CT-derived extracellular volume fraction and AI-assisted body composition for predicting neoadjuvant therapy response and prognosis in colorectal cancer.

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Abstract

To develop a combined model integrating clinical parameters, quantitative parameters from contrast-enhanced computed tomography (CECT), and Artificial Intelligence-assisted body composition metrics for predicting treatment response and prognosis in colorectal cancer (CRC) patients receiving neoadjuvant therapy (NAT). This retrospective study included CRC patients received NAT and surgery between January 2016 and June 2025. Quantitative CECT parameters (extracellular volume fraction [ECV] and arterial enhancement fraction), and body composition parameters (skeletal muscle index [SMI], skeletal muscle attenuation, and visceral-to-subcutaneous adipose tissue ratio) were measured before and after NAT, and their changes were calculated. Logistic regression analysis was used to identify independent predictors of treatment response. A multivariable logistic regression model was constructed, and evaluated using the area under the receiver operating characteristic curve (AUC). Patients were stratified into high- and low-risk groups based on risk score produced from the model. Cox regression analysis was used to assess association between risk score and prognosis. A total of 163 patients (age, 58.50 ± 10.55 years) were included. A combined model incorporating post-NAT carcinoembryonic antigen, ECV and SMI achieved an AUC of 0.836 (95% CI: 0.754-0.915) and 0.784 (95% CI: 0.583-0.947) in the training and validation cohorts. The risk score was significantly associated with prognosis. High-risk patients had shorter overall survival (Hazard ratio [HR]: 3.745, 95% CI: 1.014-13.836, P = 0.048) and disease-free survival (HR: 2.801, 95% CI: 1.050-7.475, P = 0.040). The combined model may help predict treatment response and survival outcomes in patients with CRC undergoing NAT.

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