Radiomics and Prostate MRI: Current Role and Future Applications

Area of Science:

• Radiology
• Oncology
• Medical Imaging Analysis

Background:

• Multiparametric prostate magnetic resonance imaging (mpMRI) is crucial for evaluating men at risk of prostate cancer.
• Traditionally, mpMRI focused on staging, but its role is expanding.
• Radiomics offers quantitative image analysis for improved lesion characterization.

Purpose of the Study:

• To review the application of radiomics in prostate mpMRI for cancer detection and localization.
• To explore radiomics' role in predicting Gleason score, PI-RADS classification, extracapsular extension, and biochemical recurrence.
• To discuss the future integration of artificial intelligence (AI) in prostate cancer imaging.

Main Methods:

• Review of current literature on radiomics applied to prostate mpMRI.
• Analysis of radiomics' utility in various aspects of prostate cancer assessment.
• Exploration of machine learning and deep learning in prostate cancer imaging.

Main Results:

• Radiomics shows promise in detecting and localizing prostate cancer lesions on mpMRI.
• It aids in predicting key clinical parameters like Gleason score and PI-RADS classification.
• Radiomics can help predict extracapsular extension and biochemical recurrence risk.

Conclusions:

• Radiomics is a valuable tool for enhancing prostate mpMRI's diagnostic capabilities.
• It offers predictive insights into cancer aggressiveness and treatment response.
• AI integration holds significant future potential for prostate cancer management through imaging.