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

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.