A review on innovations in prostate cancer diagnosis: automated techniques for gleason score estimation via mpMRI and DWSI imaging

Abstract Diagnosis of prostate cancer is an area of medical research of critical importance, in which advancements in imaging technologies have much improved detection as well as treatment outcomes. Although substantial progress has been made concerning the application of machine learning (ML) and deep learning (DL) techniques, few systematic reviews have examined these techniques in the context of multiparametric MRI (mpMRI) and diffusion-weighted synthetic imaging (DWSI). Existing studies often focus on individual methods or imaging modalities, leaving a gap in understanding how these techniques are integrated and optimized for diagnostic precision. This motivated this review paper to comprehensively review and summarize the new automated methods for prostate cancer diagnosis, particularly through the use of mpMRI and DWSI imaging. It explores various imaging modalities and their integration with DL and ML techniques to improve diagnostic accuracy. The review assesses the effectiveness of these advanced imaging approaches in Gleason score (GS) estimation and highlights the challenges associated with each modality. The review systematically compares performance evaluated by specific feature values such as specificity, F-measure, precision, and accuracy of several ML and DL algorithms for prostate cancer diagnosis. Alongside this, the review brings to attention the current limitations of the approaches and points to future research directions with an emphasis on the innovative requirement for finding better generalization methods to mitigate diagnosis problems in prostate cancer management.