Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To evaluate a fully automatic deep learning system to detect and segment clinically significant prostate cancer (csPCa) on same-vendor prostate MRI from two different institutions not contributing to training of the system.

Authors

  • Nils Netzer
  • Carolin Eith
    Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
  • Oliver Bethge
    German Cancer Consortium, Heidelberg, Germany.
  • Thomas Hielscher
    Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Constantin Schwab
    Institute of Pathology, University of Heidelberg Medical Center.
  • Albrecht Stenzinger
    From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.).
  • Regula Gnirs
    From the Division of Radiology, German Cancer Research Center.
  • Heinz-Peter Schlemmer
    From the Department of Radiology (D.B., P.S., J.P.R., P.K., K.Y., M.F., H.P.S.), Division of Medical Image Computing (S.K., M.G., N.G., K.H.M.H.), Division of Statistics (M.W.), and Department of Medical Physics (T.A.K., F.D.), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany (D.B., H.P.S., K.H.M.H.); and Departments of Urology (J.P.R., B.H., M.H., B.A.H.) and Neuroradiology (P.K.), University of Heidelberg Medical Center, Heidelberg, Germany.
  • Klaus H Maier-Hein
    Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address: k.maier-hein@dkfz.de.
  • Lars Schimmöller
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany.
  • David Bonekamp
    From the Department of Radiology (D.B., P.S., J.P.R., P.K., K.Y., M.F., H.P.S.), Division of Medical Image Computing (S.K., M.G., N.G., K.H.M.H.), Division of Statistics (M.W.), and Department of Medical Physics (T.A.K., F.D.), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany (D.B., H.P.S., K.H.M.H.); and Departments of Urology (J.P.R., B.H., M.H., B.A.H.) and Neuroradiology (P.K.), University of Heidelberg Medical Center, Heidelberg, Germany.