Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer
Abstract 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 dev...
Збережено в:
| Автори: | , , , , |
|---|---|
| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Nature Portfolio
2022-08-01
|
| Серія: | npj Digital Medicine |
| Онлайн доступ: | https://doi.org/10.1038/s41746-022-00659-w |
| Теги: |
Немає тегів, Будьте першим, хто поставить тег для цього запису!
|