Ultrasonographic Algorithm for the Assessment of Sentinel Lymph Nodes That Drain the Mammary Carcinomas in Female Dogs

SIMPLE SUMMARY: Mammary neoplasms are one of the most common oncological diseases diagnosed in female dogs. Their staging also involves the evaluation of the sentinel lymph nodes that drain these tumors. Since non-invasive diagnosis is an important requirement in both human and veterinary medicine, by using simple and available ultrasound techniques, our study proposes a non-invasive assessment of the status of sentinel lymph nodes of the tumoral mammary glands. The algorithm consists of B-mode ultrasound, Doppler technique, contrast-enhanced ultrasound (CEUS), and real-time elastography. Diagnostic performance was established for each technique. The highest accuracy in identifying metastases in sentinel lymph nodes was given by the elasticity score followed by the short/long-axis ratio and resistivity index. CEUS had the same accuracy as the Doppler examination. By assigning a score to each parameter of the mentioned techniques and summing up these scores, we obtained a high accuracy of metastasis detection in sentinel lymph nodes (92.2%). This algorithm for examining the status of sentinel lymph nodes that uses simple and widely available techniques in the current practice can be extremely useful to practitioners for staging mammary malignant tumors in female dogs, leading to the right treatment decision. ABSTRACT: The status of sentinel lymph nodes (SLNs) is decisive in staging, prognosis, and therapeutic approach. Using an ultrasonographic examination algorithm composed of B-mode, Doppler technique, contrast-enhanced ultrasound (CEUS) and elastography, this study aimed to determine the diagnostic performance of the four techniques compared to histopathological examination. 96 SLNs belonging to 71 female dogs with mammary gland carcinomas were examined. After examinations, mastectomy and lymphadenectomy were performed. Histopathological examination confirmed the presence of metastases in 54 SLNs. The elasticity score had the highest accuracy—89.71%, identifying metastases in SLNs with 88.9.9% sensitivity (SE) and 90.5% specificity (SP), ROC analysis providing excellent results. The S/L (short axis/long axis) ratio showed 83.3% SE and 78.6% SP as a predictor of the presence of metastases in SLN having a good accuracy of 81.2%. On Doppler examination, the resistivity index(RI) showed good accuracy of 80% in characterizing lymph nodes with metastases versus unaffected ones; the same results being obtained by CEUS examination. By assigning to each ultrasonographic parameter a score (0 or 1) and summing up the scores of the four techniques, we obtained the best diagnostic performance in identifying lymph node metastases with 92.2% accuracy. In conclusion, the use of the presented algorithm provides the best identification of metastases in SLNs, helping in mammary carcinoma staging and appropriate therapeutic management.