Trial reportRadiation oncology (London, England)2022
Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures.
Trial report in Radiation oncology (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy.Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus · 2023Pooled it
- Machine learning applications in the detection and treatment of esophageal cancer.Discover oncology · 2026Review
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Intratumoural and Peritumoural Radiomics-Based Machine Learning Models for the Postoperative Survival Prediction in Oesophageal Squamous Cell Carcinoma.Interdisciplinary cardiovascular and thoracic surgery · 2026Article
- Computed tomography- and magnetic resonance imaging-based multi-modality radiomics models for predicting survival in esophageal cancer patients.Translational oncology · 2026Article
- Interpretable survival modeling integrating nutritional-inflammatory biomarkers in elderly patients with locally advanced esophageal squamous cell carcinoma treated with definitive radiotherapy.Frontiers in immunology · 2026Article
- Building an interpretable machine learning prognosis prediction model-based on baseline examinations of patients with esophageal cancer undergoing surgery.Frontiers in oncology · 2026Article
- Longitudinal CT-based deep learning radiomics for predicting prognosis in esophageal squamous cell carcinoma treated with definitive chemoradiotherapy: a two-center study.Journal of Cancer · 2026Article
- Preoperative CT-based Radiomics for Predicting Response to Neoadjuvant Chemoimmunotherapy in Esophageal Squamous Cell Carcinoma.Radiology. Imaging cancer · 2026Article
- Validation and refinement of the 8th edition of the AJCC staging system for esophageal squamous cell carcinoma treated with definitive IMRT or chemo-IMRT: a Chinese multi-institutional retrospective study.Radiation oncology (London, England) · 2025Article
- Machine learning combined with CT-based radiomics predicts the prognosis of oesophageal squamous cell carcinoma.Insights into imaging · 2025Article
- Incorporating frequency domain features into radiomics for improved prognosis of esophageal cancer.Medical & biological engineering & computing · 2025Article
- A novel framework for esophageal cancer grading: combining CT imaging, radiomics, reproducibility, and deep learning insights.BMC gastroenterology · 2025Article
- Camrelizumab combined with apatinib plus irinotecan as a second-line treatment in advanced or metastatic esophageal squamous cell carcinoma patients.BMC cancer · 2025Article
- Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients.BMC gastroenterology · 2025Article
- Current Role of Artificial Intelligence in the Management of Esophageal Cancer.Journal of clinical medicine · 2025Review
- CT radiomics to predict pathologic complete response after neoadjuvant immunotherapy plus chemoradiotherapy in locally advanced esophageal squamous cell carcinoma.European radiology · 2025Article
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- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeTo construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients.
methods204 ESCC patients were randomly divided into training cohort (n = 143) and test cohort (n = 61) according to the ratio of 7:3. Two radiomics models were constructed by radiomics features, which were selected by LASSO Cox model to predict PFS and OS, respectively. Clinical features were selected by univariate and multivariate Cox proportional hazards model (p < 0.05). Combined radiomics and clinical model was developed by selected clinical and radiomics features. The receiver operating characteristic curve, Kaplan Meier curve and nomogram were used to display the capability of constructed models.
resultsThere were 944 radiomics features extracted based on volume of interest in CT images. There were six radiomics features and seven clinical features for PFS prediction and three radiomics features and three clinical features for OS prediction; The radiomics models showed general performance in training cohort and test cohort for prediction for prediction PFS (AUC, 0.664, 0.676. C-index, 0.65, 0.64) and OS (AUC, 0.634, 0.646.C-index, 0.64, 0.65). The combined models displayed high performance in training cohort and test cohort for prediction PFS (AUC, 0.856, 0.833. C-index, 0.81, 0.79) and OS (AUC, 0.742, 0.768. C-index, 0.72, 0.71).
conclusionWe developed combined radiomics and clinical machine learning models with better performance than radiomics or clinical alone, which were used to accurate predict 3 years PFS and OS of non-surgical ESCC patients. The prediction results could provide a reference for clinical decision.
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