Key points are not available for this paper at this time.
Background: Active surveillance (AS) is an established management strategy for patients with low- and favorable intermediate-risk prostate cancer (PCa). Accurately distinguishing indolent from aggressive disease and identifying progression during follow-up remain significant clinical challenges. Artificial intelligence (AI) has appeared as a promising approach to enhance risk stratification and support clinical decision-making. Our aim was to evaluate the role of AI-based decision support systems in patient selection and monitoring during AS for PCa, as well as their potential to guide timely transition to definitive treatment. Methods: A scoping review was conducted in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines. The review protocol was prospectively registered in PROSPERO (CRD420261358519). Literature searches in PubMed and Scopus were performed, applying filters for English-language studies published within the past 5 years. Of 133 records identified, 24 studies met the inclusion criteria after screening and eligibility assessment. Results: Most included studies were retrospective and used machine learning (ML) or deep learning (DL) techniques on clinical, radiological, and histopathological data. AI models demonstrated stronger predictive performance than traditional statistical methods, with reported area under the curve (AUC) values ranging from 0.74 to 0.96. Radiomics-based models derived from magnetic resonance imaging (MRI) facilitated non-invasive characterization of tumor aggressiveness, while DL algorithms applied to biopsy whole-slide images improved grading accuracy and progression prediction. Multimodal models that integrated heterogeneous data sources consistently outperformed single-modality approaches. Finally, longitudinal models including serial prostate-specific antigen (PSA) measurements and imaging data enabled dynamic risk assessment and customized monitoring during AS. Conclusion: AI-based models show considerable potential to improve patient selection and predict progression in AS for PCa, potentially reducing overtreatment and unnecessary biopsies. However, current evidence is limited by retrospective study designs, lack of external validation, and variability in AS protocols. Prospective multicenter studies and enhanced model explainability are necessary before routine clinical implementation. Level of evidence: Not applicable.
Anthousis et al. (2026) studied this question.