Literature DB >> 27663373

Texture analysis of B-mode ultrasound images to stage hepatic lipidosis in the dairy cow: A methodological study.

Tommaso Banzato1, Enrico Fiore2, Massimo Morgante3, Elisabetta Manuali4, Alessandro Zotti5.   

Abstract

Hepatic lipidosis is the most diffused hepatic disease in the lactating cow. A new methodology to estimate the degree of fatty infiltration of the liver in lactating cows by means of texture analysis of B-mode ultrasound images is proposed. B-mode ultrasonography of the liver was performed in 48 Holstein Friesian cows using standardized ultrasound parameters. Liver biopsies to determine the triacylglycerol content of the liver (TAGqa) were obtained from each animal. A large number of texture parameters were calculated on the ultrasound images by means of a free software. Based on the TAGqa content of the liver, 29 samples were classified as mild (TAGqa<50mg/g), 6 as moderate (50mg/g<TAGqa>100mg/g) and 13 as severe (TAG>100mg/g) in steatosis. Stepwise linear regression analysis was performed to predict the TAGqa content of the liver (TAGpred) from the texture parameters calculated on the ultrasound images. A five-variable model was used to predict the TAG content from the ultrasound images. The regression model explained 83.4% of the variance. An area under the curve (AUC) of 0.949 was calculated for <50mg/g vs >50mg/g of TAGqa; using an optimal cut-off value of 72mg/g TAGpred had a sensitivity of 86.2% and a specificity of 84.2%. An AUC of 0.978 for <100mg/g vs >100mg/g of TAGqa was calculated; using an optimal cut-off value of 89mg/g, TAGpred sensitivity was 92.3% and specificity was 88.6%. Texture analysis of B-mode ultrasound images may therefore be used to accurately predict the TAG content of the liver in lactating cows.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cow; Hepatic lipidosis; Texture analysis; Triacylglycerol; Ultrasound

Mesh:

Substances:

Year:  2016        PMID: 27663373     DOI: 10.1016/j.rvsc.2016.08.007

Source DB:  PubMed          Journal:  Res Vet Sci        ISSN: 0034-5288            Impact factor:   2.534


  6 in total

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2.  A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features.

Authors:  Silvia Burti; Alessandro Zotti; Federico Bonsembiante; Barbara Contiero; Tommaso Banzato
Journal:  Front Vet Sci       Date:  2022-05-02

3.  Associations between Milk Fatty Acid Profile and Body Condition Score, Ultrasound Hepatic Measurements and Blood Metabolites in Holstein Cows.

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Journal:  Animals (Basel)       Date:  2022-05-06       Impact factor: 3.231

4.  Application of Ultrasound Images Texture Analysis for the Estimation of Intramuscular Fat Content in the Longissimus Thoracis Muscle of Beef Cattle after Slaughter: A Methodological Study.

Authors:  Giorgia Fabbri; Matteo Gianesella; Luigi Gallo; Massimo Morgante; Barbara Contiero; Michele Muraro; Matteo Boso; Enrico Fiore
Journal:  Animals (Basel)       Date:  2021-04-13       Impact factor: 2.752

5.  A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images.

Authors:  Tommaso Banzato; Marco Bernardini; Giunio B Cherubini; Alessandro Zotti
Journal:  BMC Vet Res       Date:  2018-10-22       Impact factor: 2.741

6.  A retrospective study on transabdominal ultrasound measurements of the rumen wall thickness to evaluate chronic rumen acidosis in beef cattle.

Authors:  Enrico Fiore; Vanessa Faillace; Massimo Morgante; Leonardo Armato; Matteo Gianesella
Journal:  BMC Vet Res       Date:  2020-09-15       Impact factor: 2.741

  6 in total

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