Preoperative CT-based Radiomics for Predicting Response to Neoadjuvant Chemoimmunotherapy in Esophageal Squamous Cell Carcinoma.

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 un

SUBMITTER: Chen D 

PROVIDER: S-EPMC12862479 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature