Risk factors for misclassification in predicting EGFR mutation status using PET/CT imaging in non-small cell lung cancer patients.

ABSTRACT:

Objective

This study aims to develop 10 machine learning models based on positron emission tomography/computed tomography (PET/CT) radiomic features to predict epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) patients and to identify risk factors contributing to model misclassification.

Methods

This study included 277 NSCLC patients from Zhongnan Hospital, Wuhan University, who underwent pretreatment ¹⁸F-FDG PET/CT and EGFR mutation testing. A PET/CT signature (PCS)-nomogram was developed by comparing 10 machine learning algorithms for EGFR prediction. Leave-one-out cross-validation generated model-specific EGFR mutation probabilities for individual patients, and performance disparities were analyzed across clinical subgroups

SUBMITTER: Li J 

PROVIDER: S-EPMC12708245 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature