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ORIGINAL RESEARCH article
Front. Vet. Sci.
Sec. Oncology in Veterinary Medicine
Volume 12 - 2025 | doi: 10.3389/fvets.2025.1570106
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The demand for non-invasive tumor biomarkers in veterinary field has recently grown significantly. Thymidine Kinase 1 (TK1) is one of the non-invasive proliferation biomarkers that has been used for diagnosis and treatment monitoring of different canine malignancies. However, recent studies showed that the combination of TK1 with inflammatory biomarkers such as canine C-reactive protein (cCRP) can enhance the sensitivity for early tumor detection. Herein, we developed a machine learning (ML) model i.e Alertix Cancer Risk Index (Alertix-CRI) which incorporates canine TK1 protein, CRP levels in conjunction with an age factor.Methods: A total of 287 serum samples were included in this study, consisting of 67 healthy dogs and dogs with different tumors (i.e., T cell lymphoma n=24, B cell lymphoma n=29, histiocytic sarcoma n=47, hemangiosarcoma n=26, osteosarcoma n=26, mastocytoma n=40, and mammary tumors n=28). Serum TK1 protein levels were measured using TK1-ELISA and cCRP levels by a quantitative ELISA. The whole data set was divided as training (70%) and validation (30%). The Alertix Cancer Risk Index (Alertix-CRI) is a Generalized boosted regression model (GBM) with high accuracy in the training set and further validation was carried out with the same model.Both the TK1-ELISA and cCRP levels were significantly higher in the tumor group compared to healthy controls (P<0.0001). For overall tumors, the ROC curve analysis showed that TK1-ELISA has similar sensitivity as cCRP (54% vs 51%) at a specificity of 95%. However, the Alertix-CRI for all malignancies showed an area under the curve (AUC) of 0.98, demonstrating very high discriminatory capacity, with a sensitivity of 90% and a specificity of 97%.These results demonstrate that the novel Alertix-CRI could be used as a decision-support tool helping clinicians to early differentiate dogs with malignant diseases from healthy. Additionally, these findings would facilitate the advancement of more precise and dependable diagnostic tools for early cancer detection and therapy monitoring within the realm of veterinary medicine.
Keywords: canine TK1 ELISA, solid tumors, Monoclonal antibody, serum TK1 concentration, CCRP, Canine lymphoma, Gradient boosting algorithm, machine learning models
Received: 02 Feb 2025; Accepted: 03 Apr 2025.
Copyright: © 2025 Sharif, Belaghi, Jagarlamudi, Saellström, Wang, Rönnberg and Eriksson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Henrik Rönnberg, Swedish University of Agricultural Sciences, Uppsala, Sweden
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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