An interpretable machine learning model for predicting visceral pleural invasion in cT1 lung adenocarcinoma based on habitat analysis.

ABSTRACT:

Background

Visceral pleural invasion (VPI) is a critical prognostic factor in early-stage lung adenocarcinoma, influencing T staging and treatment decisions, yet its preoperative assessment remains challenging due to reliance on postoperative pathological confirmation. This study evaluates interpretable machine learning models to predict VPI in solid cT1 pulmonary nodules using preoperative computed tomography (CT)-based habitat analysis, offering a non-invasive tool to optimize surgical planning.

Methods

This retrospective study analyzed data of 802 patient with invasive pulmonary adenocarcinoma and solid lung nodules ≤3 cm from two centers and a public database. CT scans were preprocessed and regions were delineated for feature extraction. Habitat analysis using K-means cl

SUBMITTER: Gan X 

PROVIDER: S-EPMC12682543 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature