Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization.

Journal: PloS one
Published Date: Jul 8, 2019

Abstract

PURPOSE: To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2).

Authors

  • Jussi Toivonen
    Dept. of Diagnostic Radiology, University of Turku, Turku, Finland.
  • Ileana Montoya Perez
    Dept. of Diagnostic Radiology, University of Turku, Turku, Finland.
  • Parisa Movahedi
    Dept. of Diagnostic Radiology, University of Turku, Turku, Finland.
  • Harri Merisaari
    Dept. of Diagnostic Radiology, University of Turku, Turku, Finland.
  • Marko Pesola
    Dept. of Diagnostic Radiology, University of Turku, Turku, Finland.
  • Pekka Taimen
    Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland.
  • Peter J Boström
    Department of Urology, Turku University Hospital, Turku, Finland.
  • Jonne Pohjankukka
    Dept. of Future Technologies, University of Turku, Turku, Finland.
  • Aida Kiviniemi
    Dept. of Diagnostic Radiology, University of Turku, Turku, Finland.
  • Tapio Pahikkala
    University of Turku, Turun Yliopisto, Turku, Finland. Electronic address: tapio.pahikkala@utu.fi.
  • Hannu J Aronen
    Department of Diagnostic Radiology, University of Turku, Turku, Finland.
  • Ivan Jambor
    Department of Diagnostic Radiology, University of Turku, Turku, Finland.

Keywords

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