ArticleBMC medical imaging2026
Interpretable machine learning models based on CT radiomics for predicting chemoradiotherapy response in rectal cancer.
Article in BMC medical imaging, 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
11 authors.
Funding
Abstract
backgroundAccurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) is essential for optimizing treatment decisions. This study aimed to develop interpretable machine learning models based on computed tomography (CT) radiomics and clinical biomarkers to predict nCRT efficacy.
methodsA total of 272 patients with pathologically confirmed LARC were retrospectively included and divided into training (n = 156), internal validation (n = 67), and external validation (n = 49) sets. Radiomics features were extracted from pretreatment contrast-enhanced CT images. A radiomics score (R-score) was constructed from 10 LASSO-selected features with high reproducibility (intraclass correlation coefficient > 0.75). Clinical variables including carcinoembryonic antigen (CEA) and carbohydrate antigen 19 − 9 (CA19-9) were incorporated. Logistic regression, support vector machine, random forest, decision tree, and XGBoost algorithms were used to develop predictive models. Model performance was assessed by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used to interpret model output.
resultsIn the test cohorts, the combined model using the XGBoost algorithm outperformed clinical-only and imaging-only models, achieving AUCs of 0.844 (internal validation) and 0.800 (external validation). The R-score was significantly higher in responders than in non-responders (P < 0.05 across all datasets). DCA demonstrated superior clinical net benefit of the combined model across threshold probabilities. SHAP analysis confirmed R-score as the most influential predictor of response.
conclusionsThe XGBoost-based combined model integrating CT radiomics and clinical biomarkers demonstrated robust performance and good interpretability in predicting nCRT response in LARC patients. This approach may support individualized treatment planning and risk stratification in clinical practice.
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.