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

Xiao, Zhenghui1,2; Cai, Haihua1; Wang, Yue3; Cui, Ruixue4; Huo, Li4; Lee, Elaine Yuen-Phin5; Liang, Ying6,7; Li, Xiaomeng8; Hu, Zhanli1; Chen, Long3; Zhang, Na1

2023-02-01

10.21037/qims-22-760

Quantitative Imaging in Medicine and Surgery   影响因子和分区

2223-4292

2223-4306

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.

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

SCI

英语

其他

Natural Science Foundation of Guangdong Province in China[2020A1515010733] ; National High-Level Hospital Clinical Research Funding[2022-PUMCH-B-070]

Radiology, Nuclear Medicine & Medical Imaging

Radiology, Nuclear Medicine & Medical Imaging

WOS:000927492100001

AME PUBLISHING COMPANY

2-s2.0-85149013817

Web of Science

1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Lauterbur Res Ctr Biomed Imaging, 1068 Xueyuan Ave, Shenzhen 518055, Peoples R China
2.Southern Univ Sci & Technol, Shenzhen, Peoples R China
3.Kunming Med Univ, Yunnan Canc Hosp, Dept PET, Canc Ctr Yunnan Prov,CT Ctr,Affiliated Hosp 3, 519 Kunzhou Rd, Kunming 650118, Peoples R China
4.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Ctr Rare Dis Res, Beijing Key Lab Mol Targeted Diag & Therapy Nucl M, Beijing, Peoples R China
5.Univ Hong Kong, Li Ka Shing Fac Med, Clin Sch Med, Dept Diagnost Radiol, Hong Kong, Peoples R China
6.Chinese Acad Med Sci & Peking Union Med Coll, Canc Hosp, Natl Canc Ctr, Natl Clin Res Ctr Canc,Dept Nucl Med, Shenzhen, Peoples R China
7.Chinese Acad Med Sci & Peking Union Med Coll, Shenzhen Hosp, Shenzhen, Peoples R China
8.Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Peoples R China

Xiao, Zhenghui,Cai, Haihua,Wang, Yue,et al. Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images[J]. Quantitative Imaging in Medicine and Surgery,2023,13(3):1286-1299.

Xiao, Zhenghui.,Cai, Haihua.,Wang, Yue.,Cui, Ruixue.,Huo, Li.,...&Zhang, Na.(2023).Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images.Quantitative Imaging in Medicine and Surgery,13(3),1286-1299.

Xiao, Zhenghui,et al."Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images".Quantitative Imaging in Medicine and Surgery 13.3(2023):1286-1299.