Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung cancer.

ABSTRACT:

Objectives

To extract intratumoral, peritumoral, and integrated intratumoral-peritumoral CT radiomic features, develop multi-source radiomic models using various machine learning algorithms to identify the optimal model, and integrate clinical factors to establish a nomogram for predicting the therapeutic response to induction therapy(IT) in locally advanced non-small cell lung cancer.

Methods

This study included 209 patients with locally advanced non-small cell lung cancer (LA-NSCLC) who received IT as the training cohort, and an external validation cohort comprising 50 patients from another center. Radiomic features were extracted from intratumoral, peritumoral, and integrated intratumoral-peritumoral regions by manually delineating the gross tumor volume (GTV) and an addi

SUBMITTER: Cai F 

PROVIDER: S-EPMC11907900 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature