Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer

Active Surveillance (AS) for prostate cancer is a management option that continually monitors early disease and considers intervention if progression occurs. A robust method to incorporate “live” updates of progression risk during follow-up has hitherto been lacking. To address this, we developed a...

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Detalles Bibliográficos
Autores principales: Lee, Changhee, Light, Alexander, Saveliev, Evgeny S., van der Schaar, Mihaela, Gnanapragasam, Vincent J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357044/
https://www.ncbi.nlm.nih.gov/pubmed/35933478
http://dx.doi.org/10.1038/s41746-022-00659-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357044/
https://www.ncbi.nlm.nih.gov/pubmed/35933478
http://dx.doi.org/10.1038/s41746-022-00659-w

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