用于结直肠肝转移预后预测的代谢影像整合模型
A Metabolic-Imaging Integrated Model for Prognostic Prediction in Colorectal Liver Metastases
AI总结：
本研究构建融合术前临床参数与增强CT影像组学特征的机器学习模型，用于预测CRLM术后早期复发，并强调避免数据泄漏以提升临床转化可靠性。
摘要：
由于传统临床模型的准确性欠佳，结直肠肝转移（CRLM）患者的预后评估仍具挑战性。本研究开发并验证了一个用于预测术后复发风险的稳健机器学习模型。初步集成模型取得了极高性能（AUC > 0.98），但纳入了术后特征，从而带来数据泄漏风险。为提高临床适用性，我们将输入变量限制为术前基线临床参数和来自增强CT成像的影像组学特征，专门针对术后3、6和12个月的复发预测。3个月复发预测模型表现出最佳性能，交叉验证中AUC为0.723。决策曲线分析显示，在0.55–0.95的阈值概率范围内，该模型始终比“全部治疗”或“均不治疗”策略提供更大的净获益，支持其在术后监测和治疗决策中的效用。本研究成功开发了一个具有已证实临床效用的早期CRLM复发稳健预测模型。重要的是，它强调了临床预后建模中数据泄漏的严重风险，并提出了一个严谨框架以缓解该问题，从而提升模型在真实世界环境中的可靠性和转化价值。
英文摘要：
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.
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