1. 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.
- Author
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Zhang Y, Cheng X, Luo X, Sun R, Huang X, Liu L, Zhu M, and Li X
- Subjects
- Humans, Male, Female, Middle Aged, Retrospective Studies, Aged, Prospective Studies, Tomography, X-Ray Computed, Adult, Radiomics, Esophageal Neoplasms diagnostic imaging, Esophageal Neoplasms therapy, Esophageal Neoplasms radiotherapy, Deep Learning, Chemoradiotherapy, Esophageal Fistula diagnostic imaging, Esophageal Fistula etiology
- Abstract
Background: Esophageal fistula (EF), a rare and potentially fatal complication, can be better managed with predictive models for personalized treatment plans in esophageal cancers. We aim to develop a clinical-deep learning radiomics model for effectively predicting the occurrence of EF., Methods: The study involved esophageal cancer patients undergoing radiotherapy or chemoradiotherapy. Arterial phase enhanced CT images were used to extract handcrafted and deep learning radiomic features. Along with clinical information, a 3-step feature selection method (statistical tests, Least Absolute Shrinkage and Selection Operator, and Recursive Feature Elimination) was used to identify five feature sets in training cohort for constructing random forest EF prediction models. Model performance was compared and validated in both retrospective and prospective test cohorts., Results: One hundred seventy five patients (122 in training and 53 in test cohort)were retrospectively collected from April 2018 to June 2022. An additional 27 patients were enrolled as a prospective test cohort from June 2022 to December 2023. Post-selection in the training cohort, five feature sets were used for model construction: clinical, handcrafted radiomic, deep learning radiomic, clinical-handcrafted radiomic, and clinical-deep learning radiomic. The clinical-deep learning radiomic model excelled with AUC of 0.89 (95% Confidence Interval: 0.83-0.95) in the training cohort, 0.81 (0.65-0.94) in the test cohort, and 0.85 (0.71-0.97) in the prospective test cohort. Brier-score and calibration curve analyses validated its predictive ability., Conclusions: The clinical-deep learning radiomic model can effectively predict EF in patients with advanced esophageal cancer undergoing radiotherapy or chemoradiotherapy., Competing Interests: Declarations Ethics approval and consent to participate The research was authorized by the Institutional Human Ethics Committee Institutional Committee of the Hefei Cancer Hospital, Chinese Academy of Science, under the ethics approval number SL-KY2023-007. The study was conducted in compliance with the ethical principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants before they were included in the research. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)
- Published
- 2024
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