Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer.
PURPOSE
The prognostic value of mitotic count for invasive breast cancer is firmly established. As yet, however, limited studies have been aimed at assessing mitotic counts as a prognostic factor for triple negative breast cancers (TNBC). Here, we assessed the prognostic value of absolute mitotic counts for TNBC, using both deep learning and manual procedures.


METHODS
A retrospective TNBC cohort (n = 298) was used. The absolute manual mitotic count was assessed by averaging counts from three independent observers. Deep learning was performed using a convolutional neural network on digitized H&E slides. Multivariable Cox regression models for relapse-free survival and overall survival served as baseline models. These were expanded with dichotomized mitotic counts, attempting every possible cut-off value, and evaluated by means of the c-statistic.


RESULTS
We found that per 2 mm2 averaged manual mitotic counts ranged from 1 to 187 (mean 37.6, SD 23.4), whereas automatic counts ranged from 1 to 269 (mean 57.6; SD 42.2). None of the cut-off values improved the models' baseline c-statistic, for both manual and automatic assessments.


CONCLUSIONS
Based on our results we conclude that the level of proliferation, as reflected by mitotic count, does not serve as a prognostic factor for TNBC. Therefore, TNBC patient management based on mitotic count should be discouraged.
['Balkenhol|Maschenka C A|MCA|http://orcid.org/0000-0001-9457-618X', 'Bult|Peter|P|', 'Tellez|David|D|', 'Vreuls|Willem|W|', 'Clahsen|Pieter C|PC|', 'Ciompi|Francesco|F|', 'van der Laak|Jeroen A W M|JAWM|']
D000465:Algorithms D000077321:Deep Learning D018572:Disease-Free Survival D005260:Female D006801:Humans D008297:Male D008875:Middle Aged D008938:Mitosis D015999:Multivariate Analysis D011379:Prognosis D016016:Proportional Hazards Models D064726:Triple Negative Breast Neoplasms
Mitotic count Triple negative breast cancer Artificial intelligence Prognosis