ArticleBMC cancer2025
Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Computed tomography- and magnetic resonance imaging-based multi-modality radiomics models for predicting survival in esophageal cancer patients.Translational oncology · 2026Article
- MR-Guided Radiotherapy in Oesophageal Cancer: From Principles to Practice-A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
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17 authors.
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Abstract
purposeTo establish prediction models to predict 2-year overall survival (OS) and stratify patients with different risks based on radiomics features extracted from magnetic resonance imaging (MRI) and computed tomography (CT) before definite chemoradiotherapy (dCRT) in locally advanced esophageal squamous cell carcinoma (ESCC).
methodsPatients with locally advanced ESCC were recruited. We extracted 547 radiomics features from MRI and CT images. The least absolute shrinkage and selection operator (LASSO) for COX algorithm was used to obtain features highly correlated with survival outcomes in the training cohort. Based on MRI, CT, and the hybrid image data, three prediction models were built. The predictive performance of the radiomics models was evaluated in the training cohort and verified in the validation cohort using AUC values.
resultsA total of 192 patients were included and randomized into the training and validation cohorts. In predicting 2-year OS, the AUCs of the CT-based model were 0.733 and 0.654 for the training and validation sets. The MRI radiomics-based model was observed with similar AUCs of 0.750 and 0.686 in the training and validation sets. The AUC values of hybrid model combining MRI and CT radiomics features in predicting 2-year OS were 0.792 and 0.715 in the training and validation cohorts. It showed significant differences in 2-year OS in the high-risk and low-risk groups divided by the best cutoff value in the hybrid radiomics-based model.
conclusionsThe hybrid radiomics-based model demontrated the best performance of predicting 2-year OS and can differentiate the high-risk and low-risk patients.
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