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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2022
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| Materias: | |
| 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 |