Abstract
Tumor cells with two nuclei (binucleated cells, BiNC) or more nuclei (multinucleated cells, MuNC) indicate an increased amount of cellular genetic material which is thought to facilitate oncogenesis, tumor progression and treatment resistance. In canine cutaneous mast cell tumors (ccMCT), binucleation and multinucleation are parameters used in cytologic and histologic grading schemes (respectively) which correlate with poor patient outcome. For this study, we created the first open source data-set with 19,983 annotations of BiNC and 1,416 annotations of MuNC in 32 histological whole slide images of ccMCT. Labels were created by a pathologist and an algorithmic-aided labeling approach with expert review of each generated candidate. A state-of-the-art deep learning-based model yielded an F1 score of 0.675 for BiNC and 0.623 for MuNC on 11 test whole slide images. In regions of interest (2:37mm2) extracted from these test images, 6 pathologists had an object detection performance between 0.270 - 0.526 for BiNC and 0.316 - 0.622 for MuNC, while our model archived an F1 score of 0.667 for BiNC and 0.685 for MuNC. This open dataset can facilitate development of automated image analysis for this task and may thereby help to promote standardization of this facet of histologic tumor prognostication.
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References
Kiupel M, Webster J, Bailey K, et al. Proposal of a 2-tier histologic grading system for canine cutaneous mast cell tumors to more accurately predict biological behavior. Vet Pathol. 2011;48(1):147–155.
Camus M, Priest H, Koehler J, et al. Cytologic criteria for mast cell tumor grading in dogs with evaluation of clinical outcome. Vet Pathol. 2016;53(6):1117–1123.
Amend SR, Torga G, Lin KC, et al. Polyploid giant cancer cells: unrecognized actuators of tumorigenesis, metastasis, and resistance. Prostate. 2019;79(13):1489–1497.
Chen J, Niu N, Zhang J, et al. Polyploid giant cancer cells (PGCCs): the evil roots of cancer. Curr cancer Drug Targets. 2019;19(5):360–367.
Aubreville M, Bertram CA, Marzahl C, et al. Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region. Sci Rep. 2020;10(16447):1–11.
Bertram CA, Aubreville M, Marzahl C, et al. A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor. Sci Data. 2019;6(1):1–9.
Aubreville M, Bertram C, Klopfleisch R, et al. SlideRunner. In: Bildverarbeitung für die Medizin 2018. Springer; 28. p. 309–314.
Lin TY, Goyal P, Girshick R, et al. Focal loss for dense object detection. Proc IEEE ICCV. 2017; p. 2980–2988.
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. Proc IEEE CVPR. 2016; p. 770–778.
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© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Bertram, C.A. et al. (2021). Dataset on Bi- and Multi-nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_33
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DOI: https://doi.org/10.1007/978-3-658-33198-6_33
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