Predicting Tyrosine Kinase Inhibitor Treatment Response in Stage IV Lung Adenocarcinoma Patients With EGFR Mutation Using Model-Based Deep Transfer Learning
BackgroundFor stage IV patients harboring EGFR mutations, there is a differential response to the first-line TKI treatment. We constructed three-dimensional convolutional neural networks (CNN) with deep transfer learning to stratify patients into subgroups with different response and progression ris...
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doaj-cc14630bd30f42cabdd1cca52547c76e2021-07-20T12:31:46ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-07-011110.3389/fonc.2021.679764679764Predicting Tyrosine Kinase Inhibitor Treatment Response in Stage IV Lung Adenocarcinoma Patients With EGFR Mutation Using Model-Based Deep Transfer LearningRunping Hou0Runping Hou1Xiaoyang Li2Xiaoyang Li3Junfeng Xiong4Junfeng Xiong5Tianle Shen6Wen Yu7Lawrence H. Schwartz8Binsheng Zhao9Jun Zhao10Xiaolong Fu11School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, ChinaThe First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, ChinaSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaDivision of Health Care, Tencent, Shenzhen, ChinaDepartment of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Radiology, Columbia University Irving Medical Center, New York, NY, United StatesDepartment of Radiology, Columbia University Irving Medical Center, New York, NY, United StatesSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, ChinaBackgroundFor stage IV patients harboring EGFR mutations, there is a differential response to the first-line TKI treatment. We constructed three-dimensional convolutional neural networks (CNN) with deep transfer learning to stratify patients into subgroups with different response and progression risks.Materials and MethodsFrom 2013 to 2017, 339 patients with EGFR mutation receiving first-line TKI treatment were included. Progression-free survival (PFS) time and progression patterns were confirmed by routine follow-up and restaging examinations. Patients were divided into two subgroups according to the median PFS (<=9 months, > 9 months). We developed a PFS prediction model and a progression pattern classification model using transfer learning from a pre-trained EGFR mutation classification 3D CNN. Clinical features were fused with the 3D CNN to build the final hybrid prediction model. The performance was quantified using area under receiver operating characteristic curve (AUC), and model performance was compared by AUCs with Delong test.ResultsThe PFS prediction CNN showed an AUC of 0.744 (95% CI, 0.645–0.843) in the independent validation set and the hybrid model of CNNs and clinical features showed an AUC of 0.771 (95% CI, 0.676–0.866), which are significantly better than clinical features-based model (AUC, 0.624, P<0.01). The progression pattern prediction model showed an AUC of 0.762(95% CI, 0.643–0.882) and the hybrid model with clinical features showed an AUC of 0.794 (95% CI, 0.681–0.908), which can provide compensate information for clinical features-based model (AUC, 0.710; 95% CI, 0.582–0.839).ConclusionThe CNN exhibits potential ability to stratify progression status in patients with EGFR mutation treated with first-line TKI, which might help make clinical decisions.https://www.frontiersin.org/articles/10.3389/fonc.2021.679764/fulldeep learning—convolutional neural networkscomputed tomographylung cancertransfer learningepidermal growth factor receptor mutation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Runping Hou Runping Hou Xiaoyang Li Xiaoyang Li Junfeng Xiong Junfeng Xiong Tianle Shen Wen Yu Lawrence H. Schwartz Binsheng Zhao Jun Zhao Xiaolong Fu |
spellingShingle |
Runping Hou Runping Hou Xiaoyang Li Xiaoyang Li Junfeng Xiong Junfeng Xiong Tianle Shen Wen Yu Lawrence H. Schwartz Binsheng Zhao Jun Zhao Xiaolong Fu Predicting Tyrosine Kinase Inhibitor Treatment Response in Stage IV Lung Adenocarcinoma Patients With EGFR Mutation Using Model-Based Deep Transfer Learning Frontiers in Oncology deep learning—convolutional neural networks computed tomography lung cancer transfer learning epidermal growth factor receptor mutation |
author_facet |
Runping Hou Runping Hou Xiaoyang Li Xiaoyang Li Junfeng Xiong Junfeng Xiong Tianle Shen Wen Yu Lawrence H. Schwartz Binsheng Zhao Jun Zhao Xiaolong Fu |
author_sort |
Runping Hou |
title |
Predicting Tyrosine Kinase Inhibitor Treatment Response in Stage IV Lung Adenocarcinoma Patients With EGFR Mutation Using Model-Based Deep Transfer Learning |
title_short |
Predicting Tyrosine Kinase Inhibitor Treatment Response in Stage IV Lung Adenocarcinoma Patients With EGFR Mutation Using Model-Based Deep Transfer Learning |
title_full |
Predicting Tyrosine Kinase Inhibitor Treatment Response in Stage IV Lung Adenocarcinoma Patients With EGFR Mutation Using Model-Based Deep Transfer Learning |
title_fullStr |
Predicting Tyrosine Kinase Inhibitor Treatment Response in Stage IV Lung Adenocarcinoma Patients With EGFR Mutation Using Model-Based Deep Transfer Learning |
title_full_unstemmed |
Predicting Tyrosine Kinase Inhibitor Treatment Response in Stage IV Lung Adenocarcinoma Patients With EGFR Mutation Using Model-Based Deep Transfer Learning |
title_sort |
predicting tyrosine kinase inhibitor treatment response in stage iv lung adenocarcinoma patients with egfr mutation using model-based deep transfer learning |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Oncology |
issn |
2234-943X |
publishDate |
2021-07-01 |
description |
BackgroundFor stage IV patients harboring EGFR mutations, there is a differential response to the first-line TKI treatment. We constructed three-dimensional convolutional neural networks (CNN) with deep transfer learning to stratify patients into subgroups with different response and progression risks.Materials and MethodsFrom 2013 to 2017, 339 patients with EGFR mutation receiving first-line TKI treatment were included. Progression-free survival (PFS) time and progression patterns were confirmed by routine follow-up and restaging examinations. Patients were divided into two subgroups according to the median PFS (<=9 months, > 9 months). We developed a PFS prediction model and a progression pattern classification model using transfer learning from a pre-trained EGFR mutation classification 3D CNN. Clinical features were fused with the 3D CNN to build the final hybrid prediction model. The performance was quantified using area under receiver operating characteristic curve (AUC), and model performance was compared by AUCs with Delong test.ResultsThe PFS prediction CNN showed an AUC of 0.744 (95% CI, 0.645–0.843) in the independent validation set and the hybrid model of CNNs and clinical features showed an AUC of 0.771 (95% CI, 0.676–0.866), which are significantly better than clinical features-based model (AUC, 0.624, P<0.01). The progression pattern prediction model showed an AUC of 0.762(95% CI, 0.643–0.882) and the hybrid model with clinical features showed an AUC of 0.794 (95% CI, 0.681–0.908), which can provide compensate information for clinical features-based model (AUC, 0.710; 95% CI, 0.582–0.839).ConclusionThe CNN exhibits potential ability to stratify progression status in patients with EGFR mutation treated with first-line TKI, which might help make clinical decisions. |
topic |
deep learning—convolutional neural networks computed tomography lung cancer transfer learning epidermal growth factor receptor mutation |
url |
https://www.frontiersin.org/articles/10.3389/fonc.2021.679764/full |
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