Journal Article
. 2019 Mar; 54:111-121.
doi: 10.1016/j.media.2019.02.012.

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

Mitko Veta 1 Yujing J Heng 2 Nikolas Stathonikos 3 Babak Ehteshami Bejnordi 4 Francisco Beca 5 Thomas Wollmann 6 Karl Rohr 6 Manan A Shah 7 Dayong Wang 2 Mikael Rousson 8 Martin Hedlund 8 David Tellez 5 Francesco Ciompi 5 Erwan Zerhouni 9 David Lanyi 9 Matheus Viana 10 Vassili Kovalev 11 Vitali Liauchuk 11 Hady Ahmady Phoulady 12 Talha Qaiser 13 Simon Graham 13 Nasir Rajpoot 13 Erik Sjöblom 14 Jesper Molin 14 Kyunghyun Paeng 15 Sangheum Hwang 15 Sunggyun Park 15 Zhipeng Jia 16 Eric I-Chao Chang 17 Yan Xu 18 Andrew H Beck 2 Paul J van Diest 3 Josien P W Pluim 19 
Affiliations
  • PMID: 30861443
  •     20 citations

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.

Keywords: Breast cancer; Cancer prognostication; Deep learning; Tumor proliferation.

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