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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References
Bertram CA, Donovan TA, Bartel A. Mitotic activity: a systematic literature review of the assessment methodology and prognostic value in canine tumors. Vet Pathol. 2024;61(5):752– 64.
Van Dooijeweert C, Van Diest P, Ellis I. Grading of invasive breast carcinoma: the way forward. Virchows Archiv. 2022;480(1):33–43.
Aubreville M, Stathonikos N, Donovan TA et al. Domain generalization across tumor types, laboratories, and species – insights from the 2022 edition of the Mitosis Domain Generalization Challenge. Med Image Anal. 2024;94:103155.
Veta M, Heng YJ, Stathonikos N et al. Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge. Med Image Anal. 2019;54:111–21.
Aubreville M, Stathonikos N, Bertram CAet al. Mitosis domain generalization in histopathology images – the MIDOG challenge. Med Image Anal. 2023;84:102699.
Bertram CA, Veta M, Marzahl C et al. Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels. Proc MICCAI. 2020:204–13.
Gisselsson D. Classification of chromosome segregation errors in cancer. Chromosoma. 2008;117(6):511–9.
Lashen A, Toss MS, AlsaleemMet al. The characteristics and clinical significance of atypical mitosis in breast cancer. Mod Pathol. 2022;35(10):1341–8.
Ohashi R, Namimatsu S, Sakatani T et al. Prognostic utility of atypical mitoses in patients with breast cancer: A comparative study with Ki67 and phosphohistone H3. J Surg Oncol. 2018;118(3):557–67.
Bertram CA, Bartel A, Donovan TA et al. Atypical mitotic figures are prognostically meaningful for canine cutaneous mast cell tumors. Vet Sci. 2023;11(1):5.
Matsuda Y, Yoshimura H, Ishiwata T et al. Mitotic index and multipolar mitosis in routine histologic sections as prognostic markers of pancreatic cancers: a clinicopathological study. Pancreatology. 2016;16(1):127–32.
Aubreville M, Ganz J, Ammeling J et al. Deep learning-based subtyping of atypical and normal mitoses using a hierarchical anchor-free object detector. Proc BVM. 2023:189–95.
Fick RR, Bertram C, Aubreville M. Improving CNN-based mitosis detection through rescanning annotated glass slides and atypical mitosis subtyping. Proc MIDL. 2024.
Donovan TA, Moore FM, Bertram CAet al. Mitotic figures—normal, atypical, and imposters: a guide to identification. Vet Pathol. 2021;58(2):243–57.
Travaglino A, Raffone A, Santoro A et al. Prognostic significance of atypical mitotic figures in smooth muscle tumors of uncertain malignant potential (STUMP) of the uterus and uterine adnexa. Apmis. 2021;129(4):165–9.
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© 2025 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Bertram, C.A. et al. (2025). Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br). In: Palm, C., et al. Bildverarbeitung für die Medizin 2025. BVM 2025. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-47422-5_25
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DOI: https://doi.org/10.1007/978-3-658-47422-5_25
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