Predicting Tyrosine Kinase Inhibitor Treatment Response in Stage IV Lung Adenocarcinoma Patients With EGFR Mutation Using Model-Based Deep Transfer Learning

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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Bibliographic Details
Main Authors: Runping Hou, Xiaoyang Li, Junfeng Xiong, Tianle Shen, Wen Yu, Lawrence H. Schwartz, Binsheng Zhao, Jun Zhao, Xiaolong Fu
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-07-01
Series:Frontiers in Oncology
Subjects:
deep learning—convolutional neural networks
computed tomography
lung cancer
transfer learning
epidermal growth factor receptor mutation
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2021.679764/full

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https://www.frontiersin.org/articles/10.3389/fonc.2021.679764/full

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