Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground-Glass Nodules Based on AI-Extracted Radiomic Features.

ABSTRACT:

Background

With the widespread adoption of low-dose CT screening, the detection of pulmonary ground-glass nodules (GGNs) has risen markedly, presenting diagnostic challenges in distinguishing preinvasive lesions from invasive adenocarcinomas (IAC). This study aimed to develop a machine learning (ML)-based model using artificial intelligence (AI)-extracted CT radiomic features to predict the invasiveness of GGNs.

Methods

A retrospective cohort of 285 patients (148 with preinvasive lesions, 137 with IAC) from the Lingnan Campus was divided into training and internal validation sets (8:2). An independent cohort of 210 patients (118 with preinvasive lesions, 92 with IAC) from the Tianhe Campus served as external validation. Nineteen radiomic features were extracted and filtered

SUBMITTER: Yang G 

PROVIDER: S-EPMC12313823 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature