Application of hierarchical clustering to multi-parametric MR in prostate: Differentiation of tumor and normal tissue with high accuracy.

Journal: Magnetic resonance imaging
Published Date: Sep 12, 2020

Abstract

PURPOSE: Hierarchical clustering (HC), an unsupervised machine learning (ML) technique, was applied to multi-parametric MR (mp-MR) for prostate cancer (PCa). The aim of this study is to demonstrate HC can diagnose PCa in a straightforward interpretable way, in contrast to deep learning (DL) techniques.

Authors

  • Yuta Akamine
    Philips Japan, Konan 2-13-37, Minato-ku, Tokyo 108-8507, Japan. Electronic address: yuta.akamine@philips.com.
  • Yu Ueda
    Philips Japan, Konan 2-13-37, Minato-ku, Tokyo 108-8507, Japan.
  • Yoshiko Ueno
    Department of Radiology, Royal Victoria Hospital, McGill University Health Centre, 1001 Decarie Blvd, Montreal, QC, H4A 3J1, Canada.
  • Keitaro Sofue
    Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe City, Hyogo 650-0017, Japan.
  • Takamichi Murakami
    Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe City, Hyogo 650-0017, Japan.
  • Masami Yoneyama
    Philips Japan, Konan 2-13-37, Minato-ku, Tokyo 108-8507, Japan.
  • Makoto Obara
    Philips Japan, Konan 2-13-37, Minato-ku, Tokyo 108-8507, Japan.
  • Marc Van Cauteren
    Philips Healthcare BIU MR, Asia Pacific, Konan 2-13-37, Minato-ku, Tokyo 108-8507, Japan.

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