Development of a novel nomogram-based model incorporating 3D radiomic signatures and lung CT radiological features for differentiating invasive adenocarcinoma from adenocarcinoma in situ and minimally invasive adenocarcinoma.

ABSTRACT:

Background

Lung cancer is one of the most serious cancers in the world. Subtypes of lung adenocarcinoma can be quickly distinguished by analyzing 3D radiomic signatures and radiological features.

Methods

This study included 493 patients from 3 hospitals with a total of 506 lesions confirmed as minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), or invasive adenocarcinoma (IAC). After segmenting the lesion area, 3D radiomic signatures were extracted using the PyRadiomics package v. 3.0.1 implemented in Python (https://pyradiomics.readthedocs.io/en/latest/index.html), and the corresponding radiological features were collected. Subsequently, the top 100 features were identified by feature screening methods, including the Spearman rank correlation and minimum

SUBMITTER: Ren H 

PROVIDER: S-EPMC9816727 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature