Features of assessing the economic efficiency of introducing artificial intelligence into the oncology screening system (based on materials from the Sverdlovsk region)

The article conducts research on increasing the economic efficiency of oncomammoscreening and monitoring the results of breast cancer diagnostics (BC) using artificial intelligence (AI) technologies in Russia. The relevance of breast cancer screening is noted, scientific approaches to screening problems that exist in the context of healthcare financing are analyzed, and the processes of using AI in Russia and the global medical community are flaring up.

Objective: to increase the economic efficiency of screening and diagnostic X-ray mammography (X-ray mammography) programs by introducing AI as a physician’s assistant in analyzing the results of medical examinations with budgetary financing of state healthcare institutions in the Sverdlovsk region.

Research objectives. 1. To identify key issues that reduce the effectiveness of oncomammoscreening. 2. To analyze the effectiveness of automated detection systems for pathological formations in the mammary gland using AI. 3. To justify ways to increase the economic effect by introducing an intelligent physician assistant in screening and diagnostic X-ray mammography. 4. To determine the areas of growth in the profitability of mammography as a medical service within the framework of budget financing. 5. To propose options for solving the problem of personnel shortage of radiologists in state healthcare institutions of the Sverdlovsk region.

1. Kaprin A. D. The state of oncological care for the population of Russia in 2022 / A. D. Kaprin, V. V. Starinsky, A. O. Shakhzadova. Moscow: P. A. Herzen Moscow Oncology Research Institute, 2023. (In Russ.).

2. Rozhkova NI, Burdina II, Zapirova SB, Kaprin AD, Labazanova PG, Mazo ML, Mikushin SYu., Prokopenko SP, Yakobs OE. Screening of the breast carcinoma in the program of active aging. Akademicheskij zhurnal Zapadnoj Sibiri. 2019; 15 (2): 3–5. (In Russ.).

3. Rasskazova E. A., Rozhkova N. I. Skrining dlia rannei diagnostiki raka molochnoi zhelezy. Issledovaniia i praktika v meditsine. 2014; 1 (1): 45–51. (In Russ.).

4. Belyaev A. M., Stilidi I. S., Kaprin A. D., Lichinitser M. R., Meshcheryakov A. A., Semiglazaov V. F., Imyanitov E. N., Semiglazova T. Yu., Poltoratsky A. N., Konstantinov L. V., Petrusenko I. A., Nikitin O. I., Zakharov K. A., Trifonov M. I., Plakhov D. N. Blockchain in healthcare: opportunities for use in clinical research. Medical business. 2018. P. 100–105. (In Russ.).

5. Massat N. J., Dibden A., Parmar D. et al. Impact of screening on breast cancer mortality: the UK program 20 years on. Cancer Epidemiol Biomarkers Prev. 2016; 25: 455–462.

6. Nehmat Houssami, Christoph I Lee, Diana S M Buist, Dacheng Tao. Artificial intelligence for breast cancer screening: Opportunity or hype? PMID: 28938172. DOI: 10.1016/j.breast.2017.09.003

7. Autier P, Boniol M. The incidence of advanced breast cancer in the West Midlands, United Kingdom. Eur. J. Cancer Prev. Feb 2012; 21 (3): 217–221. DOI: 10.1097/CEJ.0b013e328350b107

8. Blumen Helen, Fitch Kathryn, Polkus Vincent. Comparison of Treatment Costs for Breast Cancer, by Tumor Stage and Type of Service Affiliations expand. PMID: 27066193, PMCID: PMC 4822976

9. Semiglazov V. F., Kanaev S. V., Pozharisky K. P. et al. Organ-preserving treatment of early stages of invasive breast cancer (pTl-2N 0M0): Method. rec. St. Petersburg, 2001. 13 p. (In Russ.).

10. Lane DS, Smith RA. Cancer Screening: Patient and Population Strategies, Medical Clinics. July, 14, 2023. DOI: https://doi.org/10.1016/j.mcna.2023.06.002

11. Houssami N, Kirkpatrick-Jones G, Noguchi N, Lee CI. Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI' s potential in breast screening practice. Expert Rev Med Devices. 2019; 16: 351–362.

12. Nelson D. J. A review of the importance of immune responses in luminal B breast cancer / D. J. Nelson, B. Clark, K. Munyard, V. Williams, D. Groth, J. Gill, H. Preston, A. Chan. Oncoimmunology. 2017 Jan 1; 6 (3); e1282590.

13. Gerald R Kolb. New tools for cost-effective delivery of breast imaging. Radioliol Manage. 2002 Jul-Aug; 24 (4): 22–6, 28, 30; quiz 32–4. PMID: 12229054.

14. Ioannis Sechopoulos, Ritse M. Mann. Stand-alone artificial intelligence – The future of breast cancer screening? The Breast, 9 (2020) 254e260.

15. Kooi T., Litjens G., van Ginneken B. et al. Large Scale Deep Learning for Computer Aided Detection of Mammographic Lesions. Medical Image Analysis. 2017; 35: 303–312. https://doi.org/10.1016/j.media.2016.07.007

16. Wu N, Phang J, Park J, Shen Y, Huang Z, Zorin M, Jastrzebski S, Fevry T, Katsnelson J, Kim E, Wolfson S, Parikh U, Gaddam S, Lin LLY, Ho K, Weinstein JD, Reig B, Gao Y, Toth H, Pysarenko K, Lewin A, Lee J, Airola K, Mema E, Chung S, Hwang E, Samreen N, Kim SG, Heacock L, Moy L, Cho K, Geras KJ. Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening. IEEE Transactions on Medical Imaging. 2020; 39 (4): 1184–1194. https://doi.org/10.1109/TMI.2019.2945514

17. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GS, Darzi A, Etemadi M, Garcia-Vicente F, Gilbert FJ, Halling-Brown M, Hassabis D, Jansen S, Karthikesalingam A, Kelly CJ, King D, Ledsam JR, Melnick D, Mostofi H, Peng L, Reicher JJ, Romera-Paredes B, Sidebottom R, Suleyman M, Tse D, Young KC, De Fauw J, Shetty S. International evaluation of an AI system for breast cancer screening. Nature. 2020; 577 (7788): 89–94. https://doi.org/10.1038/s41586-019-1799-6

18. Siu A. L. MSPH on behalf of the U. S. Preventive Services Task Force. Screening for Breast Cancer: U. S. Preventive Services Task Force Recommendation Statement / A. L. Siu. Ann Intern Med. 2016; 164: 279–296. DOI: 10.7326/M15-2886

19. Rodriguez-Ruiz A, Lång K, Gubern-Merida A. et al. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J. Natl. Cancer Inst. 2019; 111 (9): 916e22.

20. Morozov SP, Govorukhina VG, Didenko VV, Puchkova OS, Pavlov NA, Ovsyannikov AG, Andrejchenko AE, Ledihova NV, Vladzimirskij AV. Prospect of application of artificial intelligence systems for breast cancer screening. Voprosy onkologii. 2020; 66 (6): 603–608. (In Russ.). https://doi.org/10.37469/0507-3758-2020-66-6-603-608

21. Kretz T, Mueller KR, Schaeffter T, Elster C. Mammography image quality assurance using deep learning. IEEE Trans Biomed Eng. 2020; 67 (12): 3317–3326.

22. Lei J, Ou Y, Zhao Y, Tuo X, Zhang B, Shen M. Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis. Clinical and Experimental Medicine. 2022; October 15. Online ahead of print. https://doi. org/10.1007/S 10238-022-00895-0

23. Geras K. J., Mann R. M., Moy L. Articial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives. Radiology. 2019; 293: 246–259.

24. Moustapha Touré et al. Dimensions Used in Instruments for QALY Calculation: A Systematic Review, J. Environ Res Public Health. 2021 Apr 21; 18 (9): 4428. PMID: 33919471. DOI: 10.3390/ijerph18094428

25. Trister AD, Buist DSM, Lee CI. Will machine learning tip the balance in breast cancer screening? JAMA Oncol. 2017; 3: 1463–1464.

26. Statistical bulletin “The permanent population of the Russian Federation by municipalities as of January 1, 2024”. Federal State Statistics Service (April 27, 2024).

27. Shweta Mital, Hai V Nguyen. Cost-effectiveness of using artificial intelligence versus polygenic risk score to guide breast cancer screening. BMC Cancer. 2022; 22: Article number: 501. PMCID: PMC 9074290 DOI: 10.1186/s12885-022-09613-1

28. Pepijn Vemer 1,3 Unremarked or Unperformed? Systematic Review on Reporting of Validation Efforts of Health Economic Decision Models in Seasonal Influenza and Early Breast Cancer. Systematic Review. Open access. Published: 29 Apr 2016; 34: 833–845. https://link.springer.com/article/10.1007/s40273–016–0410–3#auth-Pieter_T_-Boer-Aff1

29. Fuchsjäger М. Is the future of breast imaging with AI? European Radiology. 2019. https://doi.org/10.1007/s00330-019-06286-6