TY - JOUR
AU - 
AB - cancers Review Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications 1 , 1 1 , 2 1 , 3 Jose M. Castillo T. *, Muhammad Arif , Wiro J. Niessen , Ivo G. Schoots 1 , 4 and Jifke F. Veenland Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; a.muhammad@erasmusmc.nl (M.A.); w.niessen@erasmusmc.nl (W.J.N.); i.schoots@erasmusmc.nl (I.G.S.); j.veenland@erasmusmc.nl (J.F.V.) Faculty of Applied Sciences, Delft University of Technology, 2600 AA Delft, The Netherlands Department of Radiology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands Department of Medical Informatics, Erasmus MC, 3015 GD Rotterdam, The Netherlands * Correspondence: j.castillotovar@erasmusmc.nl Received: 16 May 2020; Accepted: 14 June 2020; Published: 17 June 2020 Abstract: Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification, (ii) to investigate whether there exists a relation between the performance and the method or the MRI sequences used, (iii) to assess what study design factors a ect the performance 
TI - Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications
JF - Cancers
DO - 10.3390/cancers12061606
DA - 2020-06-17
UR - https://www.deepdyve.com/lp/unpaywall/automated-classification-of-significant-prostate-cancer-on-mri-a-LgippM5nEm
DP - DeepDyve
ER -