Peer-reviewed veterinary case report
How CT scans help diagnose liver lumps in dogs
By Burti, Silvia et al.·Published in Frontiers in veterinary science·2021·Department of Animal Medicine, Italy·View original on PubMed →
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Original publication title: Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases.
- Species:
- dog
Plain-English summary
A group of dogs with liver lesions underwent CT scans to help identify the type of lesions they had. The study looked at 69 dogs with different types of liver issues, including cancer and benign growths. The researchers found specific features on the CT images that could help distinguish between these types, and they developed a decision-making tool that could accurately classify the lesions most of the time. However, a final diagnosis still required additional tests like cytology (cell analysis) or histology (tissue analysis).
People also search for: dog liver lesions diagnosis · CT scan for dog liver cancer · symptoms of liver cancer in dogs
Abstract
To describe the computed tomographic (CT) features of focal liver lesions (FLLs) in dogs, that could enable predicting lesion histotype. Dogs diagnosed with FLLs through both CT and cytopathology and/or histopathology were retrospectively collected. Ten qualitative and 6 quantitative CT features have been described for each case. Lastly, a machine learning-based decision tree was developed to predict the lesion histotype. Four categories of FLLs - hepatocellular carcinoma (HCC,= 13), nodular hyperplasia (NH,= 19), other benign lesions (OBL,= 18), and other malignant lesions (OML,= 19) - were evaluated in 69 dogs. Five of the observed qualitative CT features resulted to be statistically significant in the distinction between the 4 categories: surface, appearance, lymph-node appearance, capsule formation, and homogeneity of contrast medium distribution. Three of the observed quantitative CT features were significantly different between the 4 categories: the Hounsfield Units (HU) of the radiologically normal liver parenchyma during the pre-contrast scan, the maximum dimension, and the ellipsoid volume of the lesion. Using the machine learning-based decision tree, it was possible to correctly classify NHs, OBLs, HCCs, and OMLs with an accuracy of 0.74, 0.88, 0.87, and 0.75, respectively. The developed decision tree could be an easy-to-use tool to predict the histotype of different FLLs in dogs. Cytology and histology are necessary to obtain the final diagnosis of the lesions.
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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/33748206/