Maschenka Balkenhol - Deep learning enables fully automated mitotic density assessment in breast cancer histopathology

Background & objectives

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).

Methods

A deep learning algorithm fully automatically detected mitoses in scanned H&E slides of 90 invasive breast tumours and determined the mitotic hotspot. Two independent observers assessed mitotic density on glass slides according to routine practice, and in the computer-defined hotspot. Automated mitotic counting was also performed in a TNBC cohort (n=597). Cox regression models were expanded with dichotomized mitotic counts, using the c-statistic 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, 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.