ им. П. А. Герцена - филиал ФГБУ «Национальный медицинский исследовательский радиологический центр» Минздрава России; Медицинский институт ФГАОУ ВО Российский университет дружбы народов</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Moscow Research Institute n.a. P. A. Herzen - a Branch of the National Medical Radiological Research Centre; People’s Friendship University of Russia</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>ФГБУ «Российский научный центр рентгенорадиологии» Минздрава России</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Scientific Centre of Roentgenoradiology</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-3"><aff xml:lang="ru"><institution>Московский научно-исследовательский онкологический институт имени П. А. Герцена - филиал ФГБУ «Национальный медицинский исследовательский радиологический центр» Минздрава России</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Moscow Research Institute n.a. P. A. Herzen - a Branch of the National Medical Radiological Research Centre</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-4"><aff xml:lang="ru"><institution>Московский научно-исследовательский онкологический институт имени П. А. Герцена - филиал ФГБУ «Национальный медицинский исследовательский радиологический центр» Минздрава России</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Moscow Research Institute n.a. P. A. Herzen - a Branch of the National Medical Radiological Research Centre; People’s Friendship University of Russia</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2020</year></pub-date><pub-date pub-type="epub"><day>10</day><month>10</month><year>2020</year></pub-date><volume>0</volume><issue>20</issue><issue-title>Диагностика и онкотерапия (2)</issue-title><fpage>21</fpage><lpage>29</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Рожкова Н.И., Боженко В.К., Бурдина И.И., Запирова С.Б., Кудинова Е.А., Лабазанова П.Г., Мазо М.Л., Микушин С.Ю., Прокопенко С.П., Якобс О.Э., 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder xml:lang="ru">Рожкова Н.И., Боженко В.К., Бурдина И.И., Запирова С.Б., Кудинова Е.А., Лабазанова П.Г., Мазо М.Л., Микушин С.Ю., Прокопенко С.П., Якобс О.Э.</copyright-holder><copyright-holder xml:lang="en">Rozhkova N.I., Bozhenko V.K., Burdina I.I., Zapirova S.B., Kudinova E.A., Labazanova P.G., Mazo M.L., Mikushin S.Y., Prokopenko S.P., Yakobs O.E.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.med-alphabet.com/jour/article/view/1649">https://www.med-alphabet.com/jour/article/view/1649</self-uri><abstract><p>В обзоре представлены последние данные о развитии нового направления междисциплинарной интеграции цифровых лучевых и молекулярно-биологических технологий omfcs, включающих высокие технологии в области геномики, транскриптомики, протеомики и метаболомики, которые являются основой системной биологии и будущего медицины. Интеграция медицинской визуализациии и достижений генетики породили новое направление научных исследований - радиогеномику, являющуюся ключевым шагом в развитии от/сэ-технологий. Радиогеномика - фенотип визуализации, компьютерное зрение - представляет междисциплинарную интеграцию визуальной радиологии и биологических систем, изучающих биомедицинские изображения, включающие фенотипические и генотипические параметры, отражающие молекулярную и генотипическую основу ткани, по которым можно предсказать риск РМЖ и результаты лечения пациентов. Связанные с современными аналитическими программными средствами количественные и качественные биомаркеры визуализации приносят беспрецедентное понимание сложной биологии опухоли и способствуют более глубокому знанию развития и прогрессирования рака. Используя последние достижения цифровых, информационных и молекулярно-биологических технологий, ведется активное сближение специальностей радиолога и генетика, давая возможность уже на этапе изучения медицинских изображений молочной железы получать информацию о биологической характеристике опухоли, молекулярном подтипе рака, определяющем прогноз заболевания, оценку степени риска рецидива, что является важным для выбора адекватной индивидуальной тактики мониторинга и выбора лечебного пособия. Разработка визуальных симптомокомплексов медицинских изображений молочной железы, характерных для разных молекулярных подтипов рака, будет способствовать уточненной диагностике разных проявлений рака, выбору адекватной лечебной тактики, способствующей увеличению продолжительности и сохранению высокого качества жизни женщины.</p></abstract><trans-abstract xml:lang="en"><p>The review presents the latest data on the development of a new direction of interdisciplinary integration of radiation and molecular biological technologies ‘omiсs', including high technologies in the field of genomics, transcriptomics, proteomics and metabo-lomics, which are the basis of systems biology and the future of medicine. The integration of medical imaging and advances in genetics have created a new direction of research - radiogenomics, which is a key step in the development of omhs-technologies. Radiogenomics - phenotype imaging, computer vision - is an interdisciplinary integration of visual radiology and biological systems that study biomedical imaging involving phenotypic and genotypic parameters that reflect the molecular and genotypic basis of tissue from which to predict patient risk and outcomes. Coupled with state-of-the-art analytical software, quantitative and qualitative imaging biomarkers bring unprecedented insight into complex tumor biology and contribute to a deeper understanding of cancer development and progression. Using the latest advances in digital, information and molecular biological technology, is an active convergence of specialties radiologist and genetics, giving the opportunity at the stage of studying medical images of the breast to obtain information about the biological characteristics of the tumor molecular subtype of cancer, determining prognosis, evaluating risk of recurrence, which is important for the choice of adequate tactics of individual monitoring and selection of medical benefits. Development of visual symptom medical images of the breast, characteristic for different molecular subtypes of cancer, will contribute to more accurate diagnosis of different manifestations of cancer, the choice of adequate treatment tactics that increase the duration and preservation of the high quality of a woman s life.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>молочная железа</kwd><kwd>лучевая диагностика</kwd><kwd>молекулярно-генетические исследования</kwd><kwd>рак</kwd><kwd>радиогеномика</kwd></kwd-group><kwd-group xml:lang="en"><kwd>mammary gland</kwd><kwd>radiation diagnostics</kwd><kwd>molecular genetic studies</kwd><kwd>cancer</kwd><kwd>radiogenomics</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">American College of Radiology (ACR): ACR BIRADS fifth edition: Breast imaging reporting and data system, Breast Imaging Atlas. Reston,-2013.</mixed-citation><mixed-citation xml:lang="en">American College of Radiology (ACR): ACR BIRADS fifth edition: Breast imaging reporting and data system, Breast Imaging Atlas. Reston,-2013.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018. International Agency for Research on Cancer. World Health Organization. 2018. Режим доступа: https://www.iarc.fr/en/media-centre/pr/2018/pdfs/pr263_E.pdf.</mixed-citation><mixed-citation xml:lang="en">Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018. International Agency for Research on Cancer. World Health Organization. 2018. Режим доступа: https://www.iarc.fr/en/media-centre/pr/2018/pdfs/pr263_E.pdf.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Mammography Quality Standards Act and Program [электронныйресурс] 2017. Режим доступа: https://www.fda.gov/radiation-emit-tingproducts/mammographyqualitystandards-actandprogram/default.htm.</mixed-citation><mixed-citation xml:lang="en">Mammography Quality Standards Act and Program [электронныйресурс] 2017. Режим доступа: https://www.fda.gov/radiation-emit-tingproducts/mammographyqualitystandards-actandprogram/default.htm.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">WHO’s International Agency for Research on Cancer [Электронный ресурс]. 2018. Режимдоступа: http://gco.iarc.fr/</mixed-citation><mixed-citation xml:lang="en">WHO’s International Agency for Research on Cancer [Электронный ресурс]. 2018. Режимдоступа: http://gco.iarc.fr/</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">World Health Organization. Early diagnosis and screening of breast cancer [Электронный ресурс]. 2018. Режим доступа: http://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/.</mixed-citation><mixed-citation xml:lang="en">World Health Organization. Early diagnosis and screening of breast cancer [Электронный ресурс]. 2018. Режим доступа: http://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Злокачественные новообразования в России в 2017 году (заболеваемость и смертность). Под ред. А.Д. Каприна, 6. 6. Старинского, Г. В. Петровой. М. 2018.-263 с.</mixed-citation><mixed-citation xml:lang="en">Злокачественные новообразования в России в 2017 году (заболеваемость и смертность). Под ред. А.Д. Каприна, 6. 6. Старинского, Г. В. Петровой. М. 2018.-263 с.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Genetics Home Reference. Lister Hill National Center for Biomedical Communications, U. S. National Library of Medicine, National Institutes of Health, Department of Health &amp; Human Services. What is precision medicine?https://ghr.nlm.nih.gov/primer/precisionmedicine/definition. Accessed September 8,2017. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Genetics Home Reference. Lister Hill National Center for Biomedical Communications, U. S. National Library of Medicine, National Institutes of Health, Department of Health &amp; Human Services. What is precision medicine?https://ghr.nlm.nih.gov/primer/precisionmedicine/definition. Accessed September 8,2017. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015; 372 (9): 793-795. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015; 372 (9): 793-795. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Rahman M, Hasan MR. Cancer metabolism and drug resistance. Metabolites 2015; 5 (4): 571-600. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Rahman M, Hasan MR. Cancer metabolism and drug resistance. Metabolites 2015; 5 (4): 571-600. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Tang J, Karhinen L, Xu T et al. Target inhibition networks: predicting selective combinations of drug-gable targets to block cancer survival pathways. PLOS Comput Biol 2013; 9 (9): e1003226. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Tang J, Karhinen L, Xu T et al. Target inhibition networks: predicting selective combinations of drug-gable targets to block cancer survival pathways. PLOS Comput Biol 2013; 9 (9): e1003226. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Bettaieb A, Paul C, Plenchette S, Shan J, Chouch-ane L, Ghiringhelli F. Precision medicine in breast cancer, reality or utopia? J Transl Med 2017; 15 (1): 139. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Bettaieb A, Paul C, Plenchette S, Shan J, Chouch-ane L, Ghiringhelli F. Precision medicine in breast cancer, reality or utopia? J Transl Med 2017; 15 (1): 139. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Atkins MB, Larkin J. Immunotherapy combined or sequenced with targeted therapy in the treatment of solid tumors: current perspectives. J Natl Cancer Inst 2016; 108 (6): djv414. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Atkins MB, Larkin J. Immunotherapy combined or sequenced with targeted therapy in the treatment of solid tumors: current perspectives. J Natl Cancer Inst 2016; 108 (6): djv414. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Reuben A, Spencer CN, Prieto PA et al. Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma. NPJ Genom Med. 2017; 2: 2. Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Reuben A, Spencer CN, Prieto PA et al. Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma. NPJ Genom Med. 2017; 2: 2. Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Foth M, Wouters J, de Chaumont C, Dynoodt P, Gallagher WM. Prognostic and predictive biomarkers in melanoma: an update. Expert Rev Mol Diagn 2016; 16 (2): 223-237. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Foth M, Wouters J, de Chaumont C, Dynoodt P, Gallagher WM. Prognostic and predictive biomarkers in melanoma: an update. Expert Rev Mol Diagn 2016; 16 (2): 223-237. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Mari-Alexandre J, Diaz-Lagares A, Villalba M et al. Translating cancer epigenomics into the clinic: focus on lung cancer. Transl Res 2017; 189: 76-92. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Mari-Alexandre J, Diaz-Lagares A, Villalba M et al. Translating cancer epigenomics into the clinic: focus on lung cancer. Transl Res 2017; 189: 76-92. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Sundar R, Chenard-Poiner M, Collins DC, Yap TA. Imprecision in the era of precision medicine in non-small cell lung cancer.Front Med (Lausanne) 2017; 4: 39. Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Sundar R, Chenard-Poiner M, Collins DC, Yap TA. Imprecision in the era of precision medicine in non-small cell lung cancer.Front Med (Lausanne) 2017; 4: 39. Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Ruiz-Ceja KA, Chrino YI. Current FDA-approved treatments for non-small cell lung cancer and potential biomarkers for its detection. Biomed Pharmacother 2017; 90:24-37. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Ruiz-Ceja KA, Chrino YI. Current FDA-approved treatments for non-small cell lung cancer and potential biomarkers for its detection. Biomed Pharmacother 2017; 90:24-37. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Rosenbaum JN, Weisman P. The evolving role of companion diagnostics for breast cancer in an era of next-generation omics.Am J Pathol 2017; 187 (10): 2185-2198. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Rosenbaum JN, Weisman P. The evolving role of companion diagnostics for breast cancer in an era of next-generation omics.Am J Pathol 2017; 187 (10): 2185-2198. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Dey N, De P, Leyland-Jones B. PI3K-AKT-mTOR inhibitors in breast cancers: from tumor cell signaling to clinical trials. Pharmacol Ther2017; 175: 91-106. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Dey N, De P, Leyland-Jones B. PI3K-AKT-mTOR inhibitors in breast cancers: from tumor cell signaling to clinical trials. Pharmacol Ther2017; 175: 91-106. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Hobbs SK, Shi G, Homer R, Harsh G, Atlas SW, Bed-narski MD. Magnetic resonance image-guided proteomics of human glioblastoma multiforme. J Magn Reson Imaging 2003; 18 (5): 530-536. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Hobbs SK, Shi G, Homer R, Harsh G, Atlas SW, Bed-narski MD. Magnetic resonance image-guided proteomics of human glioblastoma multiforme. J Magn Reson Imaging 2003; 18 (5): 530-536. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Hodges TR, Ferguson SD, Heimberger AB. Immunotherapy in glioblastoma: emerging options in precision medicine. CNS Oncol2016; 5 (3): 175-186. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Hodges TR, Ferguson SD, Heimberger AB. Immunotherapy in glioblastoma: emerging options in precision medicine. CNS Oncol2016; 5 (3): 175-186. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Chung C, Ma H. Driving toward precision medicine for acute leukemias: are we there yet? Pharmacotherapy 2017; 37 (9): 1052-1072. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Chung C, Ma H. Driving toward precision medicine for acute leukemias: are we there yet? Pharmacotherapy 2017; 37 (9): 1052-1072. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Sikkema AH, den Dunnen WF, Diks SH, Peppelen-bosch MP, de Bont ES. Optimizing targeted cancer therapy: towards clinical application of systems biology approaches. Crit Rev Oncol Hematol2012; 82 (2): 171-186. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Sikkema AH, den Dunnen WF, Diks SH, Peppelen-bosch MP, de Bont ES. Optimizing targeted cancer therapy: towards clinical application of systems biology approaches. Crit Rev Oncol Hematol2012; 82 (2): 171-186. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Kichko K, Marschall P, Flessa S. Personalized medicine in the U.S. and Germany: awareness, acceptance, use and preconditions for the wide implementation into the medical standard. J Pers Med 2016; 6 (2): E15. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Kichko K, Marschall P, Flessa S. Personalized medicine in the U.S. and Germany: awareness, acceptance, use and preconditions for the wide implementation into the medical standard. J Pers Med 2016; 6 (2): E15. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">Peng J. Meeting Report: EMBL Conference -Omics and Personalized Medicine: February 16-18,2012, Heidelberg, Germany. Biotechnol J2012; 7 (8): 943-945. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Peng J. Meeting Report: EMBL Conference -Omics and Personalized Medicine: February 16-18,2012, Heidelberg, Germany. Biotechnol J2012; 7 (8): 943-945. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">Westerhoff HV, Palsson BO.The evolution of molecular biology into systems biology.Nat Biotechnol 2004; 22 (10): 1249-1252. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Westerhoff HV, Palsson BO.The evolution of molecular biology into systems biology.Nat Biotechnol 2004; 22 (10): 1249-1252. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Peitsch MC, de Graaf D. A decade of systems biology: where are we and where are we going to? Drug Discov Today 2014; 19 (2): 105-107. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Peitsch MC, de Graaf D. A decade of systems biology: where are we and where are we going to? Drug Discov Today 2014; 19 (2): 105-107. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Chuang HY, Hofree M, Ideker T. A decade of systems biology. Annu Rev Cell Dev Biol2010; 26 (1): 721-744. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Chuang HY, Hofree M, Ideker T. A decade of systems biology. Annu Rev Cell Dev Biol2010; 26 (1): 721-744. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">Institute for Systems Biology. What is systems biology. https://www.systemsbiology.org/about/what-is-systems-biology/. Accessed September 8, 2017. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Institute for Systems Biology. What is systems biology. https://www.systemsbiology.org/about/what-is-systems-biology/. Accessed September 8, 2017. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">Kell DB, OliverSG. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. Bio Essays 2004; 26 (1): 99-105. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Kell DB, OliverSG. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. Bio Essays 2004; 26 (1): 99-105. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">Dey N, Williams C, Leyland-Jones B, De P. Mutation matters in precision medicine: a future to believe in. Cancer Treat Rev2017; 55: 136-149. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Dey N, Williams C, Leyland-Jones B, De P. Mutation matters in precision medicine: a future to believe in. Cancer Treat Rev2017; 55: 136-149. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">Bellazzi R. Big data and biomedical informatics: a challenging opportunity. Yearb Med Inform 2014; 9 (1): 8-13. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Bellazzi R. Big data and biomedical informatics: a challenging opportunity. Yearb Med Inform 2014; 9 (1): 8-13. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit33"><label>33</label><citation-alternatives><mixed-citation xml:lang="ru">International Cancer Genome Consortium; Hudson TJ. Anderson W. et al. International network of cancer genome projects. Nature 2010; 464 (7291): 993-998. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">International Cancer Genome Consortium; Hudson TJ. Anderson W. et al. International network of cancer genome projects. Nature 2010; 464 (7291): 993-998. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit34"><label>34</label><citation-alternatives><mixed-citation xml:lang="ru">Collins FS, Barker AD. Mapping the cancer genome: pinpointing the genes involved in cancer will help chart a new course across the complex landscape of human malignancies.Sci Am 2007; 296 (3): 50-57. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Collins FS, Barker AD. Mapping the cancer genome: pinpointing the genes involved in cancer will help chart a new course across the complex landscape of human malignancies.Sci Am 2007; 296 (3): 50-57. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit35"><label>35</label><citation-alternatives><mixed-citation xml:lang="ru">Carrasco-Ramiro F, Peiro-Pastor R, Aguado B. Human genomics projects and precision medicine. Gene Ther2017; 24 (9): 551-561. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Carrasco-Ramiro F, Peiro-Pastor R, Aguado B. Human genomics projects and precision medicine. Gene Ther2017; 24 (9): 551-561. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit36"><label>36</label><citation-alternatives><mixed-citation xml:lang="ru">Casamassimi A, Federico A, Rienzo M, Esposito S, Ciccodicola A. Transcriptome profiling in human diseases: new advances and perspectives. Int J Mol Sci2017; 18 (8): E 1652. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Casamassimi A, Federico A, Rienzo M, Esposito S, Ciccodicola A. Transcriptome profiling in human diseases: new advances and perspectives. Int J Mol Sci2017; 18 (8): E 1652. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit37"><label>37</label><citation-alternatives><mixed-citation xml:lang="ru">U. S. National Library of Medicine. Genomics: MeSH descriptor data 2017. https://meshb.nlm.nih.gov/ record/ui?ui=D 023281. Accessed September 8, 2017. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">U. S. National Library of Medicine. Genomics: MeSH descriptor data 2017. https://meshb.nlm.nih.gov/ record/ui?ui=D 023281. Accessed September 8, 2017. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit38"><label>38</label><citation-alternatives><mixed-citation xml:lang="ru">U. S. National Library of Medicine. Genetics MeSH descriptor data 2017.https://meshb.nlm.nih.gov/record/ui?ui=D005823. Accessed September 8, 2017. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">U. S. National Library of Medicine. Genetics MeSH descriptor data 2017.https://meshb.nlm.nih.gov/record/ui?ui=D005823. Accessed September 8, 2017. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit39"><label>39</label><citation-alternatives><mixed-citation xml:lang="ru">Lander ES, Linton LM, Birren B et al. Initial sequencing and analysis of the human genome. Nature 2001; 409 (6822): 860-921. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Lander ES, Linton LM, Birren B et al. Initial sequencing and analysis of the human genome. Nature 2001; 409 (6822): 860-921. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit40"><label>40</label><citation-alternatives><mixed-citation xml:lang="ru">Venter JC, Smith HO, Adams MD. The Sequence of the human genome. Clin Chem 2015; 61 (9): 1207-1208. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Venter JC, Smith HO, Adams MD. The Sequence of the human genome. Clin Chem 2015; 61 (9): 1207-1208. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit41"><label>41</label><citation-alternatives><mixed-citation xml:lang="ru">National Human Genome Research Institute. What is genomic medicine? https://www.genome.gov/27552451/what-is-genomic-medicine/. Accessed September8, 2017. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">National Human Genome Research Institute. What is genomic medicine? https://www.genome.gov/27552451/what-is-genomic-medicine/. Accessed September8, 2017. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit42"><label>42</label><citation-alternatives><mixed-citation xml:lang="ru">Kamel HFM, Al-Amodi HSAB. Exploitation of gene expression and cancer biomarkers in paving the path to era of personalized medicine. Genomics Proteomics Bioinformatics 2017; 15 (4): 220-235. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Kamel HFM, Al-Amodi HSAB. Exploitation of gene expression and cancer biomarkers in paving the path to era of personalized medicine. Genomics Proteomics Bioinformatics 2017; 15 (4): 220-235. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit43"><label>43</label><citation-alternatives><mixed-citation xml:lang="ru">Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of tegrating data to uncover genotype-phenotype interactions. Nat Rev Genet2015; 16 (2): 85-97. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of tegrating data to uncover genotype-phenotype interactions. Nat Rev Genet2015; 16 (2): 85-97. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit44"><label>44</label><citation-alternatives><mixed-citation xml:lang="ru">Evans DG, Shenton A, Woodward E, Lalloo F, Howell A, Maher ER. Penetrance estimates for BRCA1 and BRCA2 based on genetic testing in a Clinical Cancer Genetics service setting: risks of breast/ ovarian cancer quoted should reflect the cancer burden in the family. BMC Cancer2008; 8 (1): 155. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Evans DG, Shenton A, Woodward E, Lalloo F, Howell A, Maher ER. Penetrance estimates for BRCA1 and BRCA2 based on genetic testing in a Clinical Cancer Genetics service setting: risks of breast/ ovarian cancer quoted should reflect the cancer burden in the family. BMC Cancer2008; 8 (1): 155. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit45"><label>45</label><citation-alternatives><mixed-citation xml:lang="ru">Riedl CC, Luff N, Bernhart C et al. Triple-modality screening trial for familial breast cancer underlines the importance of magnetic resonance imaging and questions the role of mammography and ultrasound regardless of patient mutation status, age, and breast density. J Clin Oncol 2015; 33 (10): JJ28-JJ35. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Riedl CC, Luff N, Bernhart C et al. Triple-modality screening trial for familial breast cancer underlines the importance of magnetic resonance imaging and questions the role of mammography and ultrasound regardless of patient mutation status, age, and breast density. J Clin Oncol 2015; 33 (10): JJ28-JJ35. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit46"><label>46</label><citation-alternatives><mixed-citation xml:lang="ru">Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T Tran-scriptomics technologies. PLOS Comput Biol 2017; 13 (5): e1005457. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T Tran-scriptomics technologies. PLOS Comput Biol 2017; 13 (5): e1005457. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit47"><label>47</label><citation-alternatives><mixed-citation xml:lang="ru">Lee-Liu D, Almonacid LI, Faunes F, Melo F, Lar-rain J. Transcriptomics using next generation sequencing technologies. Methods Mol Biol 2012; 917: 293-317. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Lee-Liu D, Almonacid LI, Faunes F, Melo F, Lar-rain J. Transcriptomics using next generation sequencing technologies. Methods Mol Biol 2012; 917: 293-317. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit48"><label>48</label><citation-alternatives><mixed-citation xml:lang="ru">U. S. National Library of Medicine. Transcriptome MeSH Descriptor Data 2017. https://meshb.nlm.nih.gov/record/ui?ui=D059467. Accessed September 8, 2017. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">U. S. National Library of Medicine. Transcriptome MeSH Descriptor Data 2017. https://meshb.nlm.nih.gov/record/ui?ui=D059467. Accessed September 8, 2017. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit49"><label>49</label><citation-alternatives><mixed-citation xml:lang="ru">National Human Genome Research Institute. Transcriptome. https://www.genome.gov/13014330/transcriptome-fact-sheet/. Accessed September 8, 2017. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">National Human Genome Research Institute. Transcriptome. https://www.genome.gov/13014330/transcriptome-fact-sheet/. Accessed September 8, 2017. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit50"><label>50</label><citation-alternatives><mixed-citation xml:lang="ru">HarbeckN, Thomssen C, Gnant M. St. Gallen 2013: brief preliminary summary of the consensus discussion. Breast Care (Basel) 2013; 8 (2): 102-109. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">HarbeckN, Thomssen C, Gnant M. St. Gallen 2013: brief preliminary summary of the consensus discussion. Breast Care (Basel) 2013; 8 (2): 102-109. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit51"><label>51</label><citation-alternatives><mixed-citation xml:lang="ru">Goldhirsch A, Wood WC, Coates AS et al. Strategies for subtypes-dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 2011; 22 (8): 1736-1747. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Goldhirsch A, Wood WC, Coates AS et al. Strategies for subtypes-dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 2011; 22 (8): 1736-1747. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit52"><label>52</label><citation-alternatives><mixed-citation xml:lang="ru">U. S. National Library of Medicine. Proteomics MeSH Descriptor Data 2017. https://meshb.nlm.nih.gov/record/ui?ui=D 04 0901. Accessed September 8, 2017. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">U. S. National Library of Medicine. Proteomics MeSH Descriptor Data 2017. https://meshb.nlm.nih.gov/record/ui?ui=D 04 0901. Accessed September 8, 2017. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit53"><label>53</label><citation-alternatives><mixed-citation xml:lang="ru">Weston AD, Hood L. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Pro-teome Res2004; 3 (2): 179-196. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Weston AD, Hood L. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Pro-teome Res2004; 3 (2): 179-196. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit54"><label>54</label><citation-alternatives><mixed-citation xml:lang="ru">Beck M, Claassen M, Aebersold R. Comprehensive proteomics. Curr Opin Biotechnol 2011; 22 (1): 3-8. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Beck M, Claassen M, Aebersold R. Comprehensive proteomics. Curr Opin Biotechnol 2011; 22 (1): 3-8. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit55"><label>55</label><citation-alternatives><mixed-citation xml:lang="ru">Gehlenborg N, O’Donoghue SI, Baliga NS et al. Visualization of omics data for systems biology. Nat Methods 2010; 7 (3 Suppl): S 56-S 68. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Gehlenborg N, O’Donoghue SI, Baliga NS et al. Visualization of omics data for systems biology. Nat Methods 2010; 7 (3 Suppl): S 56-S 68. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit56"><label>56</label><citation-alternatives><mixed-citation xml:lang="ru">Geho DH, Lahar N, Ferrari M, Petricoin EF, Liotta LA. Opportunities for nanotechnology-based innovation in tissue proteomics. Biomed Microdevices 2004; 6 (3): 231-239. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Geho DH, Lahar N, Ferrari M, Petricoin EF, Liotta LA. Opportunities for nanotechnology-based innovation in tissue proteomics. Biomed Microdevices 2004; 6 (3): 231-239. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit57"><label>57</label><citation-alternatives><mixed-citation xml:lang="ru">Rodriguez M, Bajo-Santos C, HessvikNP et al. Identification of non-invasive miRNAs biomarkers for prostate cancer by deep sequencing analysis of urinary exosomes. Mol Cancer 2017; 16 (1): 156. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Rodriguez M, Bajo-Santos C, HessvikNP et al. Identification of non-invasive miRNAs biomarkers for prostate cancer by deep sequencing analysis of urinary exosomes. Mol Cancer 2017; 16 (1): 156. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit58"><label>58</label><citation-alternatives><mixed-citation xml:lang="ru">0verbye A, Skotland T, Koehler CJ et al. Identification of prostate cancer biomarkers in urinary exosomes. Oncotarget2015; 6 (30): 30357-30376. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">0verbye A, Skotland T, Koehler CJ et al. Identification of prostate cancer biomarkers in urinary exosomes. Oncotarget2015; 6 (30): 30357-30376. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit59"><label>59</label><citation-alternatives><mixed-citation xml:lang="ru">Kim Y, Jeon J, Mejia S et al. Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer. Nat Commun 2016; 7: 11906. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Kim Y, Jeon J, Mejia S et al. Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer. Nat Commun 2016; 7: 11906. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit60"><label>60</label><citation-alternatives><mixed-citation xml:lang="ru">Mardamshina M, Geiger T. Next-generation proteomics and its application to clinical breast cancer research. Am J Pathol2017; 187 (10): 2175-2184. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Mardamshina M, Geiger T. Next-generation proteomics and its application to clinical breast cancer research. Am J Pathol2017; 187 (10): 2175-2184. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit61"><label>61</label><citation-alternatives><mixed-citation xml:lang="ru">Riekeberg E, Powers R. New frontiers in metabolo-mics: from measurement to insight. F1000 Res2017; 6: 1148. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Riekeberg E, Powers R. New frontiers in metabolo-mics: from measurement to insight. F1000 Res2017; 6: 1148. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit62"><label>62</label><citation-alternatives><mixed-citation xml:lang="ru">Pintus R, Bassareo PP, Dessi A, Deidda M, Mercuro G, Fanos V. Metabolomics and cardiology: toward the path of perinatal programming and personalized medicine. BioMed Res Int 2017; 2017: 6970631. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Pintus R, Bassareo PP, Dessi A, Deidda M, Mercuro G, Fanos V. Metabolomics and cardiology: toward the path of perinatal programming and personalized medicine. BioMed Res Int 2017; 2017: 6970631. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit63"><label>63</label><citation-alternatives><mixed-citation xml:lang="ru">Li B, He X, Jia W, Li H. Novel applications of metabolomics in personalized medicine: a mini-review. Molecules 2017; 22 (7): E 1173. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Li B, He X, Jia W, Li H. Novel applications of metabolomics in personalized medicine: a mini-review. Molecules 2017; 22 (7): E 1173. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit64"><label>64</label><citation-alternatives><mixed-citation xml:lang="ru">Beger RD. A review of applications of metabolomics in cancer. Metabolites 2013; 3 (3): 552-574. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Beger RD. A review of applications of metabolomics in cancer. Metabolites 2013; 3 (3): 552-574. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit65"><label>65</label><citation-alternatives><mixed-citation xml:lang="ru">U. S. National Library of Medicine. Metabolome MeSH Descriptor Data 2017. https://meshb.nlm.nih.gov/record/ui?ui=D055442. Accessed DATE. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">U. S. National Library of Medicine. Metabolome MeSH Descriptor Data 2017. https://meshb.nlm.nih.gov/record/ui?ui=D055442. Accessed DATE. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit66"><label>66</label><citation-alternatives><mixed-citation xml:lang="ru">Vander Heiden MG. Targeting cancer metabolism: a therapeutic window opens. Nat Rev Drug Discov 2011; 10 (9): 671-684. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Vander Heiden MG. Targeting cancer metabolism: a therapeutic window opens. Nat Rev Drug Discov 2011; 10 (9): 671-684. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit67"><label>67</label><citation-alternatives><mixed-citation xml:lang="ru">Brown MV, McDunn JE, Gunst PR et al. Cancer detection and biopsy classification using concurrent histopathological and metabolomic analysis of core biopsies. Genome Med 2012; 4 (4): 33. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Brown MV, McDunn JE, Gunst PR et al. Cancer detection and biopsy classification using concurrent histopathological and metabolomic analysis of core biopsies. Genome Med 2012; 4 (4): 33. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit68"><label>68</label><citation-alternatives><mixed-citation xml:lang="ru">Ghasemi M, Nabipour I, Omrani A, Alipour Z, Assadi M. Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis. Am J Nucl Med Mol Imaging 2016; 6 (6): 310-327. Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Ghasemi M, Nabipour I, Omrani A, Alipour Z, Assadi M. Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis. Am J Nucl Med Mol Imaging 2016; 6 (6): 310-327. Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit69"><label>69</label><citation-alternatives><mixed-citation xml:lang="ru">Kalita-de Croft P, Al-Ejeh F, McCart Reed AE, Sau-nus JM, Lakhani SR. ‘Omics approaches in breast cancer research and clinical practice. Adv Anat Pathol 2016; 23 (6): 356-367. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Kalita-de Croft P, Al-Ejeh F, McCart Reed AE, Sau-nus JM, Lakhani SR. ‘Omics approaches in breast cancer research and clinical practice. Adv Anat Pathol 2016; 23 (6): 356-367. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit70"><label>70</label><citation-alternatives><mixed-citation xml:lang="ru">Perou CM, S0rlie T, Eisen MB et al. Molecular portraits of human breast tumours. Nature 2000; 406 (6797): 747-752. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Perou CM, S0rlie T, Eisen MB et al. Molecular portraits of human breast tumours. Nature 2000; 406 (6797): 747-752. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit71"><label>71</label><citation-alternatives><mixed-citation xml:lang="ru">Curtis C. Genomic profiling of breast cancers.Curr Opin Obstet Gynecol 2015; 27 (1): 34-39. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Curtis C. Genomic profiling of breast cancers.Curr Opin Obstet Gynecol 2015; 27 (1): 34-39. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit72"><label>72</label><citation-alternatives><mixed-citation xml:lang="ru">Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012; 490 (7418): 61-70. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012; 490 (7418): 61-70. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit73"><label>73</label><citation-alternatives><mixed-citation xml:lang="ru">Goldhirsch A, Winer EP, Coates AS et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 2013; 24 (9): 2206-2223. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Goldhirsch A, Winer EP, Coates AS et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 2013; 24 (9): 2206-2223. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit74"><label>74</label><citation-alternatives><mixed-citation xml:lang="ru">MazurowskiMA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology 2014; 273 (2): 365-372. Link, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">MazurowskiMA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology 2014; 273 (2): 365-372. Link, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit75"><label>75</label><citation-alternatives><mixed-citation xml:lang="ru">Huber KE, Carey LA, Wazer DE. Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy. Semin Radiat Oncol 2009; 19 (4): 204-210. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Huber KE, Carey LA, Wazer DE. Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy. Semin Radiat Oncol 2009; 19 (4): 204-210. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit76"><label>76</label><citation-alternatives><mixed-citation xml:lang="ru">Lam SW, Jimenez CR, Boven E. Breast cancer classification by proteomic technologies: current state of knowledge. Cancer Treat Rev 2014; 40 (1): 129-138. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Lam SW, Jimenez CR, Boven E. Breast cancer classification by proteomic technologies: current state of knowledge. Cancer Treat Rev 2014; 40 (1): 129-138. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit77"><label>77</label><citation-alternatives><mixed-citation xml:lang="ru">Carey LA, Perou CM, Livasy CA et al. Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA2006; 295 (21): 2492-2502. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Carey LA, Perou CM, Livasy CA et al. Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA2006; 295 (21): 2492-2502. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit78"><label>78</label><citation-alternatives><mixed-citation xml:lang="ru">Tsoutsou PG, Vozenin MC, Durham AD, Bourhis J. How could breast cancer molecular features contribute to locoregional treatment decision making? Crit Rev Oncol Hematol2017; 110: 43-48. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Tsoutsou PG, Vozenin MC, Durham AD, Bourhis J. How could breast cancer molecular features contribute to locoregional treatment decision making? Crit Rev Oncol Hematol2017; 110: 43-48. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit79"><label>79</label><citation-alternatives><mixed-citation xml:lang="ru">Iborra S, Stickeler E. HER2-orientated therapy in early and metastatic breast cancer. Breast Care (Basel) 2016; 11 (6): 392-397. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Iborra S, Stickeler E. HER2-orientated therapy in early and metastatic breast cancer. Breast Care (Basel) 2016; 11 (6): 392-397. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit80"><label>80</label><citation-alternatives><mixed-citation xml:lang="ru">Jatoi I, Anderson WF, Jeong JH, Redmond CK. Breast cancer adjuvant therapy: time to consider its time-dependent effects. J Clin Oncol 2011; 29 (17): 2301-2304. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Jatoi I, Anderson WF, Jeong JH, Redmond CK. Breast cancer adjuvant therapy: time to consider its time-dependent effects. J Clin Oncol 2011; 29 (17): 2301-2304. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit81"><label>81</label><citation-alternatives><mixed-citation xml:lang="ru">Metzger-Filho O, Sun 1, Viale G et al. Patterns of Recurrence and outcome according to breast cancer subtypes in lymph node-negative disease: results from international breast cancer study group trials VIII and IX. J Clin Oncol 2013; 31 (25): 30833090. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Metzger-Filho O, Sun 1, Viale G et al. Patterns of Recurrence and outcome according to breast cancer subtypes in lymph node-negative disease: results from international breast cancer study group trials VIII and IX. J Clin Oncol 2013; 31 (25): 30833090. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit82"><label>82</label><citation-alternatives><mixed-citation xml:lang="ru">Romond EH, Perez EA, Bryant J et al. Trastuzum-ab plus adjuvant chemotherapy for operable HER 2-positive breast cancer. N Engl J Med2005; 353 (16): 1673-1684. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Romond EH, Perez EA, Bryant J et al. Trastuzum-ab plus adjuvant chemotherapy for operable HER 2-positive breast cancer. N Engl J Med2005; 353 (16): 1673-1684. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit83"><label>83</label><citation-alternatives><mixed-citation xml:lang="ru">Haffty BG, Yang Q, Reiss M et al. Locoregional relapse and distant metastasis in conservatively managed triple negative early-stage breast cancer. J Clin Oncol 2006; 24 (36): 5652-5657. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Haffty BG, Yang Q, Reiss M et al. Locoregional relapse and distant metastasis in conservatively managed triple negative early-stage breast cancer. J Clin Oncol 2006; 24 (36): 5652-5657. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit84"><label>84</label><citation-alternatives><mixed-citation xml:lang="ru">Anderson WF, Jatoi I, Sherman ME. Qualitative age interactions in breast cancer studies: mind the gap. J Clin Oncol2009; 27 (32): 5308-5311. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Anderson WF, Jatoi I, Sherman ME. Qualitative age interactions in breast cancer studies: mind the gap. J Clin Oncol2009; 27 (32): 5308-5311. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit85"><label>85</label><citation-alternatives><mixed-citation xml:lang="ru">Lal S, McCart Reed AE, de Luca XM, Simpson PT. Molecular signatures in breast cancer. Methods 2017; 131: 135-146. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Lal S, McCart Reed AE, de Luca XM, Simpson PT. Molecular signatures in breast cancer. Methods 2017; 131: 135-146. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit86"><label>86</label><citation-alternatives><mixed-citation xml:lang="ru">Reis-Filho JS, Pusztai L. Gene expression profiling in breast cancer, classification, prognostication, and prediction. Lancet2011; 378 (9805): 1812-1823. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Reis-Filho JS, Pusztai L. Gene expression profiling in breast cancer, classification, prognostication, and prediction. Lancet2011; 378 (9805): 1812-1823. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit87"><label>87</label><citation-alternatives><mixed-citation xml:lang="ru">Weigelt B, Baehner FL, Reis-Filho JS. The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade. J Pathol 2010; 220 (2): 263-280. Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Weigelt B, Baehner FL, Reis-Filho JS. The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade. J Pathol 2010; 220 (2): 263-280. Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit88"><label>88</label><citation-alternatives><mixed-citation xml:lang="ru">Harris LN, Ismaila N, McShane LM et al. Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology Clinical Practice Guideline. J Clin Oncol 2016; 34 (10): 1134-1150. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Harris LN, Ismaila N, McShane LM et al. Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology Clinical Practice Guideline. J Clin Oncol 2016; 34 (10): 1134-1150. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit89"><label>89</label><citation-alternatives><mixed-citation xml:lang="ru">Gupta A, Mutebi M, Bardia A. Gene-expression-based predictors for breast cancer. Ann Surg Oncol2015; 22 (11): 3418-3432. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Gupta A, Mutebi M, Bardia A. Gene-expression-based predictors for breast cancer. Ann Surg Oncol2015; 22 (11): 3418-3432. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit90"><label>90</label><citation-alternatives><mixed-citation xml:lang="ru">Coates AS, Winer EP, GoldhirschAet al. Tailoring therapies-improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer2015. Ann Oncol 2015; 26 (8): 1533-1546. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Coates AS, Winer EP, GoldhirschAet al. Tailoring therapies-improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer2015. Ann Oncol 2015; 26 (8): 1533-1546. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit91"><label>91</label><citation-alternatives><mixed-citation xml:lang="ru">Bertoli G, Cava C, Castiglioni I. MicroRNAs: new biomarkers for diagnosis, prognosis, therapy prediction and therapeutic tools for breast cancer. Theranostics 2015; 5 (10): 1122-1143. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Bertoli G, Cava C, Castiglioni I. MicroRNAs: new biomarkers for diagnosis, prognosis, therapy prediction and therapeutic tools for breast cancer. Theranostics 2015; 5 (10): 1122-1143. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit92"><label>92</label><citation-alternatives><mixed-citation xml:lang="ru">Volinia S, Croce CM. Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer. Proc Natl Acad Sci USA 2013; 110 (18): 7413-7417. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Volinia S, Croce CM. Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer. Proc Natl Acad Sci USA 2013; 110 (18): 7413-7417. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit93"><label>93</label><citation-alternatives><mixed-citation xml:lang="ru">Blenkron C, Goldstein LD, Thorne NP et al. MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype. Genome Biol 2007; 8 (10): R214. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Blenkron C, Goldstein LD, Thorne NP et al. MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype. Genome Biol 2007; 8 (10): R214. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit94"><label>94</label><citation-alternatives><mixed-citation xml:lang="ru">Mar-AguilarF, Mendoza-Ramrez JA, Malagon-San-tiago I et al. Serum crculating microRNA profiling for identification of potential breast cancer biomarkers. Dis Markers 2013; 34 (3): 163-169. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Mar-AguilarF, Mendoza-Ramrez JA, Malagon-San-tiago I et al. Serum crculating microRNA profiling for identification of potential breast cancer biomarkers. Dis Markers 2013; 34 (3): 163-169. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit95"><label>95</label><citation-alternatives><mixed-citation xml:lang="ru">Reis-Filho JS. Next-generation sequencing. Breast Cancer Res 2009; 11 (Suppl 3): S12. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Reis-Filho JS. Next-generation sequencing. Breast Cancer Res 2009; 11 (Suppl 3): S12. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit96"><label>96</label><citation-alternatives><mixed-citation xml:lang="ru">Behjati S, Tarpey PS. What is next generation sequencing? Arch Dis Child Educ Pract Ed 2013; 98 (6): 236-238. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Behjati S, Tarpey PS. What is next generation sequencing? Arch Dis Child Educ Pract Ed 2013; 98 (6): 236-238. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit97"><label>97</label><citation-alternatives><mixed-citation xml:lang="ru">Stephens PJ, Tarpey PS, Davies H et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 2012; 486 (7403): 400-404. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Stephens PJ, Tarpey PS, Davies H et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 2012; 486 (7403): 400-404. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit98"><label>98</label><citation-alternatives><mixed-citation xml:lang="ru">Morganella S, Alexandrov LB, Glodzik D et al. The topography of mutational processes in breast cancer genomes. Nat Commun 2016; 7: 11383. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Morganella S, Alexandrov LB, Glodzik D et al. The topography of mutational processes in breast cancer genomes. Nat Commun 2016; 7: 11383. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit99"><label>99</label><citation-alternatives><mixed-citation xml:lang="ru">Yates LR, Gerstung M, Knappskog S et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat Med 2015; 21 (7): 751-759. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Yates LR, Gerstung M, Knappskog S et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat Med 2015; 21 (7): 751-759. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit100"><label>100</label><citation-alternatives><mixed-citation xml:lang="ru">Ruggles KV, Krug K, Wang X et al. Methods, tools and current perspectives in proteogenomics. Mol Cell Proteomics 2017; 16 (6): 959-981. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Ruggles KV, Krug K, Wang X et al. Methods, tools and current perspectives in proteogenomics. Mol Cell Proteomics 2017; 16 (6): 959-981. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit101"><label>101</label><citation-alternatives><mixed-citation xml:lang="ru">Gonzalez-Angulo AM, Hennessy BT, Meric-Bernstam F et al. Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer. Clin Proteomics 2011; 8 (1): 11. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Gonzalez-Angulo AM, Hennessy BT, Meric-Bernstam F et al. Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer. Clin Proteomics 2011; 8 (1): 11. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit102"><label>102</label><citation-alternatives><mixed-citation xml:lang="ru">Al-Ejeh F, Miranda M, Shi W et al. Kinome profiling reveals breast cancer heterogeneity and identifies targeted therapeutic opportunities for triple negative breast cancer. Oncotarget2014; 5 (10): 3145-3158. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Al-Ejeh F, Miranda M, Shi W et al. Kinome profiling reveals breast cancer heterogeneity and identifies targeted therapeutic opportunities for triple negative breast cancer. Oncotarget2014; 5 (10): 3145-3158. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit103"><label>103</label><citation-alternatives><mixed-citation xml:lang="ru">Chung L, Moore K, Phillips L, Boyle FM, Marsh DJ, Baxter RC. Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer. Breast Cancer Res 2014; 16 (3): R63. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Chung L, Moore K, Phillips L, Boyle FM, Marsh DJ, Baxter RC. Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer. Breast Cancer Res 2014; 16 (3): R63. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit104"><label>104</label><citation-alternatives><mixed-citation xml:lang="ru">Huang JH, Han D, Ruggles ME, Jayaraman A, Ugaz VM. Characterization of enzymatic micromachining for construction of variable cross-section microchannel topologies. Biomicrofluidics 2016; 10 (3): 033102. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Huang JH, Han D, Ruggles ME, Jayaraman A, Ugaz VM. Characterization of enzymatic micromachining for construction of variable cross-section microchannel topologies. Biomicrofluidics 2016; 10 (3): 033102. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit105"><label>105</label><citation-alternatives><mixed-citation xml:lang="ru">Mertins P, Mani DR, Ruggles KV et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 2016; 534 (7605): 55-62. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Mertins P, Mani DR, Ruggles KV et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 2016; 534 (7605): 55-62. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit106"><label>106</label><citation-alternatives><mixed-citation xml:lang="ru">Ruggles KV, Tang 1, Wang X et al. An analysis of the sensitivity of proteogenomic mapping of somatic mutations and novel splicing events in cancer. Mol Cell Proteomics 2016; 15 (3): 1060-1071. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Ruggles KV, Tang 1, Wang X et al. An analysis of the sensitivity of proteogenomic mapping of somatic mutations and novel splicing events in cancer. Mol Cell Proteomics 2016; 15 (3): 1060-1071. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit107"><label>107</label><citation-alternatives><mixed-citation xml:lang="ru">Gunther UL. Metabolomics biomarkers for breast cancer. Pathobiology 2015; 82 (3-4): 153-165. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Gunther UL. Metabolomics biomarkers for breast cancer. Pathobiology 2015; 82 (3-4): 153-165. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit108"><label>108</label><citation-alternatives><mixed-citation xml:lang="ru">Cao MD, Sitter B, Bathen TF et al. Predicting longterm survival and treatment response in breast cancer patients receiving neoadjuvant chemotherapy by MR metabolic profiling. NMR Biomed 2012; 25 (2): 369-378. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Cao MD, Sitter B, Bathen TF et al. Predicting longterm survival and treatment response in breast cancer patients receiving neoadjuvant chemotherapy by MR metabolic profiling. NMR Biomed 2012; 25 (2): 369-378. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit109"><label>109</label><citation-alternatives><mixed-citation xml:lang="ru">Mazurowski MA. Radiogenomics: what it is and why it is important. J Am Coll Radiol 2015; 12 (8): 862-866. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Mazurowski MA. Radiogenomics: what it is and why it is important. J Am Coll Radiol 2015; 12 (8): 862-866. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit110"><label>110</label><citation-alternatives><mixed-citation xml:lang="ru">Bai HX, Lee AM, Yang L et al. Imaging genomics in cancer research: limitations and promises. Br J Radiol2016; 89 (1061): 20151030. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Bai HX, Lee AM, Yang L et al. Imaging genomics in cancer research: limitations and promises. Br J Radiol2016; 89 (1061): 20151030. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit111"><label>111</label><citation-alternatives><mixed-citation xml:lang="ru">Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278 (2): 563-577. Link, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278 (2): 563-577. Link, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit112"><label>112</label><citation-alternatives><mixed-citation xml:lang="ru">Herold CJ, Lewin JS, Wibmer AG et al. Imaging in the age of precision medicine: summary of the Proceedings of the 10th Biannual Symposium of the International Society for Strategic Studies in Radiology. Radiology 2016; 279 (1): 226-238. Link, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Herold CJ, Lewin JS, Wibmer AG et al. Imaging in the age of precision medicine: summary of the Proceedings of the 10th Biannual Symposium of the International Society for Strategic Studies in Radiology. Radiology 2016; 279 (1): 226-238. Link, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit113"><label>113</label><citation-alternatives><mixed-citation xml:lang="ru">Thrall JH. Moreton lecture: imaging in the age of precision medicine. J Am Coll Radiol 2015; 12 (10): 1106-1111. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Thrall JH. Moreton lecture: imaging in the age of precision medicine. J Am Coll Radiol 2015; 12 (10): 1106-1111. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit114"><label>114</label><citation-alternatives><mixed-citation xml:lang="ru">Lambin P, Rios-Velazquez E, Leijenaar R et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer2012; 48 (4): 441-446. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Lambin P, Rios-Velazquez E, Leijenaar R et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer2012; 48 (4): 441-446. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit115"><label>115</label><citation-alternatives><mixed-citation xml:lang="ru">Kuo MD, Jamshidi N. Behind the numbers: decoding molecular phenotypes with radiogenomics-guiding principles and technical considerations. Radiology 2014; 270 (2): 320-325. Link, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Kuo MD, Jamshidi N. Behind the numbers: decoding molecular phenotypes with radiogenomics-guiding principles and technical considerations. Radiology 2014; 270 (2): 320-325. Link, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit116"><label>116</label><citation-alternatives><mixed-citation xml:lang="ru">Kumar V, Gu Y, Basu S et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012; 30 (9): 1234-1248. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Kumar V, Gu Y, Basu S et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012; 30 (9): 1234-1248. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit117"><label>117</label><citation-alternatives><mixed-citation xml:lang="ru">Sala E, Mema E, Himoto Y et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol2017; 72 (1): 3-10. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Sala E, Mema E, Himoto Y et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol2017; 72 (1): 3-10. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit118"><label>118</label><citation-alternatives><mixed-citation xml:lang="ru">European Society of Radiology (ESR). Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR). Insights Imaging 2015; 6 (2): 141-155. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">European Society of Radiology (ESR). Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR). Insights Imaging 2015; 6 (2): 141-155. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit119"><label>119</label><citation-alternatives><mixed-citation xml:lang="ru">D'Orsi CJ, Sickles EA, Mendelson EB et al. ACR BIRADS Atlas, Breast Imaging Reporting and Data System. 5th ed. Reston, Va: American College of Radiology, 2013. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">D'Orsi CJ, Sickles EA, Mendelson EB et al. ACR BIRADS Atlas, Breast Imaging Reporting and Data System. 5th ed. Reston, Va: American College of Radiology, 2013. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit120"><label>120</label><citation-alternatives><mixed-citation xml:lang="ru">Szczypihski PM, Strzelecki M, Materka A, Klepaczko A. MaZda: a software package for image texture analysis. Comput Methods Programs Biomed2009; 94 (1): 66-76. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Szczypihski PM, Strzelecki M, Materka A, Klepaczko A. MaZda: a software package for image texture analysis. Comput Methods Programs Biomed2009; 94 (1): 66-76. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit121"><label>121</label><citation-alternatives><mixed-citation xml:lang="ru">Materka A. Texture analysis methodologies for magnetic resonance imaging. Dialogues Clin Neu-rosci 2004; 6 (2): 243-250. Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Materka A. Texture analysis methodologies for magnetic resonance imaging. Dialogues Clin Neu-rosci 2004; 6 (2): 243-250. Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit122"><label>122</label><citation-alternatives><mixed-citation xml:lang="ru">Patil SS, Junnarkar AA, Gore DV. Study of texture representation techniques. Int J Emerg Trends Technol Comput Sci 2014; 3 (3). Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Patil SS, Junnarkar AA, Gore DV. Study of texture representation techniques. Int J Emerg Trends Technol Comput Sci 2014; 3 (3). Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit123"><label>123</label><citation-alternatives><mixed-citation xml:lang="ru">Haralick RM, Shanmugam M, Dinstein IH. Textural features for image classification. IEEE Trans Syst Man Cybern1973; SMC-3 (6): 610-621. Crossref, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Haralick RM, Shanmugam M, Dinstein IH. Textural features for image classification. IEEE Trans Syst Man Cybern1973; SMC-3 (6): 610-621. Crossref, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit124"><label>124</label><citation-alternatives><mixed-citation xml:lang="ru">Wengert GJ, Helbich TH, Vogl WD et al. Introduction of an automated, user-independent, quantitative, volumetric magnetic resonance imaging breast density measurement system using the Dixon sequence: comparison with mammographic breast density assessment. Invest Radiol 2015; 50 (2): 73-80. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Wengert GJ, Helbich TH, Vogl WD et al. Introduction of an automated, user-independent, quantitative, volumetric magnetic resonance imaging breast density measurement system using the Dixon sequence: comparison with mammographic breast density assessment. Invest Radiol 2015; 50 (2): 73-80. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit125"><label>125</label><citation-alternatives><mixed-citation xml:lang="ru">Grimm LJ. Breast MRI radiogenomics: current status and research implications. J Magn Reson Imaging 2016; 43 (6): 1269-1278. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Grimm LJ. Breast MRI radiogenomics: current status and research implications. J Magn Reson Imaging 2016; 43 (6): 1269-1278. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit126"><label>126</label><citation-alternatives><mixed-citation xml:lang="ru">Peterson CB, Bogomolov M, Benjamini Y, Sabatti C. Many phenotypes without many false discoveries: error controlling strategies for multitrait association studies. Genet Epidemiol 2016; 40 (1): 45-56. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Peterson CB, Bogomolov M, Benjamini Y, Sabatti C. Many phenotypes without many false discoveries: error controlling strategies for multitrait association studies. Genet Epidemiol 2016; 40 (1): 45-56. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit127"><label>127</label><citation-alternatives><mixed-citation xml:lang="ru">Reiner A, Yekutieli D, Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 2003; 19 (3): 368-375. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Reiner A, Yekutieli D, Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 2003; 19 (3): 368-375. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit128"><label>128</label><citation-alternatives><mixed-citation xml:lang="ru">Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R StatSoc Series B StatMethodol 1995; 57 (1): 289-300. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R StatSoc Series B StatMethodol 1995; 57 (1): 289-300. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit129"><label>129</label><citation-alternatives><mixed-citation xml:lang="ru">Sadot E, Simpson AL, Do RK et al. Cholangiocar-cinoma: correlation between molecular profiling and imaging phenotypes. PLoS One 2015; 10 (7): e0132953. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Sadot E, Simpson AL, Do RK et al. Cholangiocar-cinoma: correlation between molecular profiling and imaging phenotypes. PLoS One 2015; 10 (7): e0132953. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit130"><label>130</label><citation-alternatives><mixed-citation xml:lang="ru">Yamamoto S, Han W, Kim Y et al. Breast cancer: radiogenomic biomarker reveals associations among dynamic contrast-enhanced MR imaging, long noncoding RNA, and metastasis. Radiology 2015; 275 (2): 384-392. Link, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Yamamoto S, Han W, Kim Y et al. Breast cancer: radiogenomic biomarker reveals associations among dynamic contrast-enhanced MR imaging, long noncoding RNA, and metastasis. Radiology 2015; 275 (2): 384-392. Link, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit131"><label>131</label><citation-alternatives><mixed-citation xml:lang="ru">Ashraf AB, Daye D, Gavenonis S et al. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology 2014; 272 (2): 374-384. Link, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Ashraf AB, Daye D, Gavenonis S et al. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology 2014; 272 (2): 374-384. Link, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit132"><label>132</label><citation-alternatives><mixed-citation xml:lang="ru">Yamamoto S, Maki DD, Korn RL, Kuo MD. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. AJR Am J Roentgenol2012; 199 (3): 654-663. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Yamamoto S, Maki DD, Korn RL, Kuo MD. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. AJR Am J Roentgenol2012; 199 (3): 654-663. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit133"><label>133</label><citation-alternatives><mixed-citation xml:lang="ru">Grimm LJ. Breast MRI radiogenomics: current status and research implications. J Magn Reson Imaging 2016; 43 (6): 1269-1278. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Grimm LJ. Breast MRI radiogenomics: current status and research implications. J Magn Reson Imaging 2016; 43 (6): 1269-1278. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit134"><label>134</label><citation-alternatives><mixed-citation xml:lang="ru">Pinker K, Shitano F, Sala E et al. Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging 2017 Nov2. [Epub ahead of print]. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Pinker K, Shitano F, Sala E et al. Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging 2017 Nov2. [Epub ahead of print]. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit135"><label>135</label><citation-alternatives><mixed-citation xml:lang="ru">Hu Z, Fan C, Oh DS et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 2006; 7 (1): 96. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Hu Z, Fan C, Oh DS et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 2006; 7 (1): 96. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit136"><label>136</label><citation-alternatives><mixed-citation xml:lang="ru">Teschendorff AE, Miremadi A, Pinder SE, Ellis IO, Caldas C. An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol 2007; 8 (8): R 157. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Teschendorff AE, Miremadi A, Pinder SE, Ellis IO, Caldas C. An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol 2007; 8 (8): R 157. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit137"><label>137</label><citation-alternatives><mixed-citation xml:lang="ru">Zhu Y, Li H, Guo W et al. Deciphering genomic underpinnings of quantitative MRI-based radiom-ic phenotypes of invasive breast carcinoma. Sci Rep 2015; 5 (1): 17787. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Zhu Y, Li H, Guo W et al. Deciphering genomic underpinnings of quantitative MRI-based radiom-ic phenotypes of invasive breast carcinoma. Sci Rep 2015; 5 (1): 17787. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit138"><label>138</label><citation-alternatives><mixed-citation xml:lang="ru">Li H, Zhu Y, Burnside ES et al. MR Imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of Mam-maPrint, Oncotype DX, and PAM50 gene assays. Radiology 2016; 281 (2): 382-391. Link, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Li H, Zhu Y, Burnside ES et al. MR Imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of Mam-maPrint, Oncotype DX, and PAM50 gene assays. Radiology 2016; 281 (2): 382-391. Link, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit139"><label>139</label><citation-alternatives><mixed-citation xml:lang="ru">Li H, Zhu Y, Burnside ES et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer2016; 2. pii: 16012. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Li H, Zhu Y, Burnside ES et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer2016; 2. pii: 16012. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit140"><label>140</label><citation-alternatives><mixed-citation xml:lang="ru">Sutton EJ, Dashevsky BZ, Oh JH et al. Breast cancer molecular subtype classifier that incorporates MRI features. J Magn Reson Imaging 2016; 44 (1): 122-129. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Sutton EJ, Dashevsky BZ, Oh JH et al. Breast cancer molecular subtype classifier that incorporates MRI features. J Magn Reson Imaging 2016; 44 (1): 122-129. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit141"><label>141</label><citation-alternatives><mixed-citation xml:lang="ru">Sutton EJ, Oh JH, Dashevsky BZ et al. Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay. J Magn Reson Imaging 2015; 42 (5): 1398-1406. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Sutton EJ, Oh JH, Dashevsky BZ et al. Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay. J Magn Reson Imaging 2015; 42 (5): 1398-1406. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit142"><label>142</label><citation-alternatives><mixed-citation xml:lang="ru">Mahrooghy M, Ashraf AB, Daye D et al. Pharmacokinetic tumor heterogeneity as a prognostic biomarker for classifying breast cancer recurrence risk. IEEE Trans Biomed Eng 2015; 62 (6): 1585-1594. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Mahrooghy M, Ashraf AB, Daye D et al. Pharmacokinetic tumor heterogeneity as a prognostic biomarker for classifying breast cancer recurrence risk. IEEE Trans Biomed Eng 2015; 62 (6): 1585-1594. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit143"><label>143</label><citation-alternatives><mixed-citation xml:lang="ru">Mahrooghy M, Ashraf AB, Daye D et al. Heterogeneity wavelet kinetics from DCE-MRI for classifying gene expression based breast cancer recurrence risk. Med Image Comput Assist Interv 2013; 16 (Pt 2): 295-302. Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Mahrooghy M, Ashraf AB, Daye D et al. Heterogeneity wavelet kinetics from DCE-MRI for classifying gene expression based breast cancer recurrence risk. Med Image Comput Assist Interv 2013; 16 (Pt 2): 295-302. Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit144"><label>144</label><citation-alternatives><mixed-citation xml:lang="ru">Yamaguchi K, Abe H, Newstead GM et al. Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer, comparison based on the molecular subtypes of invasive breast cancer. Breast Cancer2015; 22 (5): 496-502. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Yamaguchi K, Abe H, Newstead GM et al. Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer, comparison based on the molecular subtypes of invasive breast cancer. Breast Cancer2015; 22 (5): 496-502. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit145"><label>145</label><citation-alternatives><mixed-citation xml:lang="ru">Blaschke E, Abe H. MRI phenotype of breast cancer kinetic assessment for molecular subtypes. J Magn Reson Imaging 2015; 42 (4): 920-924. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Blaschke E, Abe H. MRI phenotype of breast cancer kinetic assessment for molecular subtypes. J Magn Reson Imaging 2015; 42 (4): 920-924. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit146"><label>146</label><citation-alternatives><mixed-citation xml:lang="ru">Li H, Zhu Y, Burnside ES et al. MR Imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of Mam-maPrint, Oncotype DX, and PAM50 gene assays. Radiology 2016; 281 (2): 382-391. Link, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Li H, Zhu Y, Burnside ES et al. MR Imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of Mam-maPrint, Oncotype DX, and PAM50 gene assays. Radiology 2016; 281 (2): 382-391. Link, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit147"><label>147</label><citation-alternatives><mixed-citation xml:lang="ru">Waugh SA, Purdie CA, Jordan LB et al. Magnetic resonance imaging texture analysis classification of primary breast cancer. Eur Radiol 2016; 26 (2): 322-330. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Waugh SA, Purdie CA, Jordan LB et al. Magnetic resonance imaging texture analysis classification of primary breast cancer. Eur Radiol 2016; 26 (2): 322-330. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit148"><label>148</label><citation-alternatives><mixed-citation xml:lang="ru">Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer. Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging 2015; 42 (4): 902-907. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer. Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging 2015; 42 (4): 902-907. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit149"><label>149</label><citation-alternatives><mixed-citation xml:lang="ru">Grimm LJ, Zhang J, Baker JA, Soo MS, Johnson KS, Mazurowski MA. Relationships between MRI Breast Imaging-Reporting and Data System (BI-RADS) lexicon descriptors and breast cancer molecular subtypes: internal enhancement is associated with luminal B subtype. Breast J 2017; 23 (5): 579-582. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Grimm LJ, Zhang J, Baker JA, Soo MS, Johnson KS, Mazurowski MA. Relationships between MRI Breast Imaging-Reporting and Data System (BI-RADS) lexicon descriptors and breast cancer molecular subtypes: internal enhancement is associated with luminal B subtype. Breast J 2017; 23 (5): 579-582. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit150"><label>150</label><citation-alternatives><mixed-citation xml:lang="ru">Woodard GA, Ray KM, Joe BN, Price ER. Qualitative radiogenomics: association between Oncotype DX test recurrence score and BI-RADS mammographic and breast MR imaging features. Radiology 2018; 286 (1): 60-70. Link, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Woodard GA, Ray KM, Joe BN, Price ER. Qualitative radiogenomics: association between Oncotype DX test recurrence score and BI-RADS mammographic and breast MR imaging features. Radiology 2018; 286 (1): 60-70. Link, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit151"><label>151</label><citation-alternatives><mixed-citation xml:lang="ru">Zaric O, Pinker K, Zbyn S et al. Quantitative sodium MR imaging at 7 T: initial results and comparison with diffusion-weighted imaging in patients with breast tumors. Radiology 2016; 280 (1): 39-48. Link, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Zaric O, Pinker K, Zbyn S et al. Quantitative sodium MR imaging at 7 T: initial results and comparison with diffusion-weighted imaging in patients with breast tumors. Radiology 2016; 280 (1): 39-48. Link, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit152"><label>152</label><citation-alternatives><mixed-citation xml:lang="ru">Schmitt B, Trattnig S, Schlemmer HP. CEST-imaging: a new contrast in MR-mammography by means of chemical exchange saturation transfer. Eur J Radiol 2012; 81 (Suppl 1): S 144-S 146. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Schmitt B, Trattnig S, Schlemmer HP. CEST-imaging: a new contrast in MR-mammography by means of chemical exchange saturation transfer. Eur J Radiol 2012; 81 (Suppl 1): S 144-S 146. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit153"><label>153</label><citation-alternatives><mixed-citation xml:lang="ru">Schmitt B, Zamecnik P, Zaiss M et al. A new contrast in MR mammography by means of chemical exchange saturation transfer (CEST) imaging at 3 Tesla: preliminary results. Rofo 2011; 183 (11): 1030-1036. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Schmitt B, Zamecnik P, Zaiss M et al. A new contrast in MR mammography by means of chemical exchange saturation transfer (CEST) imaging at 3 Tesla: preliminary results. Rofo 2011; 183 (11): 1030-1036. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit154"><label>154</label><citation-alternatives><mixed-citation xml:lang="ru">Rakow-Penner R, Daniel B, Glover GH. Detecting blood oxygen level-dependent (BOLD) contrast in the breast. J Magn Reson Imaging 2010; 32 (1): 120-129. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Rakow-Penner R, Daniel B, Glover GH. Detecting blood oxygen level-dependent (BOLD) contrast in the breast. J Magn Reson Imaging 2010; 32 (1): 120-129. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit155"><label>155</label><citation-alternatives><mixed-citation xml:lang="ru">Pinker K, Bogner W, Baltzer P et al. Improved differentiation of benign and malignant breast tumors with multiparametric 18 fluorodeoxyglucose positron emission tomography magnetic resonance imaging: a feasibility study. Clin Cancer Res 2014; 20 (13): 3540-3549. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Pinker K, Bogner W, Baltzer P et al. Improved differentiation of benign and malignant breast tumors with multiparametric 18 fluorodeoxyglucose positron emission tomography magnetic resonance imaging: a feasibility study. Clin Cancer Res 2014; 20 (13): 3540-3549. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit156"><label>156</label><citation-alternatives><mixed-citation xml:lang="ru">Djekidel M. Radiogenomics and Radioproteomics. OMICS J Radiol 2013; 2: 115. Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Djekidel M. Radiogenomics and Radioproteomics. OMICS J Radiol 2013; 2: 115. Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit157"><label>157</label><citation-alternatives><mixed-citation xml:lang="ru">Noor AM, Holmberg L, Gillett C, Grigoriadis A. Big data: the challenge for small research groups in the era of cancer genomics. Br J Cancer 2015; 113 (10): 1405-1412. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Noor AM, Holmberg L, Gillett C, Grigoriadis A. Big data: the challenge for small research groups in the era of cancer genomics. Br J Cancer 2015; 113 (10): 1405-1412. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit158"><label>158</label><citation-alternatives><mixed-citation xml:lang="ru">Alyass A, Turcotte M, Meyre D. From big data analysis to personalized medicine for all: challenges and opportunities.BMC Med Genomics 2015; 8 (1): 33. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Alyass A, Turcotte M, Meyre D. From big data analysis to personalized medicine for all: challenges and opportunities.BMC Med Genomics 2015; 8 (1): 33. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit159"><label>159</label><citation-alternatives><mixed-citation xml:lang="ru">Clark K, Vendt B, Smith K et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013; 26 (6): 1045-1057. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">Clark K, Vendt B, Smith K et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013; 26 (6): 1045-1057. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit160"><label>160</label><citation-alternatives><mixed-citation xml:lang="ru">European Society of Radiology (ESR). White paper on imaging biomarkers. Insights Imaging 2010; 1 (2): 42-45. Crossref, Medline, Google Scholar.</mixed-citation><mixed-citation xml:lang="en">European Society of Radiology (ESR). White paper on imaging biomarkers. Insights Imaging 2010; 1 (2): 42-45. Crossref, Medline, Google Scholar.</mixed-citation></citation-alternatives></ref><ref id="cit161"><label>161</label><citation-alternatives><mixed-citation xml:lang="ru">Prediction of Low versus High Recurrence Scores in Estrogen Receptor-Positive, Lymph Node-Negative Invasive Breast Cancer on the Basis of Radiologic-Pathologic Features: Comparison with Oncotype DX Test Recurrence Scores, Radiology 2016.Volume: 280 Issue: 2 pp. 370-378.</mixed-citation><mixed-citation xml:lang="en">Prediction of Low versus High Recurrence Scores in Estrogen Receptor-Positive, Lymph Node-Negative Invasive Breast Cancer on the Basis of Radiologic-Pathologic Features: Comparison with Oncotype DX Test Recurrence Scores, Radiology 2016.Volume: 280 Issue: 2 pp. 370-378.</mixed-citation></citation-alternatives></ref><ref id="cit162"><label>162</label><citation-alternatives><mixed-citation xml:lang="ru">Molecular Classification of Infitrating Breast Cancer: Toward Personalized Therapy, Radio Graphics 2014, Volume: 34 Issue: 5 pp. 1178-1195.</mixed-citation><mixed-citation xml:lang="en">Molecular Classification of Infitrating Breast Cancer: Toward Personalized Therapy, Radio Graphics 2014, Volume: 34 Issue: 5 pp. 1178-1195.</mixed-citation></citation-alternatives></ref><ref id="cit163"><label>163</label><citation-alternatives><mixed-citation xml:lang="ru">Lipid and Metabolite Deregulation in the Breast Tissue of Women Carrying BRCA1 and BRCA2 Genetic Mutations, Radiology 2015.Volume: 275 Issue: 3 pp. 675-682, Vol. 287, No. 3.</mixed-citation><mixed-citation xml:lang="en">Lipid and Metabolite Deregulation in the Breast Tissue of Women Carrying BRCA1 and BRCA2 Genetic Mutations, Radiology 2015.Volume: 275 Issue: 3 pp. 675-682, Vol. 287, No. 3.</mixed-citation></citation-alternatives></ref><ref id="cit164"><label>164</label><citation-alternatives><mixed-citation xml:lang="ru">Katja Pinker1, Joanne Chin, Amy N. Melsaether, Elizabeth A. Morris, Linda Moy. Published Online: May 212018 https://doi.org/10.1148/radiol.2018172171.</mixed-citation><mixed-citation xml:lang="en">Katja Pinker1, Joanne Chin, Amy N. Melsaether, Elizabeth A. Morris, Linda Moy. Published Online: May 212018 https://doi.org/10.1148/radiol.2018172171.</mixed-citation></citation-alternatives></ref><ref id="cit165"><label>165</label><citation-alternatives><mixed-citation xml:lang="ru">Радиологические технологии и биогенетические маркеры в дифференциальной диагностике заболеваний молочной железы, сопровождающихся скоплением микрокальцинатов. Якобс О.Э., Кудинова Е. А., Рожкова Н. И., Боженко В. К. Вестник Российского научного центра рентгенорадиологии Минздрава России. 2017. Т. 17. № 1. С. 6. http://vestnik.rncrr.ru/vestnik/v17/docs/yakobs.pdf.</mixed-citation><mixed-citation xml:lang="en">Радиологические технологии и биогенетические маркеры в дифференциальной диагностике заболеваний молочной железы, сопровождающихся скоплением микрокальцинатов. Якобс О.Э., Кудинова Е. А., Рожкова Н. И., Боженко В. К. Вестник Российского научного центра рентгенорадиологии Минздрава России. 2017. Т. 17. № 1. С. 6. http://vestnik.rncrr.ru/vestnik/v17/docs/yakobs.pdf.</mixed-citation></citation-alternatives></ref><ref id="cit166"><label>166</label><citation-alternatives><mixed-citation xml:lang="ru">Маммология. Национальное руководство.2-е издание. Ред. Каприна А. Д., Рожковой Н. И. // М.. ГЭОТАР Медиа, 2016, 496.</mixed-citation><mixed-citation xml:lang="en">Маммология. Национальное руководство.2-е издание. Ред. Каприна А. Д., Рожковой Н. И. // М.. ГЭОТАР Медиа, 2016, 496.</mixed-citation></citation-alternatives></ref><ref id="cit167"><label>167</label><citation-alternatives><mixed-citation xml:lang="ru">Каприн А.Д., Рожкова Н.И. Рак молочной железы / М.: ГЭОТАР-Медиа, 2018.-456 с.</mixed-citation><mixed-citation xml:lang="en">Каприн А.Д., Рожкова Н.И. Рак молочной железы / М.: ГЭОТАР-Медиа, 2018.-456 с.</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
