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 …

Purpose To determine whether stacked deep learning models based on PET/CT images
and clinical data can help to predict epidermal growth factor receptor (EGFR) mutations in …

Background To introduce a three-dimensional convolutional neural network (3D CNN)
leveraging transfer learning for fusing PET/CT images and clinical data to predict EGFR …

Background Predicting the mutation status of the epidermal growth factor receptor (EGFR)
gene based on an integrated positron emission tomography/computed tomography …

Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such
as the use of tyrosine kinase inhibitors in lung adenocarcinoma. Conventional identification …

Purpose: This study aimed to compare the performance of radiomics and deep learning in
predicting EGFR mutation status in patients with lung cancer based on PET/CT images, and …

Background To investigate whether radiomic features from (18 F)-fluorodeoxyglucose
positron emission tomography/computed tomography [(18 F)-FDG PET/CT] can predict …

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 …

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

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 …