Cargando…
Predicting Tyrosine Kinase Inhibitor Treatment Response in Stage IV Lung Adenocarcinoma Patients With EGFR Mutation Using Model-Based Deep Transfer Learning
BACKGROUND: For 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 r...
Autores principales: | Hou, Runping, Li, Xiaoyang, Xiong, Junfeng, Shen, Tianle, Yu, Wen, Schwartz, Lawrence H., Zhao, Binsheng, Zhao, Jun, Fu, Xiaolong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8329710/ https://www.ncbi.nlm.nih.gov/pubmed/34354943 http://dx.doi.org/10.3389/fonc.2021.679764 |
Ejemplares similares
-
Predicting Malignancy and Invasiveness of Pulmonary Subsolid Nodules on CT Images Using Deep Learning
por: Shen, Tianle, et al.
Publicado: (2021) -
Detailed Analysis and Radiomic Prediction of First Progression Sites of First-Line Targeted Therapy for EGFR-Mutant Lung Adenocarcinoma Patients With Systemic Metastasis
por: Li, Xiaoyang, et al.
Publicado: (2021) -
Uncommon EGFR mutations in lung adenocarcinoma: features and response to tyrosine kinase inhibitors
por: Brindel, Aurélien, et al.
Publicado: (2020) -
EGFR Mutation Status in Lung Adenocarcinoma-Associated Malignant Pleural Effusion and Efficacy of EGFR Tyrosine Kinase Inhibitors
por: Yang, Jiyoul, et al.
Publicado: (2018) -
Improved survival in patients with unresectable stage III EGFR
‐mutant adenocarcinoma with upfront EGFR‐tyrosine kinase inhibitors
por: Wang, Sheng‐Yuan, et al.
Publicado: (2021)