Exploring the utility of artificial intelligence in identifying progression of prostate cancer during active surveillance: A systematic review.
Authors
Affiliations (10)
Affiliations (10)
- EJ Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC, Australia.
- Department of Surgery, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia.
- University of Melbourne, Department of Surgery, Melbourne, VIC, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.
- Discipline of Surgery, University of Adelaide, Adelaide, SA, Australia.
- Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC, Australia.
- EJ Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC, Australia. [email protected].
- Department of Surgery, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC, Australia. [email protected].
- University of Melbourne, Department of Surgery, Melbourne, VIC, Australia. [email protected].
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia. [email protected].
Abstract
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. 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. 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. 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.