  Tumor proliferation is an important biomarker indicative of the prognosis of
breast cancer patients. Assessment of tumor proliferation in a clinical setting
is 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
$\kappa$ = 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 study that investigated tumor proliferation
assessment from WSIs. The achieved results are promising given the difficulty
of the tasks and weakly-labelled nature of the ground truth. However, further
research is needed to improve the practical utility of image analysis methods
for this task.
