Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma

Frontiers in Oncology

Guotao Yin¹  Qian Su¹  Xiaofeng Li¹  Wengui Xu¹  Lei Zhu¹  Dong Dai¹  Yiwen Chen¹  Ziyang Wang¹  Yingchao Song² 

[1] Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin, China;School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China;

关键词: adenocarcinoma of lung;    fluorodeoxyglucose F18;    positron emission tomography computed tomography;    deep learning;    epidermal growth factor receptor;   

DOI  :  10.3389/fonc.2021.709137

来源: DOAJ

【 摘 要 】

ObjectiveThe purpose of this study was to develop a deep learning-based system to automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT).MethodsThree hundred and one lung adenocarcinoma patients with EGFR mutation status were enrolled in this study. Two deep learning models (SECT and SEPET) were developed with Squeeze-and-Excitation Residual Network (SE-ResNet) module for the prediction of EGFR mutation with CT and PET images, respectively. The deep learning models were trained with a training data set of 198 patients and tested with a testing data set of 103 patients. Stacked generalization was used to integrate the results of SECT and SEPET.ResultsThe AUCs of the SECT and SEPET were 0.72 (95% CI, 0.62–0.80) and 0.74 (95% CI, 0.65–0.82) in the testing data set, respectively. After integrating SECT and SEPET with stacked generalization, the AUC was further improved to 0.84 (95% CI, 0.75–0.90), significantly higher than SECT (p<0.05).ConclusionThe stacking model based on 18F-FDG PET/CT images is capable to predict EGFR mutation status of patients with lung adenocarcinoma automatically and non-invasively. The proposed model in this study showed the potential to help clinicians identify suitable advanced patients with lung adenocarcinoma for EGFR‐targeted therapy.

【 授权许可】

Unknown