Ultrasonographic Algorithm for the Assessment of Sentinel Lymph Nodes That Drain the Mammary Carcinomas in Female Dogs
Florin Stan,
Alexandru Gudea,
Aurel Damian,
Adrian Florin Gal,
Ionel Papuc,
Alexandru Raul Pop,
Cristian Martonos
Affiliations
Florin Stan
Department of Comparative Anatomy, Faculty of Veterinary Medicine, University of Agricultural Sciences and Veterinary Medicine, 3-5 Manastur Street, 400372 Cluj Napoca, Romania
Alexandru Gudea
Department of Comparative Anatomy, Faculty of Veterinary Medicine, University of Agricultural Sciences and Veterinary Medicine, 3-5 Manastur Street, 400372 Cluj Napoca, Romania
Aurel Damian
Department of Comparative Anatomy, Faculty of Veterinary Medicine, University of Agricultural Sciences and Veterinary Medicine, 3-5 Manastur Street, 400372 Cluj Napoca, Romania
Adrian Florin Gal
Department of Cell Biology, Histology and Embryology, Faculty of Veterinary Medicine, University of Agricultural Sciences and Veterinary Medicine, 3-5 Manastur Street, 400372 Cluj Napoca, Romania
Ionel Papuc
Department of Semiology and Medical Imaging, Faculty of Veterinary Medicine, University of Agricultural Sciences and Veterinary Medicine, 3-5 Manastur Street, 400372 Cluj Napoca, Romania
Alexandru Raul Pop
Department of Reproduction, Obstetrics and Reproductive Pathology, Biotechnologies in Reproduction, Faculty of Veterinary Medicine, University of Agricultural Sciences and Veterinary Medicine, 3-5 Manastur Street, 400372 Cluj Napoca, Romania
Cristian Martonos
Department of Comparative Anatomy, Faculty of Veterinary Medicine, University of Agricultural Sciences and Veterinary Medicine, 3-5 Manastur Street, 400372 Cluj Napoca, Romania
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.
