Peer-reviewed veterinary case report
Early detection of dog mammary tumors using blood antibody tests
By Lan, Bluest et al.·Published in The veterinary quarterly·2026·Department of Mechanical Engineering·View original on PubMed →
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Original publication title: Machine learning-assisted detection of canine mammary tumors using serum autoantibody signatures.
- Species:
- dog
Plain-English summary
A study found that a new blood test could help detect mammary tumors in female dogs, which are the most common type of tumor in this group. Researchers analyzed blood samples from 154 dogs with either benign or malignant tumors and compared them to healthy dogs. They developed a machine learning model that looks for specific autoantibodies in the blood, achieving a good accuracy rate for identifying tumors. While this test shows promise, further research is needed to confirm its effectiveness in more dogs.
People also search for: dog mammary tumor detection · blood test for dog tumors · signs of mammary tumors in dogs
Abstract
Canine mammary tumors (CMTs) are the most common neoplasms in intact female dogs, yet early detection remains challenging due to the lack of clinically validated, noninvasive biomarkers. This study aimed to develop a noninvasive diagnostic model for CMT detection by integrating serum autoantibody biomarkers with machine learning. Serum samples from 154 dogs with mammary tumors (31 benign, 123 malignant) and 39 healthy controls were analyzed using a custom multiplex immunoassay detecting autoantibodies against AGR2, HAPLN1, IGFBP5, and TYMS, normalized to anti-BSA levels. Median fluorescence intensity (MFI), standardized autoantibody ratios, and their combination, together with clinical variables, were used to train random forest classifiers. The model based on standardized autoantibody ratios achieved the best performance, with an AUC of 0.79 (sensitivity 75.3%, specificity 74.4%) for overall CMT detection; 0.78 (92.7%, 61.5%) for malignant CMTs; and 0.77 (82.2%, 71.8%) for early-stagemalignancies. Assuming a CMT prevalence of 0.5 in the hospital-referred population, the positive and negative predictive values ranged from 0.74-0.75 and 0.75-0.91, respectively. This proof-of-concept study demonstrates that a machine learning-assisted multiplex autoantibody assay offers a feasible noninvasive approach for CMT detection. Further validation in larger, independent cohorts is warranted to support clinical translation in veterinary oncology.
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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/41562247/