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Med Image Anal. 2019 Feb 27;54:111-121. doi: 10.1016/ [Epub ahead of print]

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

Author information

Medical Image Analysis Group, Eindhoven University of Technology, Eindhoven, the Netherlands. Electronic address:
Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands.
Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the Netherlands.
Department of Pathology, Stanford University School of Medicine, USA.
Biomedical Computer Vision Group, University of Heidelberg, BIOQUANT, IPMB and DKFZ, Heidelberg, Germany.
The Harker School, San Jose, USA.
ContextVision AB, Linköping, Sweden.
Foundations of Cognitive Computing, IBM Research Zürich, Rüschlikon, Switzerland.
Visual Analytics and Insights, IBM Research Brazil, São Paulo, Brazil.
Biomedical Image Analysis Department, United Institute of Informatics, Minsk, Belarus.
Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA.
Department of Computer Science, University of Warwick, Warwick, UK.
Research, Sectra, Linköping, Sweden.
Lunit Inc., Seoul, South Korea.
Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
Microsoft Research, Beijing, China.
Microsoft Research, Beijing, China; Biology and Medicine Engineering, Beihang University, Beijing, China.
Medical Image Analysis Group, Eindhoven University of Technology, Eindhoven, the Netherlands.


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.


Breast cancer; Cancer prognostication; Deep learning; Tumor proliferation

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