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

Zhenghui Xiao; Haihua Cai; Yue Wang; Ruixue Cui; Li Huo; Elaine Yuen-Phin Lee; Ying Liang; Xiaomeng Li; Zhanli Hu; Long Chen; Na Zhang

doi:10.21037/qims-22-760

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

Quant Imaging Med Surg. 2023 Mar 1;13(3):1286-1299. doi: 10.21037/qims-22-760. Epub 2023 Feb 6.

Authors

Zhenghui Xiao^#^{1

2}, Haihua Cai^#¹, Yue Wang³, Ruixue Cui⁴, Li Huo⁴, Elaine Yuen-Phin Lee⁵, Ying Liang⁶, Xiaomeng Li⁷, Zhanli Hu¹, Long Chen³, Na Zhang¹

Affiliations

¹ Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
² Southern University of Science and Technology, Shenzhen, China.
³ Department of PET/CT Center, Cancer Center of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China.
⁴ Nuclear Medicine Department, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
⁵ Department of Diagnostic Radiology, Clinical School of Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China.
⁶ Department of Nuclear Medicine, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
⁷ Department of Electronic and Computer Engineering, the Hong Kong University of Science and Technology, Hong Kong, China.

^# Contributed equally.

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. The network model was used to predict the mutation status of lung nodules by the new data after fusion, and the model was weighted adaptively. The EfficientNet-V2 model used multiple channels to represent nodules comprehensively.

Results: We trained the EfficientNet-V2 model through our PET/CT fusion algorithm using a dataset of 150 patients. The prediction accuracy of EGFR and non-EGFR mutations was 86.25% in the training dataset, and the accuracy rate was 81.92% in the validation set.

Conclusions: Combined with experiments, the demonstrated PET/CT fusion algorithm outperformed radiomics methods in predicting EGFR and non-EGFR mutations in NSCLC.

Keywords: EfficientNet-V2 Model; epidermal growth factor receptor (EGFR); positron emission tomography/computed tomography (PET/CT).