Enhancing the prediction of the invasiveness of pulmonary adenocarcinomas presenting as pure ground-glass nodules: Integrating intratumor heterogeneity score with clinical-radiological features via machine learning in a multicenter study.

ABSTRACT:

Objective

The invasiveness of lung adenocarcinoma significantly impacts clinical decision-making. However, assessing this invasiveness preoperatively, especially when it manifests as pure ground-glass nodules (pGGN) on CT scans, poses challenges. This study aims to quantify intratumor heterogeneity (ITH) and determine whether the ITH score can enhance the accuracy of invasiveness predictions.

Methods

A total of 524 patients with lung adenocarcinomas presenting as pGGN were enrolled in the study, with 177 (33.78%) receiving a pathologic diagnosis of invasiveness. Four diagnostic approaches were developed to predict the invasiveness of lung adenocarcinoma presenting as pGGN: (1) conventional lesion size, (2) ITH score, (3) clinical-radiological features (ClinRad), and (4) inte

SUBMITTER: Zheng H 

PROVIDER: S-EPMC11459516 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature