Improvement of machine learning models for predicting high-grade subtypes of lung adenocarcinoma based on delta radiomics: A multicenter cohort study.

ABSTRACT:

Objectives

To evaluate the effectiveness of delta radiomics in predicting high-grade components in lung adenocarcinoma and to develop a robust machine learning model for clinical application.

Methods

This retrospective multi-center cohort study included lung cancer patients from three hospitals who had pre-surgery CT follow-up scans. Training (n = 491) and validation (n = 210) were performed using cases from Center 1, and testing was conducted using cases from Centers 2 and 3 (n = 92). Radiomic features were extracted from baseline and follow-up CT images, and delta radiomic features were calculated. The LASSO algorithm was used for radiomic feature selection, and rad-score and delta rad-score were constructed. Significant clinical and radiomic features were combined to buil

SUBMITTER: Zhong F 

PROVIDER: S-EPMC12617617 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature