A novel cross-validated machine learning based Alertix-Cancer Risk Index for early detection of canine malignancies.

INTRODUCTION

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 = 24, B-cell lymphoma = 29, histiocytic sarcoma = 47, hemangiosarcoma = 26, osteosarcoma = 26, mastocytoma = 40, and mammary tumors = 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.

RESULTS

Both the TK1-ELISA and cCRP levels were significantly higher in the tumor group compared to healthy controls ( < 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%.

CONCLUSION

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.