Background/Objectives: Anastomotic leakage (AL) remains a major complication after restorative rectal cancer surgery, with accurate preoperative risk stratification posing a significant challenge. Pelvic measurements derived from magnetic resonance imaging (MRI) have been proposed as potential predictors of AL, but their clinical utility remains uncertain.Methods: This retrospective, multicenter cohort study analyzed rectal cancer patients undergoing restorative surgery between 2013 and 2021. Pelvic dimensions were assessed using MRI-based pelvimetry. Univariate and multivariate regression analyses identified independent risk factors for AL. Subsequently, machine Learning (ML) models—logistic regression, random forest classifier, and XGBoost—were developed to predict AL using preoperative clinical data alone and in combination with pelvimetry. Model performance was evaluated using F1 scores, with the area under the receiver operating characteristic (ROC-AUC) and precision–recall curves (AUC-PR) as primary metrics.Results: Among 487 patients, the overall AL rate was 14%. Multivariate regression analysis identified distance to the anorectal junction, pelvic inlet width, and interspinous distance as independent risk factors for AL (p< 0.05). The logistic regression model incorporating pelvimetry achieved the highest predictive performance, with a mean ROC-AUC of 0.70 ± 0.09 and AUC-PR of 0.32 ± 0.10. Although predictive models that included pelvic measurements demonstrated higher ROC-AUCs compared to those without pelvimetry, the improvement was not statistically significant.Conclusions: Pelvic dimensions, specifically pelvic inlet and interspinous distance, were independently associated with an increased risk of AL. While ML models incorporating pelvimetry showed only moderate predictive performance, these measurements should be considered in developing clinical prediction tools for AL to enhance preoperative risk stratification.
背景/目的：吻合口漏是直肠癌保肛术后主要并发症之一，术前准确的风险分层仍面临重大挑战。基于磁共振成像的骨盆测量参数被认为是预测吻合口漏的潜在指标，但其临床实用性尚不明确。 方法：本回顾性多中心队列研究纳入2013年至2021年间接受保肛手术的直肠癌患者。通过磁共振成像骨盆测量评估盆腔解剖参数。采用单因素及多因素回归分析明确吻合口漏的独立危险因素。随后构建逻辑回归、随机森林分类器和XGBoost三种机器学习模型，分别基于单纯术前临床数据及联合骨盆测量参数预测吻合口漏。模型性能通过F1分数进行评估，并以受试者工作特征曲线下面积和精确率-召回率曲线下面积作为主要评价指标。 结果：在487例患者中，总体吻合口漏发生率为14%。多因素回归分析显示，肿瘤距肛直肠交界距离、骨盆入口宽度和坐骨棘间距离是吻合口漏的独立危险因素（p<0.05）。融合骨盆测量参数的逻辑回归模型预测效能最佳，其平均受试者工作特征曲线下面积为0.70±0.09，精确率-召回率曲线下面积为0.32±0.10。虽然纳入骨盆测量参数的预测模型较未纳入者显示出更高的受试者工作特征曲线下面积，但该改善未达到统计学显著性。 结论：骨盆解剖参数（特别是骨盆入口宽度和坐骨棘间距离）与吻合口漏风险增加独立相关。尽管融合骨盆测量参数的机器学习模型仅显示中等预测效能，但在构建临床预测工具时应纳入这些参数，以提升术前风险分层能力。
肿瘤(癌症)患者之家