Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience.

Journal: European journal of radiology
Published Date: Aug 5, 2021

Abstract

PURPOSE: To compare the performance of lesion detection and Prostate Imaging-Reporting and Data System (PI-RADS) classification between a deep learning-based algorithm (DLA), clinical reports and radiologists with different levels of experience in prostate MRI.

Authors

  • Seo Yeon Youn
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Moon Hyung Choi
    Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Dong Hwan Kim
    Department of Physical Medicine and Rehabilitation, College of Medicine, Kyung Hee University, Seoul, Korea.
  • Young Joon Lee
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 1021 Seoul, Republic of Korea. Electronic address: yjleerad@catholic.ac.kr.
  • Henkjan Huisman
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Evan Johnson
    Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
  • Tobias Penzkofer
    Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
  • Ivan Shabunin
    Patero Clinic, Moscow, Russia. Electronic address: shabunin@pateroclinic.ru.
  • David Jean Winkel
    Department of Radiology, University Hospital of Basel, Basel, Switzerland. Electronic address: davidjean.winkel@usb.ch.
  • Pengyi Xing
    Department of Radiology, Changhai Hospital, Shanghai, China. Electronic address: 746992685@qq.com.
  • Dieter Szolar
    Diagnostikum Graz Süd-West, Graz, Austria. Electronic address: dieter.szolar@diagnostikum-graz.at.
  • Robert Grimm
    Computational Linguistics & Psycholinguistics Research Center, Department of Linguistics, University of Antwerp, Antwerp, Belgium.
  • Heinrich von Busch
    Digital Health, Siemens Healthineers, Erlangen, Germany.
  • Yohan Son
    Siemens Healthineers Ltd., Seoul, Republic of Korea. Electronic address: yohan.son@siemens-healthineers.com.
  • Bin Lou
    755 College Road East, Digital Technology and Innovation Division, Siemens Healthineers, Princeton, NJ, 08540.
  • Ali Kamen
    755 College Road East, Digital Technology and Innovation Division, Siemens Healthineers, Princeton, NJ, 08540.

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