A Metabolic-Imaging Integrated Model for Prognostic Prediction in Colorectal Liver Metastases
It addresses the problem of suboptimal prognostic accuracy for clinicians managing colorectal liver metastases, though it is incremental as it builds on existing models by mitigating data leakage risks.
This study tackled the challenge of predicting postoperative recurrence risk in colorectal liver metastases by developing a machine learning model using preoperative clinical and imaging data, achieving an AUC of 0.723 for 3-month recurrence prediction and demonstrating clinical utility through decision curve analysis.
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.