Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review

Liu, J; Zhang, H; Woon, DTS; Perera, M; Lawrentschuk, N

Author(s): Liu, J; Zhang, H; Woon, DTS; Perera, M; Lawrentschuk, N;
Details: Publication Year 2024-10-25,Volume 16,Issue #21,Page 3596
Journal Title: Cancers
Publication Type: Review
Abstract: Background/Objectives: Biochemical recurrence (BCR) after radical prostatectomy (RP) is a significant predictor of distal metastases and mortality in prostate cancer (PCa) patients. This systematic review aims to evaluate the accuracy of artificial intelligence (AI) in predicting BCR post-RP. Methods: Adhering to PRISMA guidelines, a comprehensive literature search was conducted across Medline, Embase, Web of Science, and IEEE Xplore. Studies were included if they utilised AI to predict BCR in patients post-RP. Studies involving patients who underwent radiotherapy or salvage RP were excluded. This systematic review was registered on PROSPERO (International prospective register of systematic reviews) under the ID CRD42023482392. Results: After screening 9764 articles, 24 met the inclusion criteria. The included studies involved 27,216 patients, of whom 7267 developed BCR. AI algorithms developed using radiological parameters demonstrated higher predictive accuracy (median AUROC of 0.90) compared to algorithms based solely on pathological variables (median AUROC of 0.74) or clinicopathological variables (median AUROC of 0.81). According to the Prediction Model Risk of Bias Assessment Tool (PROBAST), the overall risk of bias was unclear in three studies due to ambiguous inclusion criteria and the exclusion of many patients because of missing follow-up data. In seven studies, the developed AI outperformed or was at least equivocal to traditional methods of BCR prediction. Conclusions: AI shows promise in predicting BCR post-RP, particularly when radiological data were used in its development. However, the significant variability in AI performance and study methodologies highlights the need for larger, standardised prospective studies with external validation prior to clinical application.
Publisher: MDPI
Keywords: artificial intelligence; biochemical recurrence; convolutional neural network; deep learning; machine learning; prostate cancer; prostatic neoplasm
Department(s): Surgical Oncology
Publisher's Version: https://doi.org/10.3390/cancers16213596
Open Access at Publisher's Site: https://doi.org/10.3390/cancers16213596
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2025-01-07 06:16:09

Last Modified: 2025-01-07 06:21:14