Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy.

ABSTRACT:

Purpose

To establish prediction models to predict 2-year overall survival (OS) and stratify patients with different risks based on radiomics features extracted from magnetic resonance imaging (MRI) and computed tomography (CT) before definite chemoradiotherapy (dCRT) in locally advanced esophageal squamous cell carcinoma (ESCC).

Methods

Patients with locally advanced ESCC were recruited. We extracted 547 radiomics features from MRI and CT images. The least absolute shrinkage and selection operator (LASSO) for COX algorithm was used to obtain features highly correlated with survival outcomes in the training cohort. Based on MRI, CT, and the hybrid image data, three prediction models were built. The predictive performance of the radiomics models was evaluated in the training c

SUBMITTER: Yu N 

PROVIDER: S-EPMC11967038 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature