High spatiotemporal resolution dynamic contrast-enhanced MRI improves the image-based discrimination of histopathology risk groups of peripheral zone prostate cancer: a supervised machine learning approach.

Journal: European radiology
Published Date:

Abstract

OBJECTIVE: To assess if adding perfusion information from dynamic contrast-enhanced (DCE MRI) acquisition schemes with high spatiotemporal resolution to T2w/DWI sequences as input features for a gradient boosting machine (GBM) machine learning (ML) classifier could better classify prostate cancer (PCa) risk groups than T2w/DWI sequences alone.

Authors

  • David J Winkel
    From the Department of Radiology, University Hospital Basel, Basel, Switzerland.
  • Hanns-Christian Breit
    Department of Radiology, University Hospital of Basel, 4031, Basel-Stadt, Switzerland.
  • Tobias K Block
    Department of Radiology, University Hospital of Basel, 4031, Basel-Stadt, Switzerland.
  • Daniel T Boll
  • Tobias J Heye
    Department of Radiology, University Hospital of Basel, 4031, Basel-Stadt, Switzerland.