Dataset on Bi- and Multi-Nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors

TL;DR

This paper introduces the first open-source dataset of binucleated and multinucleated tumor cells in canine mast cell tumors, along with a deep learning model for their detection, aiming to improve tumor grading and prognosis.

Contribution

The study provides a large annotated dataset and a state-of-the-art detection model, advancing automated analysis of tumor cell nuclei in veterinary pathology.

Findings

Deep learning model achieved F1 scores of 0.675 (BiNC) and 0.623 (MuNC) on test images.

Model performance exceeded that of six pathologists in object detection within regions of interest.

Open dataset supports development of standardized automated tumor cell analysis methods.

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 $F_1$ 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.