Ling Chenyang, Tao Ning, Maimaitiyimin Abudukeyoumu, Zhang Yifan, Yao Miao, Pu Hongyu, Li Xiaodong, Wang Yujie, An Hengqing
Department of Urological, Urology Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Xinjiang Medical University, Urumqi, Xinjiang, China.
Sci Rep. 2025 Aug 4;15(1):28378. doi: 10.1038/s41598-025-11445-5.
Prostate cancer (PCa) is the most prevalent malignant tumor in males, and many patients remain at risk of biochemical recurrence (BCR) following initial treatment. Accurate prediction of BCR is vital for effective clinical management and treatment planning. This study evaluates the effectiveness of machine learning (ML) models in predicting BCR among prostate cancer patients, comparing their performance to traditional prognostic methods. We systematically searched four databases (PubMed, Web of Science, Embase, and Cochrane) for studies employing ML techniques to predict prostate cancer BCR. Data extraction included model type, sample size, and the area under the curve (AUC). A meta-analysis was conducted using AUC as the primary performance metric to assess predictive accuracy and heterogeneity across models. Sixteen studies comprising a total of 17,316 prostate cancer patients were included. The pooled AUC for ML models was 0.82 (95% CI: 0.81-0.84). Deep learning and hybrid models outperformed traditional models (AUC = 0.83). Models using imaging data showed improved performance (AUC = 0.82). ML models were most effective in predicting 1-year BCR (AUC = 0.86), with performance slightly decreasing for longer time intervals. ML models outperform traditional methods in predicting BCR, especially in the short term. Incorporating multimodal data, such as imaging, enhances predictive accuracy. Future studies should optimize and validate these models through large-scale clinical trials.
前列腺癌（PCa）是男性中最常见的恶性肿瘤，许多患者在初始治疗后仍有生化复发（BCR）的风险。准确预测BCR对于有效的临床管理和治疗规划至关重要。本研究评估了机器学习（ML）模型在预测前列腺癌患者BCR方面的有效性，并将其性能与传统预后方法进行比较。我们系统地检索了四个数据库（PubMed、Web of Science、Embase和Cochrane），以查找采用ML技术预测前列腺癌BCR的研究。数据提取包括模型类型、样本量和曲线下面积（AUC）。使用AUC作为主要性能指标进行荟萃分析，以评估各模型的预测准确性和异质性。纳入了16项研究，共17316例前列腺癌患者。ML模型的合并AUC为0.82（95%CI：0.81-0.84）。深度学习和混合模型的表现优于传统模型（AUC = 0.83）。使用影像数据的模型表现有所改善（AUC = 0.82）。ML模型在预测1年BCR方面最有效（AUC = 0.86），随着时间间隔延长，性能略有下降。ML模型在预测BCR方面优于传统方法，尤其是在短期内。纳入多模态数据，如图像，可提高预测准确性。未来的研究应通过大规模临床试验对这些模型进行优化和验证。
