Background: Mitosis counting is an important part of breast cancer grading, yet known to suffer from observer variability. Advances in machine learning enable fully automated analysis of digitized glass slides. The present study evaluated automatic mitosis counting and demonstrated applicability on triple negative breast cancers (TNBC).
Material and Methods: In entire scanned H&E slides of 90 invasive breast tumours, a deep learning algorithm (DLA) fully automatically detected all mitoses and determined the hotspot (area with highest mitotic density). Subsequently, two independent observers assessed mitotic counts on glass slides according to routine practice, and in the computer-defined hotspot.
Next, automated mitotic counting was performed in our TNBC cohort (n = 597). Multivariable Cox regression survival models were expanded with dichotomized mitotic counts. The c-statistic was used to evaluate the additional prognostic value of every possible cut off value.
Results: Automatic counting showed excellent concordance with visual assessment in computer detected hotspots with intraclass correlation coefficients (ICC) of 0.895 (95% CI 0.845–0.930) and 0.888 (95% CI 0.783–0.936) for two observers, respectively. ICC of fully automated counting versus conventional glass slide assessment were 0.828 (95% CI 0.750–0.883 and 0.757 (95% CI 0.638–0.839), respectively.
In the TNBC cohort, automatic mitotic counts ranged from 1 to 269 (mean 57.6) in 2 mm2 hotspots. None of the cut off values improved the models’ baseline c-statistic.
Conclusion: Automatic mitosis counting is a promising complementary aid for mitoses assessment. Our method was capable of fully automatically locating the mitotic hotspot in tumours, and was capable of processing a large series of TNBC, showing that mitotic count was not prognostic for TNBC even when attempting alternative cut off points.