Lung cancer, the most frequently diagnosed cancer worldwide, is the leading cause of
cancer-associated deaths. In recent years, significant progress has been achieved in basic …

Purpose To develop and interpret optimal predictive models to identify epidermal growth
factor receptor (EGFR) mutation status and subtypes in patients with lung adenocarcinoma …

Background Accurate, noninvasive, and reliable assessment of epidermal growth factor
receptor (EGFR) mutation status and EGFR molecular subtypes is essential for treatment …

Objective The purpose of this study was to develop a deep learning-based system to
automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma …

Background This study aims to construct radiomics models based on [18F] FDG PET/CT
using multiple machine learning methods to predict the EGFR mutation status of lung …

The earlier identification of EGFR mutation status in lung adenocarcinoma patients is crucial
for treatment decision-making. Radiomics, which involves high-throughput extraction of …

Objective This study aimed to evaluate the value of 18 F-FDG PET/CT radiomics in
predicting EGFR gene mutations in non-small cell lung cancer by meta-analysis. Methods …

Background This study aimed to develop a pipeline for selecting the best feature
engineering-based radiomic path to predict epidermal growth factor receptor (EGFR) mutant …

Objective To assess the performance of pretreatment 18F-fluorodeoxyglucose positron
emission tomography/computed tomography (18F-FDG PET/CT) radiomics features for …

Objectives To investigate the development and validation of a radiomics nomogram based
on PET/CT for guiding personalized targeted therapy in patients with lung adenocarcinoma …