ArticleJournal of translational medicine2025
A nomogram integrating machine learning-derived CT radiomics and clinical characteristics for prognostic assessment in patients with locally advanced esophageal squamous cell carcinoma treated with definitive chemoradiotherapy with or without immunotherapy.
Article in Journal of translational medicine, 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.
- Feature Selection and Machine Learning Strategies for CT Radiomics-Based Survival Prediction in Non-Small Cell Lung Cancer: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- Interpretable machine learning integrating intra-/peritumoral CT radiomics and serum indicators for predicting poorly differentiated esophageal squamous cell carcinoma.Frontiers in oncology · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
objectivesThis study aimed to build a radiomics signature using machine learning methods to estimate overall survival in patients with locally advanced esophageal squamous cell carcinoma (ESCC) who underwent definitive chemoradiotherapy (dCRT), and to verify its prognostic value across independent patient cohorts.
methodsWe retrospectively included 200 ESCC patients with histological confirmation from three medical centers. Radiomics models were constructed employing machine learning algorithms. A predictive nomogram combining radiomics-derived risk metrics with clinical features was established. Model performance was assessed by the concordance index (C-index), time-dependent ROC curves, and decision curve analysis (DCA). Similar modeling approaches were also applied to an independent immunotherapy-treated cohort.
resultsThe developed radiomics signature exhibited modest predictive ability for overall survival in advanced ESCC patients treated with dCRT. High-risk individuals experienced reduced survival in the training cohort (p = 0.028) and validation cohort (p = 0.021) datasets, with similar findings observed in two external validation cohorts. The integrated nomogram combining clinical and radiomic features outperformed other predictive models and demonstrated potential clinical value for survival prediction. Within the immunotherapy-treated subgroup, the radiomics signature remained a statistically significant predictor of survival (p = 0.002), and the combined nomogram consistently exhibited acceptable prognostic performance.
conclusionsA reliable radiomics signature was established to effectively estimate survival outcomes in patients with advanced ESCC undergoing chemoradiotherapy or immunotherapy. Combining this model with clinical data enhanced its predictive capacity, underscoring its value for personalized prognostic evaluation.
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