Multi-Reader Multi-Case Trial Evaluating Computer-Aided Tool for Prognostic Prediction of Colorectal Liver Metastases

This study aims to evaluate the impact of a novel computer-aided prognostic prediction tool on clinician performance in managing patients with colorectal liver metastases (CRLM). Colorectal cancer remains one of the leading causes of cancer-related mortality worldwide, with approximately 20-30% of patients presenting synchronous liver metastases at diagnosis. These metastases are associated with poor prognosis and a high rate of postoperative recurrence. For selected patients, simultaneous resection of the primary colorectal tumor and liver metastases is the preferred treatment approach, though clinical outcomes vary widely. To address this variability, the latest web-based prediction tool employs Random Forest machine learning models that integrate comprehensive demographic, clinical, laboratory, and genetic data. This tool is specifically designed to predict postoperative recurrence and mortality for CRLM patients undergoing simultaneous resection, enabling individualized risk assessment. In this multiple-reader, multiple-case (MRMC) study, 12 physicians will independently evaluate 166 retrospective patient cases. Each physician will estimate the risk of disease recurrence and mortality at 1-, 3-, and 5-year time points, both with and without access to the prediction tool. These two assessment phases will be separated by a washout period to minimize bias. The primary objective is to determine whether use of the tool improves the accuracy of predicting 3-year postoperative mortality, quantified by the area under the receiver operating characteristic curve (AUC-ROC). Secondary and exploratory endpoints include prediction accuracy at other time points, sensitivity, specificity, inter-rater reliability, clinician confidence in decision-making, and time required for evaluation. By providing specific, data-driven risk estimates, this computer-aided prognostic tool aims to enhance clinical decision-making and support personalized treatment planning for CRLM patients undergoing simultaneous resection, ultimately striving to improve patient outcomes.