Abstract
Purpose To evaluate the performance of a CT-based model combining two-dimensional (2D) and two-and-a-half-dimensional (2.5D) deep learning (DL) with radiomic features in predicting neoadjuvant chemoimmunotherapy response in patients with esophageal squamous cell carcinoma (ESCC). Materials and Methods In this retrospective study, patients with ESCC from Sun Yat-sen Cancer Center between May 2020 and January 2023 were divided into training (80%) and internal validation (20%) groups, while an external testing group was obtained from Nanfang Hospital between January 2021 and March 2023. Radiomic features were extracted manually, while 2D and 2.5D deep transfer learning (DTL) features were derived from pretrained DL networks. The optimal model was selected based on a comparison of the areas under the receiver operating characteristic curves (AUCs). Results In total, 251 patients (mean age, 59.91 years ± 7.63; 209 male and 42 female) were included in the study, with 157 and 94 patients from centers 1 and 2, respectively. The support vector machine (SVM) model outperformed the other radiomic and DL models, while ResNet18 had the best predictive performance among the 2D and 2.5D DL models. The SVM model with ResNet18-based DTL features showed the best performance, achieving AUC values of 0.85 (95% CI: 0.76, 0.91) for 2D DTL and 0.84 (95% CI: 0.75, 0.91) for 2.5D DTL in the external testing group. Conclusion A fusion model integrating 2D and 2.5D DTL and radiomic features effectively predicted the neoadjuvant chemoimmunotherapy response in patients with ESCC. Keywords: Deep Learning, Artificial Intelligence, Prognosis & Prediction, Esophagus, CT Supplemental material is available for this article. © RSNA 2026.