为了分析不同轮廓方法下，腹腔分割法导出的剂量容积指标预测器对胃肠道（GI）毒性模型的影响，我们采用了随机森林机器学习方法预测了246例（毒性发病率=9.5%）接受肛门鳞状细胞癌（SCCA）定义性化疗放射治疗的患者的急性3级+ GI毒性情况，其中包括剂量容积指标和临床病理因素。根据不同的肠袋分割方法，构建了三种随机森林模型：1）按照放疗肿瘤学协作组（RTOG）准则由医生绘制的医师划定法2）由深度学习模型（nnU-Net）自动分割按照RTOG准则绘制的自动分割法3）自动分割然而跨越整个肠袋空间。每种模型类型都使用重复交叉验证（100次迭代；50%/50%训练/测试分割）进行评估。使用精确召回率曲线下面积（AUPRC）、接受者操作特征曲线下面积（AUROCC）以及最佳F1分数评估模型的性能。在遵循RTOG指南时，基于nnU-Net自动分割（平均值：AUROCC=0.71±0.07，AUPRC=0.42±0.09，F1分数=0.46±0.08）的模型明显优于（p<0.001）基于医师划定的轮廓（平均值：AUROCC=0.67±0.07，AUPRC=0.34±0.08，F1分数=0.36±0.07）的模型。当跨越整个肠袋空间时，自动分割模型的性能显著提高（平均值：AUROCC=0.87±0.05，AUPRC=0.70±0.09，F1分数=0.68±0.09）。与基于RTOG定义的肠袋自动分割相比，随机森林模型在预测急性G3+GI毒性方面表现出较高的优势。基于跨越整个肠袋空间的自动分割模型进一步改善了模型性能。本研究的结果应使用外部数据集进行进一步验证。版权所有 © 2023. Elsevier Inc. 发表。

To analyze the impact on gastrointestinal (GI) toxicity models when their dose-volume metrics predictors are derived from segmentations of the peritoneal cavity following different contouring approaches.A random forest machine learning approach was utilized to predict acute grade 3+ GI toxicity from dose-volume metrics and clinicopathological factors for 246 patients (toxicity incidence = 9.5%) treated with definitive chemoradiation for squamous cell carcinoma of the anus (SCCA). Three types of random forest models were constructed based on different bowel bag segmentation approaches: 1) physician-delineated following Radiation Therapy Oncology Group (RTOG) guidelines 2) auto-segmented by a deep learning model (nnU-Net) following RTOG guidelines, and 3) auto-segmented, but spanning the entire bowel space. Each model type was evaluated using repeated cross-validation (100 iterations; 50%/50% training/test split). The performance of the models was assessed using area under the precision-recall curve (AUPRC) and the receiver operating characteristic curve (AUROCC), as well as optimal F1-Score.When following RTOG guidelines, the models based on the nnU-Net auto-segmentations (mean values: AUROCC = 0.71±0.07 AUPRC = 0.42±0.09; F1-Score = 0.46±0.08) significantly outperformed (p<0.001) those based on the physician-delineated contours (mean values: AUROCC = 0.67±0.07; AUPRC = 0.34±0.08; F1-Score = 0.36±0.07). When spanning the entire bowel space, the performance of the auto-segmentation models improved considerably (mean values: AUROCC = 0.87±0.05 AUPRC = 0.70±0.09; F1-Score = 0.68±0.09).Random forest models were superior at predicting acute G3+ GI toxicity when based on RTOG defined bowel bag auto-segmentations rather than physician-delineated contours. Models based on auto-segmentations spanning the entire bowel space show further considerable improvement in model performance. The results of this study should be further validated using an external dataset.Copyright © 2023. Published by Elsevier Inc.