ArticleTranslational oncology2026
Computed tomography- and magnetic resonance imaging-based multi-modality radiomics models for predicting survival in esophageal cancer patients.
Article in Translational oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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- Erratum issued
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9 authors.
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Abstract
objectivesThis study explores the integration of radiomics features from contrast-enhanced CT and MRI with clinical and radiological risk factors for optimal prognosis models of disease-free survival (DFS) and overall survival (OS) in esophageal squamous cell carcinoma (ESCC). MATERIALS AND
methodsA retrospective study is undertaken of 371 ESCC patients who underwent contrast-enhanced CT and MRI with StarVIBE sequence, from September 2014 to December 2019. Prognosis models for DFS and OS were developed using cross-validation and Elastic-Net-Cox regression. Patients were grouped by treatment type (surgery, chemoradiotherapy, neoadjuvant therapy) to create single-modality and multi-modality models (Model-S, Model-CRT, and Model-nT). Model performance was evaluated using nomograms, calibration curves, and decision curves.
resultsTwelve optimal prognosis models were identified. For DFS, MRI c-indices were 0.595, 0.608, and 0.721, while CT c-indices were 0.686, 0.616, and 0.667. For OS, MRI c-indices were 0.692, 0.597, and 0.650, and CT c-indices were 0.656, 0.623, and 0.695. Multi-modality models demonstrated c-indices of 0.750, 0.695, 0.839 for DFS and 0.898, 0.777, 0.819 for OS.
conclusionSingle-modality radiomics models exhibit limited predictive ability for DFS and OS in ESCC patients, whereas multi-modality radiomics models enhance predictive accuracy significantly.
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