Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.

Journal: Medical image analysis
Published Date: May 1, 2019

Abstract

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a real-world scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs. The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI. The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the ground truth. This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task.

Authors

  • Mitko Veta
    Medical Image Analysis Group, Eindhoven University of Technology, Eindhoven, the Netherlands.
  • Yujing J Heng
    Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Nikolas Stathonikos
    Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands.
  • Babak Ehteshami Bejnordi
    Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Francisco Beca
    BeckLab, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
  • Thomas Wollmann
    Biomedical Computer Vision Group, University of Heidelberg, BIOQUANT, IPMB and DKFZ, Heidelberg, Germany.
  • Karl Rohr
    Biomedical Computer Vision Group, University of Heidelberg, BIOQUANT, IPMB and DKFZ, Heidelberg, Germany.
  • Manan A Shah
    The Harker School, San Jose, USA.
  • Dayong Wang
    BeckLab, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
  • Mikael Rousson
    ContextVision AB, Linköping, Sweden.
  • Martin Hedlund
    ContextVision AB, Linköping, Sweden.
  • David Tellez
    Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Francesco Ciompi
    Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: francesco.ciompi@radboudumc.nl.
  • Erwan Zerhouni
    Foundations of Cognitive Computing, IBM Research Zürich, Rüschlikon, Switzerland.
  • David Lanyi
    Foundations of Cognitive Computing, IBM Research Zürich, Rüschlikon, Switzerland.
  • Matheus Viana
    Visual Analytics and Insights, IBM Research Brazil, São Paulo, Brazil.
  • Vassili Kovalev
    Biomedical Image Analysis Department, United Institute of Informatics Problems, Belarus National Academy of Sciences, Minsk, Belarus.
  • Vitali Liauchuk
    Biomedical Image Analysis Department, United Institute of Informatics Problems, Belarus National Academy of Sciences, Minsk, Belarus.
  • Hady Ahmady Phoulady
    Department of Computer Science, University of Southern Maine, Portland, ME, USA.
  • Talha Qaiser
    Tissue Image Analytics Lab, Department of Computer Science, University of Warwick, Coventry, United Kingdom.
  • Simon Graham
    Mathematics for Real World Systems Centre for Doctoral Training, University of Warwick, Coventry, CV4 7AL, UK; Department of Computer Science, University of Warwick, UK. Electronic address: s.graham.1@warwick.ac.uk.
  • Nasir Rajpoot
    Department of Computer Science, University of Warwick, Coventry, UK.
  • Erik Sjöblom
    Sectra, Teknikringen 20, 583 30, Linköping, SE, Sweden.
  • Jesper Molin
    Sectra AB, Teknikringen 20, 58330, Linköping, Sweden. Electronic address: Jesper.Molin@sectra.com.
  • Kyunghyun Paeng
    Lunit Inc., Seoul, South Korea.
  • Sangheum Hwang
    From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.).
  • Sunggyun Park
    From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.).
  • Zhipeng Jia
  • Eric I-Chao Chang
    Microsoft Research Asia, Beijing, China. eric.chang@microsoft.com.
  • Yan Xu
    Department of Nephrology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.
  • Andrew H Beck
  • Paul J van Diest
    Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands.
  • Josien P W Pluim
    Medical Image Analysis, Department of Biomedical Engineering, Eindhoven University of Technology, The Netherlands.

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