Medical Radiology and Radiation Safety. 2025. Vol. 70. № 3

DOI:10.33266/1024-6177-2025-70-3-54-69

V.I. Burmistrov¹, E.I. Matkevich², I.V. Ivanov^{1, 3}

Analysis of the Radiation Situation in Aviation Flights under Conditions of Solar Proton Events

¹ I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

² A.I. Burnazyan Federal Medical Biophysical Center, Moscow, Russia

³ N.F. Izmerov Research Institute of Occupational Medicine, Moscow, Russia

Contact person: I.V. Ivanov, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract

With a general analysis of the levels of ionizing radiation characteristic of outer space and causing radiation hazard factors for astronauts, the issues of assessing the radiation situation in air travel also remain relevant. The purpose of the study was to analyze the types and characteristics of ionizing radiation in airspace up to heights of 20 km above the Ground and possible radiation doses to flight personnel during flights under these conditions. The composition of ionizing radiation and the energy characteristics of proton events are analyzed. The radiation dose rates are estimated depending on the altitude and geographical latitude of the flight. To minimize the exposure of flight personnel and radiation risks, it is important to systematically take into account the forecast of solar activity, altitude and latitude of flight, control the total flight time per year, radiation protection conditions and other factors.

Keywords: aviation flights, flight personnel, civil aviation, radiation situation, solar activity, proton events, radiation doses, dosimetry, anti-radiation protection

For citation: Burmistrov VI, Matkevich EI, Ivanov IV. Analysis of the Radiation Situation in Aviation Flights under Conditions of Solar Proton Events. Medical Radiology and Radiation Safety. 2025;70(3):54–69. (In Russian). DOI:10.33266/1024-6177-2025-70-3-54-69

 

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 PDF (RUS) Full-text article (in Russian)

 

Conflict of interest. The authors declare no conflict of interest.

Financing. The study had no sponsorship.

Contribution. Article was prepared with equal participation of the authors.

Article received: 20.02.2025. Accepted for publication: 25.03.2025.

 

 

Medical Radiology and Radiation Safety. 2025. Vol. 70. № 3

DOI:10.33266/1024-6177-2025-70-3-83-89

Muaayed F. Al-Rawi, Izz K. Abboud, Nasir A. Al-Awad

Using Machine Learning Algorithms to Detect Cancer Automatically

College of Engineering, Mustansiriyah University, Baghdad, Iraq

Contact person: Muaayed F. Al-Rawi, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract

The number of people diagnosed with cancer is growing all around the world. During the last twenty years, the overall cancer incidence in Iraq has doubled, leading to an increase in the number of diagnosed cancer fatalities. When it comes to deaths that occur in hospitals, cancer is the second-biggest cause. Therefore, a remedy to the issue should be an arrangement to decrease time waste, the right technique of directing the patient to notice symptoms, extremely accurate cancer detection, and a better monitoring system. The proposed method is an arrangement that lets and leads a patient to identify symptoms on their own, guiding them to a proper healthcare professional, correctly diagnosing cancer in its initial stages, and monitoring the patient throughout therapy. Currently, research into cancer detection systems only employs a single machine learning approach to identify cancer. The study that is being presented makes use of Convolutional Neural Networks (CNN), Random Forest, and the XGBoost Classifier, which are a machine learning algorithms that are applied to structured and tabular data in order to identify the existence of breast cancer, brain tumors, skin cancer, and lung cancer. These methods provide findings more quickly while also achieving a greater level of accuracy. Hosting this suggested solution in the cloud with a cutting-edge program will make it available to the public, providing an improved user experience and easier operation.

Keywords: radiation diagnostics, machine learning, CNN, Random Forest, XGBoost classifier, Cancer detection, Brain cancer, Skin cancer, Lung cancer 

For citation: Al-Rawi Muaayed F., Abboud Izz K., Al-Awad Nasir A. Using Machine Learning Algorithms to Detect Cancer Automatically. Medical Radiology and Radiation Safety. 2025;70(3):83–89. DOI:10.33266/1024-6177-2025-70-3-83-89

 

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 PDF (RUS) Full-text article (in Russian)

 

Conflict of interest. The authors declare no conflict of interest.

Financing. The study had no sponsorship.

Contribution. Article was prepared with equal participation of the authors.

Article received: 20.02.2025. Accepted for publication: 25.03.2025.

 

Medical Radiology and Radiation Safety. 2025. Vol. 70. № 3

DOI:10.33266/1024-6177-2025-70-3-70-82

A.N. Koterov¹, L.N. Ushenkova¹, A.A. Wainson²,
D.Yu. Usupzhanova¹, A.Yu. Bushmanov¹

‘Healthy Worker Effect’ In Employees of Medical-Biological and Chemical Laboratories: Comparison with Effects in Nuclear Workers, in other Professional Contingents (Meta-Analyses),
and the Role of the Radiation Factor

¹ A.I. Burnazyan Federal Medical Biophysical Center, Moscow, Russia

² N.N. Blokhin Russian Cancer Research Center, Moscow, Russia

Contact person: Alexey N. Koterov, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract

Based on a search in a supported database of sources for standardized mortality ratio (SMR; compared with the general population) for all causes and all cancer mortality for various professions and types of employment, in PubMed, the Cochrane Library, Elibrary, through Google, Google Scholar and in the reference lists of found sources, a complete/representative sample of studies of SMR all causes and SMR all cancer for personnel of biomedical, agricultural, chemical and some other research laboratories (a total of 39 sources and 3 reviews) was formed. Meta-analyses performed on the basis of this sample demonstrated a high Healthy Worker Effect (HWE) for all causes for the total group of workers, for the group including biomedical, sanitary and agricultural laboratories, and for the group of chemists. SMR (±95 % confidence intervals) were, respectively: 0.63 (0.58; 0.68); 0.65 (0.57; 0.74) and 0.62 (0.56; 0.68). For SMR all cancer HWE, although of a smaller value, it was demonstrated for the first and second groups: 0.85 (95 % CI: 0.75; 0.96) and 0.78 (95 % CI: 0.70; 0.88), respectively. At the same time, for chemists, no excess in mortality from all cancers was noted either: 0.88 (95 % CI: 0.74; 1.05).

The HWE values for laboratory workers were compared with those for occupations characterized by the highest HWE values (cosmonauts/astronauts, athletes, pilots, nuclear workers, and the military; individual studies, meta- and pooled analyses). It was found that the scientific laboratory personnel had a HWE level for SMR all causes comparable to that of athletes and nuclear workers. However, the SMR all cancer index for laboratory researchers was significantly higher than in all comparison groups, with the exception of the military, which is obviously due to contacts with a variety of carcinogenic factors when working in laboratories.

Due to the absence of prerequisites for the formation of HWE characteristic of the compared contingents for the researchers, an assumption was made that the decrease in overall mortality among laboratory personnel may be primarily due to a scientific mindset, which allows them to better navigate the causal dependencies of life and more adequately prevent various consequences, rather than a special lifestyle, increased socioeconomic status and a decrease in the frequency of smoking (as some authors suggest).

Analysis of the influence of the radiation factor (external and internal irradiation) on the studied mortality rates for laboratory workers revealed some effects in relation to certain types of malignant neoplasms (leukemia, myeloma, lung cancer, bone cancer, etc.), but a number of them were not previously recognized as radiation-induced and their increase may be a consequence of the effect of other carcinogenic factors of work in chemical, biochemical, molecular biological and other laboratories.

Keywords: laboratory researchers, biomedical laboratories, chemists, all cause mortality, all cancer mortality, healthy worker effect, radiation exposure

For citation: Koterov AN, Ushenkova LN, Wainson AA, Usupzhanova DYu, Bushmanov AYu. ‘Healthy Worker Effect’ In Employees of Medical-Biological and Chemical Laboratories: Comparison with Effects in Nuclear Workers, in other Professional Contingents (Meta-Analyses), and the Role of the Radiation Factor. Medical Radiology and Radiation Safety. 2025;70(3):70–82. (In Russian). DOI:10.33266/1024-6177-2025-70-3-70-82

 

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 PDF (RUS) Full-text article (in Russian)

 

Conflict of interest. The authors declare no conflict of interest.

Financing. The study had no sponsorship.

Contribution. Article was prepared with equal participation of the authors.

Article received: 20.02.2025. Accepted for publication: 25.03.2025.

 

 

Medical Radiology and Radiation Safety. 2025. Vol. 70. № 3

DOI:10.33266/1024-6177-2025-70-3-90-98

K.V. Koval, A.S. Tokarev, A.A. Kanibolotskiy, O.L. Evdokimova, A.A. Grin 

Pathomorphological Changes in Cell Structures of Cerebral Metastasis of Lung Adenocarcinoma after Neoadjuvant Gamma Knife Radiosurgy. A Case Report

N.V. Sklifosovsky Scientific Research Institute of First Aid, Moscow, Russia

Contact person: K.V. Koval, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract

Purpose: To identify and describe morphological changes in the cells of lung adenocarcinoma metastasis to the brain after preoperative (neoadjuvant) Gamma Knife radiosurgery.

Material and methods: A 63-year-old female patient with brain metastases of lung adenocarcinoma including large metastasis in the right frontal lobe. Neoadjuvant stereotactic radiosurgery was performed by Leksell Gamma Knife Icon. Histological and immunohistochemical studies were performed after microsurgical removal of the metastasis in the right frontal lobe. The analysis of scanned images was performed using the NDP.view2 program of the Image Viewing software (© Hamamatsu Photonics K.K.).

Results: The result of histological and immunohistochemical studies is TTF-I+, ROS- lung adenocarcinoma. The most significant changes were coagulation necrosis, vasculopathy, altered blood vessels with endothelial damage, affected cells with pyknotic nuclei, and islets of coagulation necrosis with cells of adenocarcinoma. Despite the descriptive characteristics of early post-radiation changes, apparently caused by radiosurgical exposure, the specific mechanism of post-radiation reactions occurring in malignant cells of cerebral metastases remains to be understood. It is necessary to include the series of cases, in particular, with subsequent analysis of ultramicroscopic findings obtained by electron microscopy.

Keywords: cerebral metastases, neoadjuvant radiosurgery, stereotactic radiosurgery, immunohistochemistry, molecular genetic testing, gamma knife

For citation: Koval KV, Tokarev AS, Kanibolotskiy AA, Evdokimova OL, Grin AA. Pathomorphological Changes in Cell Structures of Cerebral Metastasis of Lung Adenocarcinoma after Neoadjuvant Gamma Knife Radiosurgy. A Case Report. Medical Radiology and Radiation Safety. 2025;70(3):90–98. (In Russian). DOI:10.33266/1024-6177-2025-70-3-90-98

 

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Conflict of interest. The authors declare no conflict of interest.

Financing. The study had no sponsorship.

Contribution. Article was prepared with equal participation of the authors.

Article received: 20.02.2025. Accepted for publication: 25.03.2025.