Radiomics for diagnosing clinically significant prostate cancer PI-RADS 3: what is already known and what to do next?

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Abstract

BACKGROUND: Prostate cancer is currently the second most commonly diagnosed cancer in men. The second edition of the Prostate Imaging Magnetic Resonance Imaging Data Assessment and Reporting System (PI-RADS) was released in 2019 to standardize the diagnostic process. Within this classification, the PI-RADS 3 category indicates an intermediate risk of clinically significant prostate cancer. There is currently no consensus in the literature regarding the optimal treatment for patients in this category. Some researchers advocate for biopsy as a means of further evaluation, while others propose a strategy of active surveillance for these patients.

AIM: The aim of this study is to analyze and compare existing diagnostic models based on radiomics to differentiate and detect clinically significant prostate cancer in patients with a PI-RADS 3 category.

MATERIALS AND METHODS: A comprehensive search of the PubMed, Scopus, and Web of Science databases was conducted using the following keywords: PI-RADS 3, radiomics, texture analysis, clinically significant prostate cancer, with additional emphasis on studies evaluated by Radiology Quality Score. The selected studies were required to meet the following criteria: (1) identification of PI-RADS 3 according to version 2.1 guidelines, (2) use of systemic biopsy as a control, (3) use of tools compatible with the IBSI standard for analyzing radiologic features, and (4) detailed description of methodology. Consequently, four meta-analyses and 12 original articles were selected.

RESULTS: Radiomics-based diagnostic models have demonstrated considerable potential for enhancing the accuracy of detecting clinically significant prostate cancer in the PI-RADS 3 category using the PI-RADS V2.1 system. However, studies by A. Stanzione A. et al. and J. Bleker et al. have identified quality issues with such models, which constrains their clinical application based on low Radiology Quality Score values. In contrast, the works of T. Li et al. and Y. Hou et al. proposed innovative methods, including nomogram development and the application of machine learning, which demonstrated the potential of radiomics in improving diagnosis for this category. This indicates the potential for further development and application of radiomics in clinical practice.

CONCLUSIONS: Although the models developed today cannot completely replace PI-RADS, the inclusion of radiomics can greatly enhance the efficiency of the diagnostic process by providing radiologists with quantitative and qualitative criteria that will enable the diagnosis of prostate cancer with greater confidence.

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BACKGROUND: Prostate cancer is currently the second most commonly diagnosed cancer in men. The second edition of the Prostate Imaging Magnetic Resonance Imaging Data Assessment and Reporting System (PI-RADS) was released in 2019 to standardize the diagnostic process. Within this classification, the PI-RADS 3 category indicates an intermediate risk of clinically significant prostate cancer. There is currently no consensus in the literature regarding the optimal treatment for patients in this category. Some researchers advocate for biopsy as a means of further evaluation, while others propose a strategy of active surveillance for these patients.

AIM: The aim of this study is to analyze and compare existing diagnostic models based on radiomics to differentiate and detect clinically significant prostate cancer in patients with a PI-RADS 3 category.

MATERIALS AND METHODS: A comprehensive search of the PubMed, Scopus, and Web of Science databases was conducted using the following keywords: PI-RADS 3, radiomics, texture analysis, clinically significant prostate cancer, with additional emphasis on studies evaluated by Radiology Quality Score. The selected studies were required to meet the following criteria: (1) identification of PI-RADS 3 according to version 2.1 guidelines, (2) use of systemic biopsy as a control, (3) use of tools compatible with the IBSI standard for analyzing radiologic features, and (4) detailed description of methodology. Consequently, four meta-analyses and 12 original articles were selected.

RESULTS: Radiomics-based diagnostic models have demonstrated considerable potential for enhancing the accuracy of detecting clinically significant prostate cancer in the PI-RADS 3 category using the PI-RADS V2.1 system. However, studies by A. Stanzione A. et al. and J. Bleker et al. have identified quality issues with such models, which constrains their clinical application based on low Radiology Quality Score values. In contrast, the works of T. Li et al. and Y. Hou et al. proposed innovative methods, including nomogram development and the application of machine learning, which demonstrated the potential of radiomics in improving diagnosis for this category. This indicates the potential for further development and application of radiomics in clinical practice.

CONCLUSIONS: Although the models developed today cannot completely replace PI-RADS, the inclusion of radiomics can greatly enhance the efficiency of the diagnostic process by providing radiologists with quantitative and qualitative criteria that will enable the diagnosis of prostate cancer with greater confidence.

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About the authors

Alexandra S. Tyan

A.V. Vishnevsky National Medical Research Center of Surgery

Author for correspondence.
Email: tyan_a_s@staff.sechenov.ru
ORCID iD: 0009-0007-4193-7413
Russian Federation, Moscow

Grigoriy G. Karmazanovskij

A.V. Vishnevsky National Medical Research Center of Surgery

Email: karmazanovsky@yandex.ru
ORCID iD: 0000-0002-9357-0998
Russian Federation, Moscow

Natalia A. Karelskaya

A.V. Vishnevsky National Medical Research Center of Surgery

Email: karelskaya.n@yandex.ru
Russian Federation, Moscow

Evgeniy V. Kondratyev

A.V. Vishnevsky National Medical Research Center of Surgery

Email: evgenykondratiev@gmail.com
Russian Federation, Moscow

Alexander D. Kovalev

A.V. Vishnevsky National Medical Research Center of Surgery

Email: aledmikov@yandex.ru
Russian Federation, Moscow

References

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  9. Jin P, Shen J, Yang L, et al. Machine learning-based radiomics model to predict benign and malignant PI-RADS v2. 1 category 3 lesions: a retrospective multi-center study. BMC Medical Imaging. 2023;23(1):1–13. doi: 10.1186/s12880-023-01002-9
  10. Stanzione A, Gambardella M, Cuocolo R, et al. Prostate MRI radiomics: A systematic review and radiomic quality score assessment. European journal of radiology. 2020;129:109095. doi: 10.1016/j.ejrad.2020.109095
  11. Bleker J, Kwee TC, Yakar D. Quality of multicenter studies using MRI radiomics for diagnosing clinically significant prostate cancer: a systematic review. Life. 2022;12(7):946. doi: 10.3390/life12070946
  12. Hou Y, Bao ML, Wu CJ, et al. A radiomics machine learning-based redefining score robustly identifies clinically significant prostate cancer in equivocal PI-RADS score 3 lesions. Abdominal Radiology. 2020;45:4223–4234. doi: 10.1007/s00261-020-02678-1

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