Contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics for identifying malignancy in pulmonary ground-glass nodules.

ABSTRACT:

Background

Lung cancer remains the leading cause of cancer-related deaths worldwide. Early-stage lung cancer often presents as ground-glass nodules (GGNs) on computed tomography (CT). However, reliably distinguishing benign from malignant GGNs using conventional morphological features on CT remains a significant challenge, which impedes the accuracy of clinical surgical decision-making. Against this backdrop, radiomics, a technique that extracts quantitative features from medical images, offers a promising solution. This study aimed to investigate the predictive value of machine learning models based on radiomic features from intratumoral and peritumoral regions on contrast-enhanced CT in differentiating benign and malignant GGNs preoperatively.

Methods

This retrospective st

SUBMITTER: Peng Z 

PROVIDER: S-EPMC12688521 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature