Abstract | OBJECTIVE: METHODS: Three 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. RESULTS: The 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). CONCLUSION: The 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.
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Authors | Guotao Yin, Ziyang Wang, Yingchao Song, Xiaofeng Li, Yiwen Chen, Lei Zhu, Qian Su, Dong Dai, Wengui Xu |
Journal | Frontiers in oncology
(Front Oncol)
Vol. 11
Pg. 709137
( 2021)
ISSN: 2234-943X [Print] Switzerland |
PMID | 34367993
(Publication Type: Journal Article)
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Copyright | Copyright © 2021 Yin, Wang, Song, Li, Chen, Zhu, Su, Dai and Xu. |