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...

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書誌詳細

出版年:	npj Digital Medicine
主要な著者:	Changhee Lee, Alexander Light, Evgeny S. Saveliev, Mihaela van der Schaar, Vincent J. Gnanapragasam
フォーマット:	論文
言語:	英語
出版事項:	Nature Portfolio 2022-08-01
オンライン･アクセス:	https://doi.org/10.1038/s41746-022-00659-w