Multimodal CT and MRI Radiomics Integrated with Clinical Models Predict Pathological Complete Response in ESCC Following Neoadjuvant Immunochemotherapy.

ABSTRACT:

Background

This research focused on evaluating the utility of multimodal radiomics integrated with machine learning to predict pathological complete response (pCR) in a prospective cohort of esophageal squamous cell carcinoma (ESCC) patients undergoing neoadjuvant immunochemotherapy (nICT).

Methods

We retrospectively analyzed prospectively collected trial data from 66 ESCC patients. Radiomic features were extracted from computed tomography (CT) and magnetic resonance imaging (MRI) images. Four machine learning algorithms-Random Forest (RF), logistic regression, Support Vector Machine, and Extreme Gradient Boosting (XGBoost)-were applied with leave-one-out cross-validation to predict pCR after nICT. The predictive performance of the models was evaluated using receiver operati

SUBMITTER: Liu L 

PROVIDER: S-EPMC12656131 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature