ArticleThoracic cancer2021
Development and validation of a prognostic nomogram for malignant esophageal fistula based on radiomics and clinical factors.
Article in Thoracic cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- Treatment patterns and prognostic factors of radiation-associated esophageal fistula in patients with esophageal squamous cell carcinoma: a retrospective cohort study.Journal of thoracic disease · 2026Article
- Development and Validation of Predictive Models for Inflammatory Bowel Disease Diagnosis: A Machine Learning and Nomogram-Based Approach.Journal of inflammation research · 2025Article
- Prediction of esophageal fistula in radiotherapy/chemoradiotherapy for patients with advanced esophageal cancer by a clinical-deep learning radiomics model : Prediction of esophageal fistula in radiotherapy/chemoradiotherapy patients.BMC medical imaging · 2024Article
- Clinical-radiomics nomogram for the risk prediction of esophageal fistula in patients with esophageal squamous cell carcinoma treated with intensity-modulated radiation therapy or volumetric-modulated arc therapy.Journal of thoracic disease · 2024Article
- Multi-omics and Multi-VOIs to predict esophageal fistula in esophageal cancer patients treated with radiotherapy.Journal of cancer research and clinical oncology · 2024Article
- Risk Factors of Esophageal Fistula Associated with Volumetric Modulated Arc Therapy for Esophageal Squamous Cell Cancer.International journal of general medicine · 2024Article
- Radiomics analysis based on CT's greater omental caking for predicting pathological grading of pseudomyxoma peritonei.Scientific reports · 2022Article
- Development and validation of a prognostic nomogram for malignant esophageal fistula based on radiomics and clinical factors.Thoracic cancer · 2021Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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Abstract
backgroundThe current study aimed to comprehensively analyze the clinical prognostic factors of malignant esophageal fistula (MEF). Furthermore, this study sought to establish and validate prognostic nomograms incorporating radiomics and clinical factors to predict overall survival and median survival after fistula for patients with MEF.
methodsThe records of 76 patients with MEF were retrospectively analyzed. A stepwise Cox proportional hazards regression model was employed to screen independent prognostic factors and develop clinical nomograms. Radiomic features were extracted from prefistula CT images and post fistula CT images. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression algorithm was used to filter radiomic features and avoid overfitting. Radiomic signature was a linear combination of optimal features and corresponding coefficients. The joint prognostic nomograms was constructed by radiomic signatures and clinical features. All models were validated by Harrell's concordance index (C-index), caliberation and bootstrap validation.
resultsFor overall survival, age, prealbumin, KPS and interval between diagnosis of esophageal cancer and fistula were identified as independent prognostic factors and incorporated into the clinical nomogram. Age, prealbumin, serum albumin, KPS and neutrophil proportion were selected for the clinical nomogram of post fistula survival. The C-index of overall survival nomogram was 0.719 (95% CI: 0.645-0.793) and that was 0.722 (95% CI: 0.653-0.791) in the post fistula survival nomogram. The radiomic signature developed by radiomic features of prefistula CT showed a significant correlation with both overall survival and post fistula survival. The C-index of joint nomogarm for overall survival and post fistula survival was 0.831 (95% CI: 0.757-0.905) and 0.77 (95% CI: 0.686-0.854), respectively. The calibration curve showed the joint nomograms outperformed the clinical ones.
conclusionsThe study presents nomograms incorporating independent clinical risk factors and radiomic signature to predict the prognosis of MEF. This prognostic classification system has the potential to guide therapeutic decisions for patients with malignant esophageal fistulas.
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