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Early diagnosis of breast cancer using multiparametric ultrasound and hybrid technologies

https://doi.org/10.33667/2078-5631-2022-13-22-25

Abstract

Breast cancer currently occupies a leading place in the structure of morbidity and mortality of the female world population. Despite the improvement of methods of diagnosis and treatment of this pathology, more than 2,1 million new cases are detected annually. All these facts make us look for more informative methods of diagnosing breast cancer. The purpose of the study is to evaluate the effectiveness of a new improved algorithm of the diagnosis of one of the rarest forms of intracystic breast cancer. 153 patients with suspected cancer in breast cyst were examined in the study. Compression elastography, shear-wave elastography and fine needle aspiration were included in the standard traditional examination algorithm according to our improved method with using an ozone-oxygen mixture. We have obtained data on lower sensitivity and specificity of the traditional diagnostic complex of examination of patients with suspected intracystic breast cancer in comparison with the improved algorithm.

About the Authors

M. I. Arabachyan
Smolensk State Medical University; Smolensk Regional Oncology clinical Dispensary
Russian Federation

Arabachyan Mariam I., DM Sci (habil.), ultrasound diagnost, chief researcher at Scientific Laboratory ‘Diagnostic studies and minimally invasive technologies’, Smolensk State Medical University; oncologist, Smolensk Regional Oncology clinical Dispensary

Smolensk



A. V. Borsukov
Smolensk State Medical University
Russian Federation

Borsukov Alexey V., DM Sci (habil.), professor, director of Scientific Laboratory‘Diagnostic studies and minimally invasive technologies

Smolensk



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For citations:


Arabachyan M.I., Borsukov A.V. Early diagnosis of breast cancer using multiparametric ultrasound and hybrid technologies. Medical alphabet. 2022;(13):22-25. (In Russ.) https://doi.org/10.33667/2078-5631-2022-13-22-25

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ISSN 2078-5631 (Print)
ISSN 2949-2807 (Online)