Cargando…
Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision
Breast cancer is molecularly heterogeneous and categorized into four molecular subtypes: Luminal-A, Luminal-B, HER2-amplified and Triple-negative. In this study, we aimed to apply an ensemble decision approach to identify the ultrasound and clinical features related to the molecular subtypes. We col...
Autores principales: | Zhang, Lei, Li, Jing, Xiao, Yun, Cui, Hao, Du, Guoqing, Wang, Ying, Li, Ziyao, Wu, Tong, Li, Xia, Tian, Jiawei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457139/ https://www.ncbi.nlm.nih.gov/pubmed/26046791 http://dx.doi.org/10.1038/srep11085 |
Ejemplares similares
-
The Differences in Ultrasound and Clinicopathological Features between Basal-Like and Normal-Like Subtypes of Triple Negative Breast Cancer
por: Li, Ziyao, et al.
Publicado: (2015) -
Nomograms for Predicting Axillary Lymph Node Status Reconciled With Preoperative Breast Ultrasound Images
por: Liu, Dongmei, et al.
Publicado: (2021) -
Identification of Personalized Chemoresistance Genes in Subtypes of Basal-Like Breast Cancer Based on Functional Differences Using Pathway Analysis
por: Wu, Tong, et al.
Publicado: (2015) -
Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer
por: Guo, Xu, et al.
Publicado: (2020) -
Ensemble Feature Learning to Identify Risk Factors for Predicting Secondary Cancer
por: Ye, Xiucai, et al.
Publicado: (2019)