@article{TCR120232,
author = {Long Yuan and Shenglin Li and Yuntai Cao and Ming Xu and Huaze Xi and Jingjing Yang and Junlin Zhou},
title = {Development and validation of a multivariable CT-based Delta-radiomics model for predicting efficacy to bevacizumab therapy in patients with colorectal liver metastases},
journal = {Translational Cancer Research},
volume = {15},
number = {7},
year = {2026},
keywords = {},
abstract = {Background: Early prediction of efficacy of bevacizumab-combined chemotherapy in colorectal liver metastases (CRLM) remains challenging. This multicenter study aimed to develop and validate a multivariable computed tomography (CT)-based Delta-radiomics model to enable early and accurate prediction of treatment efficacy.Methods: We retrospectively analyzed consecutive patients with CRLM treated with bevacizumab-combined chemotherapy at three institutions from January 2018 to January 2023. According to Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, the therapeutic response of liver metastases and patient efficacy after 6 months of treatment were evaluated. The initial texture features were extracted from baseline and 2-month CT images to calculate temporal texture features (Ratio, Delta, DeltaABS). Eight logistic regression models using clinical and texture features were developed to predict the 6-month therapeutic response of liver metastases. Model performance was evaluated using area under the curve (AUC), calibration curves and decision curve analyses. Overall survival (OS) was analyzed using Kaplan-Meier curves and Cox regression.Results: A total of 90 patients and 255 liver metastases were included, with 133 liver metastases (52.16%) classified as responsive and 52 patients (57.78%) classified as responders. The Ratio, Delta and COMB models demonstrated superior performance in predicting the therapeutic response of liver metastases, with AUC ranging from 0.858 to 0.956 (training), 0.891 to 0.899 (internal validation), and 0.833 to 0.922 (external validation) across these models. The calibration and decision curves demonstrated that the prediction probabilities of the three models were highly consistent with the observed results and had good clinical utility. Cox regression analysis identified patient efficacy as the sole independent predictor of OS (P=0.002).Conclusions: The multivariable CT-based Delta-radiomics model demonstrates excellent performance in the early prediction of treatment efficacy of bevacizumab-combined chemotherapy in patients with CRLM, providing a novel tool for guiding personalized treatment strategies and early therapeutic assessment.},
issn = {2219-6803}, url = {https://tcr.amegroups.org/article/view/120232}
}