JOURNAL DESCRIPTION
The Medical Radiology and Radiation Safety journal ISSN 1024-6177 was founded in January 1956 (before December 30, 1993 it was entitled Medical Radiology, ISSN 0025-8334). In 2018, the journal received Online ISSN: 2618-9615 and was registered as an electronic online publication in Roskomnadzor on March 29, 2018. It publishes original research articles which cover questions of radiobiology, radiation medicine, radiation safety, radiation therapy, nuclear medicine and scientific reviews. In general the journal has more than 30 headings and it is of interest for specialists working in thefields of medicine¸ radiation biology, epidemiology, medical physics and technology. Since July 01, 2008 the journal has been published by State Research Center - Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency. The founder from 1956 to the present time is the Ministry of Health of the Russian Federation, and from 2008 to the present time is the Federal Medical Biological Agency.
Members of the editorial board are scientists specializing in the field of radiation biology and medicine, radiation protection, radiation epidemiology, radiation oncology, radiation diagnostics and therapy, nuclear medicine and medical physics. The editorial board consists of academicians (members of the Russian Academy of Science (RAS)), the full member of Academy of Medical Sciences of the Republic of Armenia, corresponding members of the RAS, Doctors of Medicine, professor, candidates and doctors of biological, physical mathematics and engineering sciences. The editorial board is constantly replenished by experts who work in the CIS and foreign countries.
Six issues of the journal are published per year, the volume is 13.5 conventional printed sheets, 88 printer’s sheets, 1.000 copies. The journal has an identical full-text electronic version, which, simultaneously with the printed version and color drawings, is posted on the sites of the Scientific Electronic Library (SEL) and the journal's website. The journal is distributed through the Rospechat Agency under the contract № 7407 of June 16, 2006, through individual buyers and commercial structures. The publication of articles is free.
The journal is included in the List of Russian Reviewed Scientific Journals of the Higher Attestation Commission. Since 2008 the journal has been available on the Internet and indexed in the RISC database which is placed on Web of Science. Since February 2nd, 2018, the journal "Medical Radiology and Radiation Safety" has been indexed in the SCOPUS abstract and citation database.
Brief electronic versions of the Journal have been publicly available since 2005 on the website of the Medical Radiology and Radiation Safety Journal: http://www.medradiol.ru. Since 2011, all issues of the journal as a whole are publicly available, and since 2016 - full-text versions of scientific articles. Since 2005, subscribers can purchase full versions of other articles of any issue only through the National Electronic Library. The editor of the Medical Radiology and Radiation Safety Journal in accordance with the National Electronic Library agreement has been providing the Library with all its production since 2005 until now.
The main working language of the journal is Russian, an additional language is English, which is used to write titles of articles, information about authors, annotations, key words, a list of literature.
Since 2017 the journal Medical Radiology and Radiation Safety has switched to digital identification of publications, assigning to each article the identifier of the digital object (DOI), which greatly accelerated the search for the location of the article on the Internet. In future it is planned to publish the English-language version of the journal Medical Radiology and Radiation Safety for its development. In order to obtain information about the publication activity of the journal in March 2015, a counter of readers' references to the materials posted on the site from 2005 to the present which is placed on the journal's website. During 2015 - 2016 years on average there were no more than 100-170 handlings per day. Publication of a number of articles, as well as electronic versions of profile monographs and collections in the public domain, dramatically increased the number of handlings to the journal's website to 500 - 800 per day, and the total number of visits to the site at the end of 2017 was more than 230.000.
The two-year impact factor of RISC, according to data for 2017, was 0.439, taking into account citation from all sources - 0.570, and the five-year impact factor of RISC - 0.352.
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.