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.
背景/目的：根治性前列腺切除术（RP）后生化复发（BCR）是前列腺癌（PCa）患者发生远处转移和死亡的重要预测因素。本系统综述旨在评估人工智能（AI）在预测RP后BCR方面的准确性。方法：遵循PRISMA指南，在Medline、Embase、Web of Science和IEEE Xplore数据库中进行全面文献检索。纳入标准为使用AI预测RP后患者BCR的研究，排除涉及接受放疗或挽救性RP患者的研究。本系统综述已在PROSPERO（国际系统综述前瞻性注册库）注册，编号为CRD42023482392。结果：在筛选9764篇文章后，24篇符合纳入标准。纳入研究共涉及27,216名患者，其中7267例发生BCR。基于放射学参数开发的AI算法显示出更高的预测准确性（中位AUROC为0.90），优于仅基于病理学变量（中位AUROC为0.74）或临床病理学变量（中位AUROC为0.81）的算法。根据预测模型偏倚风险评估工具（PROBAST）评估，三项研究因纳入标准不明确及大量患者因随访数据缺失被排除，总体偏倚风险不明确。在七项研究中，所开发的AI模型在BCR预测方面优于或至少等同于传统预测方法。结论：AI在预测RP后BCR方面展现出潜力，尤其是在开发过程中整合放射学数据时。然而，AI性能和研究方法存在显著异质性，提示在临床应用前需要进行更大规模、标准化且经过外部验证的前瞻性研究。
肿瘤(癌症)患者之家