标题： 一种代谢影像整合模型用于结直肠肝转移的预后预测 显示英文标题

标题： A Metabolic-Imaging Integrated Model for Prognostic Prediction in Colorectal Liver Metastases

摘要： 预后评估在结直肠肝转移患者（CRLM）中仍然具有挑战性，因为传统临床模型的准确性不佳。 本研究开发并验证了一个稳健的机器学习模型，用于预测术后复发风险。 初步的集成模型表现出极高的性能（AUC$>$0.98），但包含了术后特征，引入了数据泄露的风险。 为了提高临床适用性，我们将输入变量限制为术前基线临床参数和增强CT影像的放射组学特征，特别针对术后3、6和12个月的复发预测。 3个月复发预测模型在交叉验证中表现出最佳性能，AUC为0.723。 决策曲线分析显示，在阈值概率0.55-0.95范围内，该模型始终比“全部治疗”或“不治疗”策略提供更大的净收益，支持其在术后监测和治疗决策中的应用。 本研究成功开发了一个经过证实具有临床实用性的早期CRLM复发预测模型。 重要的是，它强调了在临床预后建模中数据泄露的关键风险，并提出了一个严格的框架来缓解这一问题，提高了模型在现实环境中的可靠性和转化价值。 显示英文摘要

摘要： Prognostic evaluation in patients with colorectal liver metastases (CRLM) remains challenging due to suboptimal accuracy of conventional clinical models. This study developed and validated a robust machine learning model for predicting postoperative recurrence risk. Preliminary ensemble models achieved exceptionally high performance (AUC $>$ 0.98) but incorporated postoperative features, introducing data leakage risks. To enhance clinical applicability, we restricted input variables to preoperative baseline clinical parameters and radiomic features from contrast-enhanced CT imaging, specifically targeting recurrence prediction at 3, 6, and 12 months postoperatively. The 3-month recurrence prediction model demonstrated optimal performance with an AUC of 0.723 in cross-validation. Decision curve analysis revealed that across threshold probabilities of 0.55-0.95, the model consistently provided greater net benefit than "treat-all" or "treat-none" strategies, supporting its utility in postoperative surveillance and therapeutic decision-making. This study successfully developed a robust predictive model for early CRLM recurrence with confirmed clinical utility. Importantly, it highlights the critical risk of data leakage in clinical prognostic modeling and proposes a rigorous framework to mitigate this issue, enhancing model reliability and translational value in real-world settings.