Application of radiomics in the diagnosis of cervical cancer: systematic review

Objective: To analyze the results of a study on the effectiveness of radiomic analysis in the interpretation of radiation images in clarifying the diagnosis of cervical cancer. Materials and Methods. A systematic literature search was conducted in the PubMed/MEDLINE, eLibrary, and Scopus databases, as well as in NCCN, ESUR, and ACR resources. Results. When selecting medical articles, a total of 289 unique publications were identified, 218 of which met the exclusion criteria. The final review included 71 articles. The average accuracy characteristics of the models were estimated based on the area under the ROC curve (AUC), including accuracy, sensitivity, specificity, and C-index. Conclusion. The main key aspects and advantages of the use of radiomics and textural image analysis in the diagnosis of cervical cancer are considered. The introduction of radiomic analysis has led to a renewed perception of medical image analysis. The results of a number of studies demonstrate that the data extracted using radiomic analysis have significant diagnostic and prognostic value, allowing an objective assessment of tumor characteristics, its stage and prevalence, and differential diagnosis of neoplasms.

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Publication Details

Journal
Medical Visualization
Published
2025-09-18
DOI
https://doi.org/10.24835/1607-0763-1547
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Application of radiomics in the diagnosis of cervical cancer: systematic review

S. P. Aksenova, Н. В. Нуднов, A. A. Borisov, V. А. Solodkiy et al.
Medical Visualization
Radiomics and Machine Learning in Medical Imaging
article

Application of radiomics in the diagnosis of cervical cancer: systematic review

S. P. Aksenova, Н. В. Нуднов, A. A. Borisov, V. А. Solodkiy, M. Е. Ivannikov, E. S.-A. Shakhvalieva, P. N. Sultanova, D. G. Karelidze, S. R. Semenova, A. I. Makovetskaya
article en

Abstract

Objective: To analyze the results of a study on the effectiveness of radiomic analysis in the interpretation of radiation images in clarifying the diagnosis of cervical cancer. Materials and Methods. A systematic literature search was conducted in the PubMed/MEDLINE, eLibrary, and Scopus databases, as well as in NCCN, ESUR, and ACR resources. Results. When selecting medical articles, a total of 289 unique publications were identified, 218 of which met the exclusion criteria. The final review included 71 articles. The average accuracy characteristics of the models were estimated based on the area under the ROC curve (AUC), including accuracy, sensitivity, specificity, and C-index. Conclusion. The main key aspects and advantages of the use of radiomics and textural image analysis in the diagnosis of cervical cancer are considered. The introduction of radiomic analysis has led to a renewed perception of medical image analysis. The results of a number of studies demonstrate that the data extracted using radiomic analysis have significant diagnostic and prognostic value, allowing an objective assessment of tumor characteristics, its stage and prevalence, and differential diagnosis of neoplasms.

Medical VisualizationVol. 29(3)
Peoples' Friendship University of Russia (RU), Pirogov Russian National Research Medical University (RU), Russian Medical Academy of Continuous Professional Education (RU), Federal State Budgetary Institution Russian Scientific Center of Roentgenoradiology (RU), Ministry of Health of the Russian Federation (RU), City Clinical Hospital (RU)
Openalex Percentile: Top 61%
Radiomics and Machine Learning in Medical Imaging
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