Cargando…
Deep learning-based survival prediction for multiple cancer types using histopathology images
Providing prognostic information at the time of cancer diagnosis has important implications for treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving risk stratification remains an...
Autores principales: | Wulczyn, Ellery, Steiner, David F., Xu, Zhaoyang, Sadhwani, Apaar, Wang, Hongwu, Flament-Auvigne, Isabelle, Mermel, Craig H., Chen, Po-Hsuan Cameron, Liu, Yun, Stumpe, Martin C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299324/ https://www.ncbi.nlm.nih.gov/pubmed/32555646 http://dx.doi.org/10.1371/journal.pone.0233678 |
Ejemplares similares
-
Interpretable survival prediction for colorectal cancer using deep learning
por: Wulczyn, Ellery, et al.
Publicado: (2021) -
Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images
por: Sadhwani, Apaar, et al.
Publicado: (2021) -
Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning
por: Krogue, Justin D., et al.
Publicado: (2023) -
Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading
por: Wulczyn, Ellery, et al.
Publicado: (2021) -
Deep learning models for histologic grading of breast cancer and association with disease prognosis
por: Jaroensri, Ronnachai, et al.
Publicado: (2022)