Cargando…
Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods
One of the most precise methods to detect prostate cancer is by evaluation of a stained biopsy by a pathologist under a microscope. Regions of the tissue are assessed and graded according to the observed histological pattern. However, this is not only laborious, but also relies on the experience of...
Autores principales: | Mokoatle, Mpho, Mapiye, Darlington, Marivate, Vukosi, Hayes, Vanessa M., Bornman, Riana |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182297/ https://www.ncbi.nlm.nih.gov/pubmed/35679280 http://dx.doi.org/10.1371/journal.pone.0267714 |
Ejemplares similares
-
A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application
por: Mokoatle, Mpho, et al.
Publicado: (2023) -
Unsupervised Anomaly Detection of Healthcare Providers Using Generative Adversarial Networks
por: Naidoo, Krishnan, et al.
Publicado: (2020) -
Pelvic nodal radiotherapy in Gleason grade group 5 prostate cancer
por: Cook, Kiri, et al.
Publicado: (2020) -
Narrative review of prostate cancer grading systems: will the Gleason scores be replaced by the Grade Groups?
por: Montironi, Rodolfo, et al.
Publicado: (2021) -
MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer
por: Qiao, Xiaofeng, et al.
Publicado: (2023)