Intratumoral and peritumoral radiomics-based machine learning models for the postoperative survival prediction in esophageal squamous cell carcinoma.
Authors
Affiliations (8)
Affiliations (8)
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China.
- College of Medical Imaging, North Sichuan Medical College, Nanchong, China.
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China.
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, China.
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
- Department of Oncology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
- Department of Surgery, Tohoku University Graduate School of Medicine, Sendai, Japan.
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan.
Abstract
This study aimed to develop and validate machine learning (ML) models to predict survival following oesophagectomy in oesophageal squamous cell carcinoma (ESCC) patients using intratumoral and peritumoral radiomic features. A retrospective analysis was conducted on ESCC patients with preoperative contrast-enhanced computed tomography who underwent oesophagectomy from June 2016 to January 2020. Patients were randomly assigned to training and test sets (8:2 ratio). Radiomic features were independently extracted from intratumoral and peritumoral regions. Cox regression, random survival forests (RSF), and gradient boosting decision tree (GBDT) were used for modelling. The performance of models was evaluated by discrimination and calibration. The study included 443 patients, 354 in the training set and 89 in the test set. Peritumoral radiomic features predominated in the final selection, with 14 of 17 selected features originating from peritumoral region. The optimal GBDT model (iAUC: 0.854; iBS: 0.160) using dual-region radiomic and clinical features outperformed other models, with a 1-year tAUC of 0.712 (95% CI 0.655-0.738) and 3-year tAUC of 0.733 (95% CI 0.655-0.805). It effectively stratified patients into high- and low-risk groups (Pā<ā0.001). ML models using intratumoral and peritumoral radiomic features showed potential for predicting postoperative survival in ESCC patients, with the optimal GBDT model demonstrating effective risk stratification.