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The review evaluates the effectiveness of artificial intelligence in identifying prostate cancer progression during active surveillance.

May 14, 2026Open Access

Exploring the utility of artificial intelligence in identifying progression of prostate cancer during active surveillance: A systematic review

Key Points

The review evaluates the effectiveness of artificial intelligence in identifying prostate cancer progression during active surveillance.
Systematic review registered on PROSPERO (ID CRD42024529354).
Literature search conducted on Medline, Embase, Web of Science, and IEEE Xplore.
Included studies evaluating AI in active surveillance-eligible prostate cancer patients.
12 studies included, with a total of 4622 active surveillance patients; 1022 experienced progression.
AI algorithms incorporating MRI parameters achieved an AUC of 0.65 to 0.95, significantly outperforming traditional logistic regression in one study.
No studies were found to have a high risk of bias as assessed by PROBAST.

Abstract

Abstract Background There is a recent surge in interest in the application of artificial intelligence (AI) in prostate cancer (PCa). When evaluating PSMA PET scans, AI has shown promise in detecting intraprostatic cancer and metastatic disease. This systematic review aims to assess the ability of AI to detect or predict the progression of PCa during AS. Methods This systematic review was registered on PROSPERO (ID CRD42024529354) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. A comprehensive literature search was performed on Medline, Embase, Web of Science, and IEEE Xplore. Only studies evaluating AI in AS-eligible patients were included. Results After screening 842 articles, 12 studies were suitable for inclusion. The included studies comprised of 4622 AS patients of whom 1022 experienced progressions. Only three studies utilised developed their AI purely on clinicopathological variables. The area under curve (AUC) of these three AI ranged between 0.65 and 0.76, and the AI algorithm in one study outperformed traditional logistic regression. The integration of MRI parameters particularly the use of radiomics improves the ability of AI to predict progression as compared to clinicopathological variables alone. AI was also able to analyse serial MRI during AS and performs on par with the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) scoring system. The AUC of the AI algorithms which included MRI parameters ranged between 0.65 and 0.95. One of the limitations was the variability in study methodologies and inclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used and none of the studies had high risk of bias. Conclusion AI shows promise in detecting PCa progression during AS. However, this systematic review highlights the need for larger prospective studies with external validation before AI can be integrated into the AS process.

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