Peer-reviewed veterinary case report
Detecting mast cell tumor spread in dog lymph nodes with flow
By Iamone, Giulia et al.·Published in Veterinary and comparative oncology·2025·Department of Veterinary Sciences, Italy·View original on PubMed →
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Original publication title: Flow Cytometry for the Detection and Quantification of Mast Cells in Lymph Nodes: A Prospective Study in 64 Dogs With Mast Cell Tumour.
- Species:
- dog
Plain-English summary
A study looked at 64 dogs with mast cell tumors (MCTs) to find better ways to detect if their lymph nodes (LNs) had cancer spread. The researchers used different methods, including flow cytometry, to identify and count mast cells in the LNs. They found that combining flow cytometry with traditional cytology improved the accuracy of detecting metastatic lymph nodes to 92.2%. This means that using these methods together can help veterinarians make more informed decisions about treatment and prognosis for dogs with MCTs.
People also search for: dog mast cell tumor lymph nodes · how to detect cancer in dogs · mast cell tumor treatment options for dogs
Abstract
Nodal metastasis is a negative prognostic factor in dogs with mast cell tumours (MCTs), thus early detection enables more informed decision-making and provides valuable prognostic information. The aim of this study is to assess the concordance between histopathologic findings of LNs and cytology and flow cytometry (FC), respectively, and to evaluate the ability of FC to differentiate between metastatic (HN2-HN3) and non-metastatic (HN0-HN1) LNs. Overall, 117 LNs from 64 dogs with first occurring MCTs were submitted for cytology, histology and FC. LNs were cytologically and histologically classified according to Krick and Weishaar systems, respectively. Using FC, mast cells (MCs) were identified as IgE+ CD117+ CD5- CD21- cells and quantified as a percentage. When compared with histologic classification, cytology showed an accuracy of 88.2% in distinguishing between metastatic and non-metastatic LNs but did not detect 25.3% of metastatic cases. FC revealed an increase in the median percentages of MCs across histologic classes, progressing from HN0 to HN3. ROC curves pinpointed 0.3% as the optimal cut-off for distinguishing between metastatic and non-metastatic LNs, with an accuracy of 84.3%. A 1.1% cut-off proved valuable in identifying HN3 LNs. The combined interpretation of cytology and FC increased accuracy to 92.2%. An algorithm for guiding the combined interpretation of cytology and FC is suggested based on these findings. In conclusion, FC proves beneficial in enhancing the early detection of metastatic LNs, particularly when utilised alongside cytology. Histopathology remains essential for confirmation, enabling the discrimination of HN classes or, in doubtful cases, for the detection or exclusion of nodal metastases.
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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/39420569/