Rajih Emad, Borhan Walaa M, Elhassan Yasir Hassan, Elhakim Assaad
Department of General and Specialized Surgery, College of Medicine, Taibah University, Madinah, Saudi Arabia.
Department of Basic Medical Sciences, College of Medicine, Taibah University, Madinah, Saudi Arabia.
J Robot Surg. 2025 Sep 25;19(1):631. doi: 10.1007/s11701-025-02786-4.
Robotic-assisted radical prostatectomy (RARP) has become the gold standard treatment for localized prostate cancer. However, predicting post-operative outcomes remains challenging. This study aims to develop and validate predictive models for key outcomes using machine learning approaches and compare them with traditional risk stratification systems. We conducted a retrospective analysis of 758 consecutive patients who underwent RARP between 2014 and 2018. Pre-operative variables included PSA, Gleason score, clinical stage, and IPSS scores. Primary outcomes were biochemical recurrence (BCR), positive surgical margins (PSM) (PSM), and functional outcomes at 12 months. Machine learning algorithms were compared with D'Amico and CAPRA risk stratification systems. The cohort included 758 patients with a mean age of 60.5 years. At 12-month follow-up (n = 634), biochemical recurrence rate was 4.5% (29/634). For pre-operative counseling applications, the machine learning model using only pre-surgical variables achieved AUC 0.783 for predicting 12-month biochemical recurrence, significantly outperforming D'Amico classification (AUC 0.692, p < 0.001). The comprehensive post-operative model incorporating pathological variables achieved optimal performance (AUC 0.847 for 12-month BCR, AUC 0.863 for 24-month BCR). At 12-month follow-up, biochemical recurrence occurred in 4.5% (34/753) of patients. Key pre-operative predictors included PSA (OR 1.23 per ng/mL, 95% CI 1.15-1.31), biopsy Gleason score ≥ 8 (OR 3.45, 95% CI 2.18-5.46), and clinical stage ≥ T2b (OR 2.67, 95% CI 1.89-3.77). Machine learning-based prediction models significantly outperform traditional risk stratification systems for predicting post-operative outcomes in RARP. These models provide personalized risk assessment to guide treatment decisions and patient counseling.
机器人辅助根治性前列腺切除术（RARP）已成为局限性前列腺癌的金标准治疗方法。然而，预测术后结果仍然具有挑战性。本研究旨在使用机器学习方法开发并验证关键结果的预测模型，并将其与传统风险分层系统进行比较。我们对2014年至2018年间连续接受RARP的758例患者进行了回顾性分析。术前变量包括前列腺特异性抗原（PSA）、 Gleason评分、临床分期和国际前列腺症状评分（IPSS）。主要结果是生化复发（BCR）、手术切缘阳性（PSM）以及12个月时的功能结果。将机器学习算法与达米科（D'Amico）和CAPRA风险分层系统进行了比较。该队列包括758例患者，平均年龄为60.5岁。在12个月的随访中（n = 634），生化复发率为4.5%（29/634）。对于术前咨询应用，仅使用术前变量的机器学习模型预测12个月生化复发的曲线下面积（AUC）为0.783，显著优于达米科分类法（AUC为0.692，p < 0.001）。纳入病理变量的综合术后模型表现最佳（12个月BCR的AUC为0.847，24个月BCR的AUC为0.863）。在12个月的随访中，4.5%（34/753）的患者出现生化复发。关键的术前预测因素包括PSA（每纳克/毫升的比值比[OR]为1.23，95%置信区间[CI]为1.15 - 1.31）、活检Gleason评分≥8（OR为3.45，95% CI为2.18 - 5.46）以及临床分期≥T2b（OR为2.67，95% CI为1.89 - 3.77）。基于机器学习的预测模型在预测RARP术后结果方面显著优于传统风险分层系统。这些模型提供个性化风险评估，以指导治疗决策和患者咨询。
