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Acoustic Radiation Force Impulse Elastographic Study of Lung Lesions in Dogs
Bruna Bressianini Lima1, Rafael Kretzer Carneiro2, Brenda Santos Pompeu Miranda1
1Departamento de Clínica e Cirurgia Veterinária, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista "Júlio de Mesquita Filho" - FCAV/Unesp, Jaboticabal, Brazil.
Abstract:
This study aimed to evaluate the use of acoustic radiation force impulse (ARFI) elastography as a diagnostic tool for lung lesions in dogs. Dogs referred to the Radiology Department of the Veterinary Teaching Hospital between 2020 and 2022 for the detection of lung lesions were included in the study. The characteristics of the lung lesions were assessed using radiography as a screening tool for localization, B-mode ultrasound for tissue characterization, and subsequently, both qualitative (elastogram grades 1-3) and quantitative (shear wave velocity-SWV) elastographic evaluations. The lesions were classified based on clinical, ultrasound, radiographic, histopathological, and/or cytological findings into the following categories: consolidations, atelectasis, or neoplasms (nodules and masses). Twenty-six dogs met the eligibility criteria and were included in the study. In some cases, the same dog had more than one type of lesion, resulting in the evaluation of 35 lung lesions: 13 masses, 8 nodules, 8 consolidations, and 4 areas of atelectasis. The quantitative elastographic evaluation revealed lower stiffness in atelectatic lesions (1.48 ± 0.35 m/s) compared with consolidations (2.94 ± 0.64 m/s), nodules (2.85 ± 1.40 m/s), and masses (3.13 ± 1.45 m/s), although no definitive diagnostic cut-off value was established, due to the limited number of benign lesions. The results suggest that ARFI elastography can be a valuable complementary tool alongside clinical data and conventional imaging techniques in assessing lung lesions in dogs. Future studies with a larger sample size of benign parenchymal lung lesions are needed to further explore the potential of elastography for predicting malignancy.
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