Fine Needle Aspiration Cytology (FNAC) of Neoplasms in Dogs and Cats, with Emphasis on Differential Diagnosis - A Retrospective Study

Abstract

Fine needle aspiration cytology (FNA) is a fast, noninvasive method, widely used by clinical laboratories to diagnose neoplasms before the surgical intervention. FNA can be used for obtaining a tissue diagnosis of subcutaneous and other tumors and also for body cavity effusions. This study proposes an evaluation of the types of neoplasms and malignancy criteria found in cytological samples obtained from dogs and cats. The study was conducted at the Faculty of Veterinary Medicine of Iasi between 2012 and 2014 on 23 dogs and 15 cats. Patients diagnosed with masses and body cavity effusions by the pathologists from Internal Medicine and Radiology Clinic were referred to further assessment. Routine FNAC was performed by the attending pathologist. The cytological samples were obtained by fine needle aspiration in order to establish the cellular origin of the neoplasms. Of 67 samples of fine needle aspiration, 38 (56,7 %) specimens were diagnosed as neoplasms, 23 (60,5 %) being obtained from dogs and 15 (39,5 %) from cats. Based on cytological malignancy criteria were diagnosed with certainty a number of 18 (47.4%) neoplasms represented by: adenocarcinoma -4 cases (three cases with pulmonary origin and one with mammary origin), cutaneous mastocytoma -3 cases, osteosarcoma -3 cases, lymphoma and lymphosarcoma -2 cases, liposarcoma -1 case, hemangiosarcoma -2 cases, leukemia -2 cases and hemophagocytic histiocytic sarcoma in one case. For the rest of the samples the cell type was not established, being recommended further histopathological examination in order to provide a certain diagnosis. The results of this study confirmed FNAC as a reliable and useful diagnostic procedure for neoplastic masses. Even though FNAC has a limited value in determining the cellular origin of tumoral masses or malignant effusions, it often provides a definitive diagnosis or can narrow the list of differentials.