The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer.

ABSTRACT:

Purpose

Machine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[¹⁸F]fluoro-D-glucose ([¹⁸F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features.

Methods

One hundred and twenty non-small cell lung cancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters-four clinical factors, four laboratory indicators, and 91 [¹⁸F]F-FDG PET/CT radiomic features-were used for data analysis and model construction. The Boruta algorithm was used to screen the features. The

SUBMITTER: Zhao H 

PROVIDER: S-EPMC9177952 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature