Analysis of clinical and ultrasonographic data by use of logistic regression models for prediction of malignant versus benign causes of ultrasonographically detected focal liver lesions in dogs

Tsuyoshi Murakami, Daniel A. Feeney, Katherine L. Bahr

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Objective-To investigate the value of clinical, laboratory, and imaging data for use in predicting malignant or benign histologic results for ultrasonographically detected focal liver lesions in dogs. Sample-Records and archived images of 247 dogs evaluated at the University of Minnesota Veterinary Medical Center from 2005 to 2008 that underwent abdominal ultrasonography and histologic evaluation of the liver. Procedures-Data were analyzed with multivariable logistic regression models. All dogs were classified as having benign or malignant liver disease on the basis of histologic reports. Three multivariable logistic regression models were fit to a development subset of the data by use of combinations of signalment, historical, physical examination, laboratory, and diagnostic imaging (survey radiography and abdominal ultrasonography) data as predictor variables. The resulting models were validated by evaluating predictive performance against a holdout validation subset of the data. Results-Models that included ultrasonographic variables had the highest overall predictive value. In these models, greater lesion size and the presence of peritoneal fluid were the only variables that had a positive association with malignant liver disease. Conclusions and Clinical Relevance-Large ultrasonographically detected liver lesions and the presence of peritoneal fluid were associated with malignant liver disease in dogs.

Original languageEnglish (US)
Pages (from-to)821-829
Number of pages9
JournalAmerican journal of veterinary research
Volume73
Issue number6
DOIs
StatePublished - Jun 1 2012

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