ArticleCurrent medical imaging2026
The Predictive Value of Radiomics for Esophagotracheal Fistula after Radiotherapy in Esophageal Cancer.
Article in Current 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.
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Abstract
introductionEsophagotracheal Fistula (ETF) is a serious complication following radiotherapy for esophageal cancer, with treatment outcomes significantly worse than expected.
methodsPre-radiotherapy CT images and clinical data from patients with esophageal malignancies treated at the Second Affiliated Hospital of Wenzhou Medical University between January 2015 and December 2023 were retrospectively analyzed. Tumor contours were manually delineated using 3D Slicer, and radiomic features were extracted using PyRadiomics. Features associated with ETF development (p<0.05) were identified via the Mann-Whitney U test and further refined using Least Absolute Shrinkage and Selection Operator (LASSO) regression to determine the final radiomic signature. Subsequently, univariate and multivariate binary logistic regression analyses were performed.
resultsThe study included 77 patients, 30 of whom developed ETF. Of the initial 845 radiomic features, 10 were significantly associated with ETF. Among clinical factors, the type of radiation therapy was an independent predictor for ETF. In the training cohort, the radiomics model achieved an AUC of 0.866 (95% CI: 0.7907-0.9402), with a sensitivity of 0.831 and specificity of 0.792. The combined model (radiomics + clinical features) achieved an AUC of 0.892 (95% CI: 0.8238-0.9601), sensitivity of 0.823, and specificity of 0.912. In the validation cohort, the radiomics model had an AUC of 0.736 (95% CI: 0.5781-0.8947), sensitivity of 0.833, and specificity of 0.621. The combined model achieved an AUC of 0.791 (95% CI: 0.6461-0.9354), sensitivity of 0.822, and specificity of 0.797. DISCUSSION: The combination of radiomic and clinical features achieves excellent AUC performance and shows potential for the non-invasive prediction of ETF following radiotherapy in esophageal cancer patients.
conclusionThe model, combined with radiomic and clinical features, has great predictive value.
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