Peer-reviewed veterinary case report
How to tell benign from malignant perianal gland lumps in dogs
By Sabattini, S et al.·Published in The Journal of small animal practice·2019·Department of Veterinary Medical Sciences, Italy·View original on PubMed →
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Original publication title: Cytological differentiation between benign and malignant perianal gland proliferative lesions in dogs: a preliminary study.
- Species:
- dog
Plain-English summary
A group of dogs with lumps near their rear end were examined to determine if these growths were benign (non-cancerous) or malignant (cancerous). The study looked at samples from 56 dogs and found that most of the lumps were adenomas (a type of benign tumor), while some were carcinomas (cancer). Researchers identified specific criteria that could help vets accurately tell the difference between the two types of growths, achieving about 87% accuracy. This information could help guide treatment decisions before surgery.
People also search for: dog perianal gland lump · how to tell if a dog tumor is cancerous · dog rear end growth treatment
Abstract
OBJECTIVES: To evaluate the diagnostic utility of individual cytological criteria and their best combination to differentiate benign from malignant perianal gland proliferative lesions in dogs. MATERIALS AND METHODS: Retrospective study of cytological samples of canine perianal gland proliferative lesions that had subsequent histopathological confirmation. RESULTS: Seventy-seven perianal gland nodules from 56 dogs were included. Histologically, lesions were diagnosed as hyperplasia (n=2), adenoma (n=53), epithelioma (n=6) and carcinoma (n=16). Of the 28 cytological criteria assessed, 13 showed promise for distinguishing benign from malignant lesions. A diagnostic algorithm with an 87% accuracy (sensitivity, 90.9%; specificity, 85.4%) was developed from these data. CLINICAL SIGNIFICANCE: Cytological evaluation can provide useful information for presurgical differentiation between benign and malignant hepatoid gland proliferative lesions. The proposed algorithm must be validated and tested for reproducibility in further, preferably larger, series of cases.
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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/31368201/