A systematic review of deep learning methods for the classification and segmentation of prostate cancer on magnetic resonance images

Prostate Cancer (PCa) is a prevalent global threat to male health, contributing significantly to male cancer-related mortality. Timely detection and management are pivotal for improved outcomes, as successful cure rates are highest in the early stages. Deep learning (DL) methodologies offer a promising avenue to enhance the precision of detection, potentially reducing mortality rates. Magnetic resonance imaging (MRI) stands out as an effective tool for PCa diagnosis, providing comprehensive visuals of the prostate and adjacent tissues. This technology aids in identifying cancerous developments early on, crucial for treatment planning. Utilizing MRI for PCa detection has demonstrated increased accuracy, minimizing unnecessary biopsies, and facilitating personalized treatment decisions. Recent studies showcase the potential of DL methods in identifying and segmenting the prostate in MRI scans. These techniques not only improve diagnostic precision but also assist in treatment planning and monitoring. Incorporating DL in MRI-based PCa diagnosis holds immense potential for enhancing efficiency and accuracy, promising better treatment outcomes. However, further research is imperative to explore these methods comprehensively, especially in larger and more diverse patient populations. This review evaluates the progress in employing DL techniques, rooted in artificial intelligence, for categorizing and outlining the prostate and lesions in MR images, underscoring the need for continued investigation and validation in varied clinical settings.