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Tumor proliferation is an important biomarker indicative of the prognosis ofbreast cancer patients. Assessment of tumor proliferation in a clinical settingis highly subjective and labor-intensive task. Previous efforts to automatetumor proliferation assessment by image analysis only focused on mitosisdetection 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 scoregiven a WSI as input. To address this, we organized the TUmor ProliferationAssessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scoresfrom WSIs. The challenge dataset consisted of 500 training and 321 testingbreast cancer histopathology WSIs. In order to ensure fair and independentevaluation, only the ground truth for the training dataset was provided to thechallenge participants. The first task of the challenge was to predict mitoticscores, i.e., to reproduce the manual method of assessing tumor proliferationby a pathologist. The second task was to predict the gene expression basedPAM50 proliferation scores from the WSI. The best performing automatic methodfor the first task achieved a quadratic-weighted Cohen's kappa score of$\kappa$ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and theground truth. For the second task, the predictions of the top method had aSpearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with theground truth. This was the first study that investigated tumor proliferationassessment from WSIs. The achieved results are promising given the difficultyof the tasks and weakly-labelled nature of the ground truth. However, furtherresearch is needed to improve the practical utility of image analysis methodsfor this task.

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