{"ID":2890042,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19734","arxiv_id":"2507.19734","title":"A Metabolic-Imaging Integrated Model for Prognostic Prediction in Colorectal Liver Metastases","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 $\u003e$ 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.","short_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 hi...","url_abs":"https://arxiv.org/abs/2507.19734","url_pdf":"https://arxiv.org/pdf/2507.19734v1","authors":"[\"Qinlong Li\",\"Pu Sun\",\"Guanlin Zhu\",\"Tianjiao Liang\",\"Honggang QI\"]","published":"2025-07-26T01:29:38Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\",\"cs.LG\",\"q-bio.QM\"]","methods":"[]","has_code":false}