Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer

Cell Oncol (Dordr). 2019 Aug;42(4):555-569. doi: 10.1007/s13402-019-00445-z. Epub 2019 Apr 15.


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.

Keywords: Artificial intelligence; Mitotic count; Prognosis; Triple negative breast cancer.

MeSH terms

  • Algorithms
  • Deep Learning*
  • Disease-Free Survival
  • Female
  • Humans
  • Male
  • Middle Aged
  • Mitosis*
  • Multivariate Analysis
  • Prognosis
  • Proportional Hazards Models
  • Triple Negative Breast Neoplasms / pathology*