Maschenka C A Balkenhol1, Peter Bult2, David Tellez2, Willem Vreuls3, Pieter C Clahsen4, Francesco Ciompi2, Jeroen A W M van der Laak2. 1. Department of Pathology, Radboud University Medical Center, PO Box 9100, 6500, HB, Nijmegen, the Netherlands. maschenka.balkenhol@radboudumc.nl. 2. Department of Pathology, Radboud University Medical Center, PO Box 9100, 6500, HB, Nijmegen, the Netherlands. 3. Department of Pathology, Canisius Wilhelmina Hospital, Nijmegen, the Netherlands. 4. Department of Pathology, Haaglanden Medical Center, 's-Gravenhage, the Netherlands.
Abstract
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.
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.
Entities:
Keywords:
Artificial intelligence; Mitotic count; Prognosis; Triple negative breast cancer
Authors: Tone Hoel Lende; Marie Austdal; Anne Elin Varhaugvik; Ivar Skaland; Einar Gudlaugsson; Jan Terje Kvaløy; Lars A Akslen; Håvard Søiland; Emiel A M Janssen; Jan P A Baak Journal: BMC Cancer Date: 2019-11-08 Impact factor: 4.430
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