Dynamics of mammary gland cancer incidence in the Russian Federation and European countries over the last 10 years

Objective. Comparative analysis of the dynamics of mammary gland cancer incidence in the Russian Federation and European countries over the last 10 years. Materials and methods. In accordance with the study objective, a comparative analysis of statistical data was performed, based on official data from statistical reports of the Ministry of Health of the Russian Federation and data from available literature on the research problem obtained from open statistical and informational databases such as PUBMED. Results. The conducted study revealed a number of important trends and patterns in the dynamics of mammary gland cancer prevalence in the Russian Federation and European countries. Based on the obtained data, it can be stated that mammary gland cancer is a widespread disease, and the dynamics of its prevalence are relentlessly increasing. Conclusions. The obtained results can be used to develop more effective measures for the prevention and early diagnosis of mammary gland cancer. To timely detect possible changes and develop effective measures for prevention and treatment, it is necessary to continue monitoring the incidence of mammary gland cancer throughout the Russian Federation and European countries.

Authors

Institutions

Publication Details

Journal
Psychosomatic and Integrative Research
Published
2025-09-23
DOI
https://doi.org/10.15275/pssr.2025.0303
Primary Topic
Cancer Risks and Factors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dynamics of mammary gland cancer incidence in the Russian Federation and European countries over the last 10 years

M. А. Polidanov, Darya A. Kuznetsova, Vladimir Vladimirovich Maslyakov, K. А. Volkov et al.
Psychosomatic and Integrative Research
Cancer Risks and Factors
article

Dynamics of mammary gland cancer incidence in the Russian Federation and European countries over the last 10 years

M. А. Polidanov, Darya A. Kuznetsova, Vladimir Vladimirovich Maslyakov, K. А. Volkov, Andrey A. Kashikhin, Polina B. Tsukanova
article en

Abstract

Objective. Comparative analysis of the dynamics of mammary gland cancer incidence in the Russian Federation and European countries over the last 10 years. Materials and methods. In accordance with the study objective, a comparative analysis of statistical data was performed, based on official data from statistical reports of the Ministry of Health of the Russian Federation and data from available literature on the research problem obtained from open statistical and informational databases such as PUBMED. Results. The conducted study revealed a number of important trends and patterns in the dynamics of mammary gland cancer prevalence in the Russian Federation and European countries. Based on the obtained data, it can be stated that mammary gland cancer is a widespread disease, and the dynamics of its prevalence are relentlessly increasing. Conclusions. The obtained results can be used to develop more effective measures for the prevention and early diagnosis of mammary gland cancer. To timely detect possible changes and develop effective measures for prevention and treatment, it is necessary to continue monitoring the incidence of mammary gland cancer throughout the Russian Federation and European countries.

Psychosomatic and Integrative ResearchVol. 11(3)
Saratov State University (RU), Samara Medical Institute Reaviz (RU), Saratov State Medical University (RU), Samara State Medical University (RU)
Openalex Percentile: Top 65%
Cancer Risks and Factors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.