ARTIFICIAL INTELLIGENCE IN PROSTATE CANCER IMAGING: CUTTING-EDGE APPROACHES TO DETECTION, DIAGNOSIS, PROGNOSTICATION, AND CLINICAL APPLICATIONS FOR PRECISION MEDICINE

Abstract

Prostate cancer remains one of the leading causes of cancer-related morbidity and mortality among men worldwide, underscoring the urgent need for accurate detection, risk stratification, and treatment planning. Imaging plays a central role in every stage of prostate cancer management, with multiparametric magnetic resonance imaging (mpMRI) recognized as the gold standard for lesion detection and characterization, and ultrasound and positron emission tomography (PET), particularly with prostate-specific membrane antigen (PSMA) tracers, providing complementary insights. However, interpretation of these imaging modalities is frequently limited by inter-observer variability, heterogeneous image quality, and the difficulty of reliably distinguishing indolent from clinically significant disease. Artificial intelligence (AI) has rapidly emerged as a transformative technology with the potential to overcome these challenges and redefine prostate cancer imaging. Radiomics-based approaches enable the extraction of high-dimensional quantitative features that serve as imaging biomarkers for diagnosis and prognosis, while deep learning architectures including convolutional neural networks and transformer-based models offer automated lesion detection, segmentation, and risk classification with performance often comparable to or exceeding expert radiologists. Beyond detection and diagnosis, AI is increasingly applied to prognostication, Gleason score prediction, treatment response monitoring, and integration with clinical and genomic data to support precision medicine. Despite this progress, several challenges hinder the widespread clinical adoption of AI in prostate cancer imaging. These include the limited availability of large, annotated, multi-institutional datasets; variability across scanners, protocols, and institutions that reduces model generalizability; concerns regarding algorithm transparency and explainability; and regulatory, ethical, and medico-legal barriers to deployment. Addressing these issues will require collaborative efforts between clinicians, data scientists, and policymakers, alongside prospective validation in large-scale, multicenter clinical trials. Looking ahead, innovations such as federated learning, explainable AI frameworks, and the integration of multimodal data sources promise to accelerate translation into clinical practice. By enhancing the reliability of detection, improving diagnostic accuracy, refining prognostic assessments, and guiding individualized treatment strategies, AI has the potential to substantially improve patient outcomes and drive the future of precision oncology in prostate cancer care.