Medical Radiology and Radiation Safety. 2026. Vol. 71. № 4
DOI:10.33266/1024-6177-2026-71-4-103-111
V.I. Pavlova1, 2, Y.S. Pyshkina1, 3, K.I. Kovalenko1, T.A. Semenova1, R.I. Kargapoltseva1, V.A. Dyukov1, E.N. Alekhin1, 2, N.Yu. Ilyasova4
DIAGNOSING OSTEOPOROSIS IN CANCER PATIENTS: ARTIFICIAL INTELLIGENCE AND TRADITIONAL APPROACHES
1 Tyumen State Medical University, Tyumen, Russia
2 Medical City, Tyumen, Russia
3 Samara State Medical University, Samara, Russia
4 Samara National Research University, Samara, Russia
Contact person: Yu.S. Pyshkina, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
ABSTRACT
Purpose: To conduct a comprehensive assessment of the potential and prospects of artificial intelligence (AI) technologies for diagnosing osteoporosis in oncology patients, with a focus on overcoming the limitations of standard methods and optimizing the screening for osteoporotic changes through the analysis of routine computed tomography (CT) data, based on a systematic review of contemporary literature.
Material and methods: This article provides a review of the literature concerning the role of artificial intelligence (AI) and traditional methods in diagnosing osteoporosis in cancer patients. It presents data on the application of AI-based technology across various medical fields, compares AI with classical diagnostic methods, and cites examples of studies demonstrating the high efficacy of AI in osteoporosis detection. The advancement of this field is expected to refine AI algorithms for osteoporosis diagnosis. Training these algorithms on large datasets will enable the automatic identification of such conditions at early stages and provide clinicians with more accurate information for timely decision-making.
Conclusion: The conducted analysis confirms that inadequate diagnosis of osteoporosis remains a key cause of pathological fractures in oncology patients. The integration of AI offers novel solutions to this problem by automating data analysis and improving the accessibility of screening. A promising direction is opportunistic screening using AI algorithms to assess bone mineral density from routine CT scans, which is particularly relevant for patients undergoing hormonal therapy. Key priorities include the development of specialized models for patients with breast cancer, prostate cancer, and myeloma, as well as the integration of AI with methods such as REMS. Despite existing limitations, such as the need for large, annotated datasets, the potential of AI to reduce labor costs and enhance diagnostic accuracy is evident. Further collaborative research and pilot implementations are warranted to overcome current barriers and advance osteoporosis diagnostics to a new level.
Keywords: oncology, osteoporosis, diagnostics, artificial intelligence, cancer patients, densitometry
For citation: Pavlova VI, Pyshkina YS, Kovalenko KI, Semenova TA, Kargapoltseva RI, Dyukov VA, Alekhin EN, Ilyasova NYu. Diagnosing Osteoporosis in Cancer Patients: Artificial Intelligence and Traditional Approaches. Medical Radiology and Radiation Safety. 2026;71(4):103–111. DOI:10.33266/1024-6177-2026-71-4-103-111
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Conflict of interest.The authors declare no conflict of interest.
Financing. The study had no sponsorship.
Contribution. Pavlova V.I.: analysis of literary sources, editing of the article; Pyshkina Y.S.: analysis of literary sources, writing of the text and editing of the article; Kovalenko K.I.: literature review, collection and analysis of literary sources, writing and editing the article; Semenova T.A.: literature review, collection and analysis of literary sources, writing the text; Kargapoltseva R.I.: analysis of literary sources, writing the text; Dyukov V.A.: literature review, collection and analysis of literary sources; Alekhin E.N.: analysis of literary sources, writing of the text and editing of the article; Ilyasova N.Yu.: analysis of literary sources, writing of the text and editing of the article.
Article received: 20.03.2026. Accepted for publication: 25.04.2026.