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Contrast-enhanced ultrasonography features of hepatobiliary neoplasms in cats
  1. Tommaso Banzato1,
  2. Silvia Burti1,
  3. Giuseppe Rubini2,
  4. Riccardo Orlandi3,
  5. Paolo Bargellini3,
  6. Federico Bonsembiante1,4 and
  7. Alessandro Zotti1
  1. 1 Animal Medicine, Productions, and Health, University of Padua, Padova, Italy
  2. 2 ULTRAVET, Bologna, Italy
  3. 3 Tyrus Veterinary clinic, Terni, Italy
  4. 4 Department of Comparative Biomedicine and Food Science, University of Padua, Legnaro, Italy
  1. Correspondence to Dr Tommaso Banzato, Animal Medicine, Productions, and Health, University of Padua, Padova, Veneto, Italy; tommaso.banzato{at}


Background Contrast-enhanced ultrasonography (CEUS) features of primary hepatobiliary neoplasms have been reported in dogs but no information is available in cats.

Methods Qualitative and quantitative features of bile duct adenomas (BDAs, n=20), bile duct carcinomas (BDCs, n=16), and hepatocellular carcinomas (HCCs, n=8) are described in 44 cats.

Results There was an overlap in CEUS qualitative features between different histotypes, both in wash-in and wash-out phases. Distinction between different neoplasms based only on the CEUS qualitative features was not possible. At peak of enhancement, the BDAs, BDCs and HCCs showed a large range of echogenicities, from hypoenhancement to hyperenhancement, in comparison to the liver parenchyma. Eight of 20 BDAs showed inhomogeneous hyperenhancement during wash-in, which is a feature reported as typical of malignant lesions in dogs. BDC had a significantly faster wash-in compared with both BDA and HCC but the diagnostic accuracy of all the included quantitative variables was only moderate. No significant differences in the wash-out quantitative features of BDA and BDC were evident.

Conclusion There is poor evidence that CEUS may be used to distinguish between different primary hepatobiliary neoplasms in cats.

  • hepatocellular carcinoma
  • cholangiocarcinoma
  • contrast-enhanced ultrasound
  • cytology
  • cat

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  • Funding The present paper is part of a project funded by a research grant from the Department of Animal Medicine, Production and Health – MAPS, University of Padua, Italy: SID- Zotti 2018 (€ 32,000; Application of deep-learning algorithms in pet animal diagnostic imaging)

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval This study was conducted according to the Italian law D. Leg.vo 26/2014 (that transposes the EU directive 2010/63/EU). As the data used in this study were part of the routine clinical activity no ethical committee approval was needed. Informed consent regarding the treatment of personal data was obtained from the owners.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement All data relevant to the study are included in the article.

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