Intratumoral Heterogeneity Scores as Predictors of Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules: Insights from Explainable Machine Learning-Based Ternary Classification Models

Intratumoral Heterogeneity Scores as Predictors of Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules: Insights from Explainable Machine Learning-Based Ternary Classification Models

Introduction Preoperative differentiation of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) using computed tomography (CT) is crucial for clinical management. However, accurately classifying pure ground-glass nodules (pGGNs) presents signific...

Full description

Saved in:
Bibliographic Details
Main Authors: Wang Peng BS, Wanyin Qi BS, Yunhua Li BS, Sanhong Zhang BS, Juan Long BS
Format: Article
Language:English
Published: SAGE Publishing 2025-08-01
Series:Technology in Cancer Research & Treatment
Online Access:https://doi.org/10.1177/15330338251365985
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1177/15330338251365985

Similar Items