A machine learning-based prediction model for colorectal liver metastasis.

Abstract

Colorectal liver metastasis (CRLM) is a primary factor contributing to poor prognosis and metastasis in colorectal cancer (CRC) patients. This study aims to develop and validate a machine learning (ML)-based risk prediction model using conventional clinical data to forecast the occurrence of CRLM. This retrospective study analyzed the clinical data of 865 CRC patients between January 2018 and September 2024. Patients were categorized into non-CRLM and CRLM groups. The least absolute shrinkage and selection operator regression was employed to identify key clinical variables, and five ML algorithms were utilized to develop prediction models. The optimal model was selected based on performance metrics including the receiver operating characteristic curve, precision-recall curve, decision curve analysis, and calibration curve, which collectively evaluated both the predictive accuracy and clinical utility of the model. Among the five ML algorithms evaluated, Random forest demonstrated the best performance. Leveraging the Random forest algorithm, we developed the CRLM-Lab6 prediction model, which incorporates six features: LDH, CA199, ALT, CEA, TBIL, and AGR. This model exhibits robust predictive performance, achieving an area under the curve of 0.94, a sensitivity of 0.88, and a specificity of 0.93. To enhance its practical utility, the model has been integrated into an accessible web application. This study developed a novel risk prediction model by integrating ML algorithms with conventional laboratory test data to evaluate the likelihood of CRLM occurrence. The model demonstrates excellent predictive performance and has significant clinical application potential.