Application of machine learning based on habitat imaging and vision transformer to predict treatment response of locally advanced esophageal squamous cell carcinoma following neoadjuvant chemoimmunotherapy: a multi-center study.

ABSTRACT: Current medical examinations and biomarkers struggle to assess the efficacy of chemoimmunotherapy (nICT) for locally advanced esophageal squamous cell carcinoma (ESCC). This study aimed to develop a machine learning model integrating habitat imaging and deep learning (DL) to predict the treatment response of ESCC patients to nICT. The study retrospectively collected 309 ESCC patients from 6 medical centers, divided into training and external validation cohorts. For habitat imaging analysis, intratumoral subregions were clustered using the K-means clustering method. DL features from intratumoral and peritumoral subregions were extracted by Vision Transformer (ViT) respectively and then subjected to feature selection. Subsequently, 11 machine learning models were constructed for predictive m

SUBMITTER: Xie SH 

PROVIDER: S-EPMC12364654 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature