Format

Send to

Choose Destination
J Vet Med Sci. 2019 Oct 10. doi: 10.1292/jvms.19-0278. [Epub ahead of print]

Computed tomographic features for differentiating benign from malignant liver lesions in dogs.

Author information

1
Laboratory of Veterinary Internal Medicine, Department of Veterinary Clinical Sciences, Graduate School of Veterinary Medicine, Hokkaido University.
2
Veterinary Teaching Hospital, Graduate School of Veterinary Medicine, Hokkaido University.
3
School of Veterinary Medicine, Department of Veterinary Medicine, Rakuno Gakuen University.

Abstract

Thus far, there are few computed tomography (CT) characteristics that can distinguish benign and malignant etiologies. The criteria are complex, subjective, and difficult to use in clinical applications due to the high level of experience needed. This study aimed to identify practical CT variables and their clinical relevance for broadly classifying histopathological diagnoses as benign or malignant. In this prospective study, all dogs with liver nodules or masses that underwent CT examination and subsequent histopathological diagnosis were included. Signalments, CT findings and histopathological diagnoses were recorded. Seventy liver nodules or masses in 57 dogs were diagnosed, comprising 18 benign and 52 malignant lesions. Twenty-three qualitative and quantitative CT variables were evaluated using univariate and stepwise multivariate analyses, respectively. Two variables, namely, the postcontrast enhancement pattern of the lesion in the delayed phase (heterogeneous; odds ratio (OR): 14.7, 95% confidence interval (CI): 0.82-262.03, P=0.0429) and the maximal transverse diameter of the lesion (>4.5 cm; OR: 33.3, 95% CI: 2.29-484.18, P=0.0006), were significantly related to the differentiation of benign from malignant liver lesions, with an area under the curve of 0.8910, representing an accuracy of 88.6%. These findings indicate that features from triple-phase CT can provide information for distinguishing pathological varieties of focal liver lesions and for clinical decision making. Evaluations of the maximal transverse diameter and postcontrast enhancement pattern of the lesion included simple CT features for predicting liver malignancy with high accuracy in clinical settings.

KEYWORDS:

canine; classification; computed tomography; liver; neoplasia

PMID:
31597816
DOI:
10.1292/jvms.19-0278
Free full text

Supplemental Content

Full text links

Icon for J-STAGE, Japan Science and Technology Information Aggregator, Electronic
Loading ...
Support Center