ArticleTranslational cancer research2026
Development and validation of a multivariable CT-based Delta-radiomics model for predicting efficacy to bevacizumab therapy in patients with colorectal liver metastases.
Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.