Cargando…
Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography
OBJECTIVES: The subtype classification of lung adenocarcinoma is important for treatment decision. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung adenocarcinoma diagnosed through computed tomography (CT)...
Autores principales: | Wang, Chengdi, Shao, Jun, Lv, Junwei, Cao, Yidi, Zhu, Chaonan, Li, Jingwei, Shen, Wei, Shi, Lei, Liu, Dan, Li, Weimin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Neoplasia Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184655/ https://www.ncbi.nlm.nih.gov/pubmed/34087705 http://dx.doi.org/10.1016/j.tranon.2021.101141 |
Ejemplares similares
-
Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images
por: Wang, Chengdi, et al.
Publicado: (2021) -
Non-Invasive Measurement Using Deep Learning Algorithm Based on Multi-Source Features Fusion to Predict PD-L1 Expression and Survival in NSCLC
por: Wang, Chengdi, et al.
Publicado: (2022) -
The number of brain metastases predicts the survival of non‐small cell lung cancer patients with EGFR mutation status
por: Shao, Jun, et al.
Publicado: (2021) -
Clinicopathological variables influencing overall survival, recurrence and post-recurrence survival in resected stage I non-small-cell lung cancer
por: Wang, Chengdi, et al.
Publicado: (2020) -
The epidemiology and therapeutic options for the COVID-19
por: Li, Jingwei, et al.
Publicado: (2020)