Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images.

ABSTRACT:

Background

Predicting the mutation status of the epidermal growth factor receptor (EGFR) gene based on an integrated positron emission tomography/computed tomography (PET/CT) image of non-small cell lung cancer (NSCLC) is a noninvasive, low-cost method which is valuable for targeted therapy. Although deep learning has been very successful in robotic vision, it is still challenging to predict gene mutations in PET/CT-derived studies because of the small amount of medical data and the different parameters of PET/CT devices.

Methods

We used the advanced EfficientNet-V2 model to predict the EGFR mutation based on fused PET/CT images. First, we extracted 3-dimensional (3D) pulmonary nodules from PET and CT as regions of interest (ROIs). We then fused each single PET and CT image.

SUBMITTER: Xiao Z 

PROVIDER: S-EPMC10006109 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature