ArticleAbdominal radiology (New York)2026
CT-derived extracellular volume fraction and AI-assisted body composition for predicting neoadjuvant therapy response and prognosis in colorectal cancer.
Article in Abdominal radiology (New York), 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
6 authors.
Funding
Abstract
objectivesTo 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). MATERIALS AND
methodsThis 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.
resultsA 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).
conclusionThe combined model may help predict treatment response and survival outcomes in patients with CRC undergoing NAT.
Indexed as
Identifiers
42726133What Socratic 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 Socratic graph.