Cargando…
A prognostic system for epithelial ovarian carcinomas using machine learning
INTRODUCTION: Integrating additional factors into the International Federation of Gynecology and Obstetrics (FIGO) staging system is needed for accurate patient classification and survival prediction. In this study, we tested machine learning as a novel tool for incorporating additional prognostic p...
Autores principales: | Grimley, Philip M., Liu, Zhenqiu, Darcy, Kathleen M., Hueman, Matthew T., Wang, Huan, Sheng, Li, Henson, Donald E., Chen, Dechang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360140/ https://www.ncbi.nlm.nih.gov/pubmed/33665831 http://dx.doi.org/10.1111/aogs.14137 |
Ejemplares similares
-
Expanding TNM for lung cancer through machine learning
por: Hueman, Matthew, et al.
Publicado: (2021) -
Integrating additional factors into the TNM staging for cutaneous melanoma by machine learning
por: Yang, Charles Q., et al.
Publicado: (2021) -
Expanding the TNM for cancers of the colon and rectum using machine learning: a demonstration
por: Hueman, Matthew, et al.
Publicado: (2019) -
Creating Prognostic Systems for Well-Differentiated Thyroid Cancer Using Machine Learning
por: Yang, Charles Q., et al.
Publicado: (2019) -
Creating prognostic systems for cancer patients: A demonstration using breast cancer
por: Hueman, Mathew T., et al.
Publicado: (2018)