Expanding inclusion criteria for active surveillance in intermediate-risk prostate cancer: a machine learning approach.

Michael BaboudjianAlberto BredaThierry RoumeguèreAlessandro UleriJean-Baptiste RocheAlae TouzaniVito LaceteraJean-Baptiste BeauvalRomain DiamandGuiseppe SimoneOlivier WindischDaniel BenamranAlexandre FourcadeGaelle FiardCamille Durand-LabrunieMathieu RoumiguiéFrancesco SanguedolceMarco OderdaEric BarretGaëlle FromontCharles DarianeAnne-Laure CharvetBastien Gondran-TellierCyrille BastideEric LechevallierJoan PalouAlain RuffionRoderick C N Van Der BerghAlexandre PeltierGuillaume Ploussard

Published in: World journal of urology (2023)

The newly developed model has the potential to expand the number of patients eligible for AS without compromising oncologic outcomes. Prospective validation is warranted.

Keyphrases

prostate cancer
machine learning
end stage renal disease
radical prostatectomy
newly diagnosed
ejection fraction
prognostic factors
peritoneal dialysis
artificial intelligence
deep learning
climate change
human health