A computed tomography-based deep learning radiomics for predicting the response to neoadjuvant chemotherapy combined with immunotherapy in patients with locally advanced esophageal cancer: a multicenter cohort study.

ABSTRACT:

Background

Esophageal squamous cell carcinoma (ESCC) ranks sixth in global cancer mortality and is the main subtype in China; neoadjuvant chemoimmunotherapy for locally advanced ESCC has heterogeneous responses, and there is a lack of non-invasive tools to pretherapeutically identify major pathologic responders (MPRs), leading to unnecessary expenditures and adverse events. The present study seeks to develop a deep learning (DL)-based radiomic nomogram and to prospectively assess its clinical value in pretherapeutically identifying MPR in patients diagnosed with locally advanced ESCC who are scheduled to receive neoadjuvant chemoimmunotherapy. This approach facilitates the reduction of superfluous pharmaceutical expenditures and mitigates the risk of treatment-related adverse event

SUBMITTER: Ye M 

PROVIDER: S-EPMC12688518 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature