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

• URL: http://arxiv.org/abs/2507.19734v1
• Date: Sat, 26 Jul 2025 01:29:38 GMT
• Title: A Metabolic-Imaging Integrated Model for Prognostic Prediction in Colorectal Liver Metastases
• Authors: Qinlong Li, Pu Sun, Guanlin Zhu, Tianjiao Liang, Honggang QI,
• Abstract summary: This study developed and validated a robust machine learning model for predicting postoperative recurrence risk.<n>We restricted input variables to preoperative baseline clinical parameters and radiomic features from contrast-enhanced CT imaging.<n>The 3-month recurrence prediction model demonstrated optimal performance with an AUC of 0.723 in cross-validation.
• Score: 5.6492616107251274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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