Dataset on Bi- and Multi-Nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors
This dataset and benchmark provide a resource for developing automated image analysis tools to standardize histologic tumor prognostication for veterinary pathologists working with canine cutaneous mast cell tumors.
The authors created the first open-source dataset containing 19,983 annotations of binucleated cells and 1,416 annotations of multinucleated cells in 32 histological whole slide images of canine cutaneous mast cell tumors. A deep learning model achieved an F1 score of 0.675 for binucleated cells and 0.623 for multinucleated cells on test images, outperforming human pathologists whose F1 scores ranged from 0.270-0.526 for binucleated cells and 0.316-0.622 for multinucleated cells.
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 $F_1$ score of 0.675 for BiNC and 0.623 for MuNC on 11 test whole slide images. In regions of interest ($2.37 mm^2$) 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 $F_1$ 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.