Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)

• URL: http://arxiv.org/abs/2501.04467v2
• Date: Thu, 13 Mar 2025 07:10:26 GMT
• Title: Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)
• Authors: Christof A. Bertram, Viktoria Weiss, Taryn A. Donovan, Sweta Banerjee, Thomas Conrad, Jonas Ammeling, Robert Klopfleisch, Christopher Kaltenecker, Marc Aubreville,
• Abstract summary: Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types.<n>Recently, it has been reported in multiple works that the quantity of MFs with an atypical morphology might be an independent prognostic criterion for breast cancer.<n>We present the first ever publicly available dataset of atypical and normal MFs (AMi-Br)
• Score: 0.2786153781225932
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has been reported in multiple works that the quantity of MFs with an atypical morphology (atypical MFs, AMFs) might be an independent prognostic criterion for breast cancer. AMFs are an indicator of mutations in the genes regulating the cell cycle and can lead to aberrant chromosome constitution (aneuploidy) of the tumor cells. To facilitate further research on this topic using pattern recognition, we present the first ever publicly available dataset of atypical and normal MFs (AMi-Br). For this, we utilized two of the most popular MF datasets (MIDOG 2021 and TUPAC) and subclassified all MFs using a three expert majority vote. Our final dataset consists of 3,720 MFs, split into 832 AMFs (22.4%) and 2,888 normal MFs (77.6%) across all 223 tumor cases in the combined set. We provide baseline classification experiments to investigate the consistency of the dataset, using a Monte Carlo cross-validation and different strategies to combat class imbalance. We found an averaged balanced accuracy of up to 0.806 when using a patch-level data set split, and up to 0.713 when using a patient-level split.

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