Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases

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 hav...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Silvia Burti, Alessandro Zotti, Federico Bonsembiante, Barbara Contiero, Tommaso Banzato
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	decision tree HCC (hepatic cellular carcinoma) contrast - enhanced CT computed tomography focal liver lesion Veterinary medicine SF600-1100
Acceso en línea:	https://doaj.org/article/12100bb9ad45403bba60a2b7481b747b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:12100bb9ad45403bba60a2b7481b747b
record_format	dspace
spelling	oai:doaj.org-article:12100bb9ad45403bba60a2b7481b747b2021-11-04T14:29:16ZDiagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases2297-176910.3389/fvets.2021.611556https://doaj.org/article/12100bb9ad45403bba60a2b7481b747b2021-03-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fvets.2021.611556/fullhttps://doaj.org/toc/2297-1769To 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, n = 13), nodular hyperplasia (NH, n = 19), other benign lesions (OBL, n = 18), and other malignant lesions (OML, n = 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.Silvia BurtiAlessandro ZottiFederico BonsembianteFederico BonsembianteBarbara ContieroTommaso BanzatoFrontiers Media S.A.articledecision treeHCC (hepatic cellular carcinoma)contrast - enhanced CTcomputed tomographyfocal liver lesionVeterinary medicineSF600-1100ENFrontiers in Veterinary Science, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	decision tree HCC (hepatic cellular carcinoma) contrast - enhanced CT computed tomography focal liver lesion Veterinary medicine SF600-1100
spellingShingle	decision tree HCC (hepatic cellular carcinoma) contrast - enhanced CT computed tomography focal liver lesion Veterinary medicine SF600-1100 Silvia Burti Alessandro Zotti Federico Bonsembiante Federico Bonsembiante Barbara Contiero Tommaso Banzato Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
description	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, n = 13), nodular hyperplasia (NH, n = 19), other benign lesions (OBL, n = 18), and other malignant lesions (OML, n = 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.
format	article
author	Silvia Burti Alessandro Zotti Federico Bonsembiante Federico Bonsembiante Barbara Contiero Tommaso Banzato
author_facet	Silvia Burti Alessandro Zotti Federico Bonsembiante Federico Bonsembiante Barbara Contiero Tommaso Banzato
author_sort	Silvia Burti
title	Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
title_short	Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
title_full	Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
title_fullStr	Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
title_full_unstemmed	Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
title_sort	diagnostic accuracy of delayed phase post contrast computed tomographic images in the diagnosis of focal liver lesions in dogs: 69 cases
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/12100bb9ad45403bba60a2b7481b747b
work_keys_str_mv	AT silviaburti diagnosticaccuracyofdelayedphasepostcontrastcomputedtomographicimagesinthediagnosisoffocalliverlesionsindogs69cases AT alessandrozotti diagnosticaccuracyofdelayedphasepostcontrastcomputedtomographicimagesinthediagnosisoffocalliverlesionsindogs69cases AT federicobonsembiante diagnosticaccuracyofdelayedphasepostcontrastcomputedtomographicimagesinthediagnosisoffocalliverlesionsindogs69cases AT federicobonsembiante diagnosticaccuracyofdelayedphasepostcontrastcomputedtomographicimagesinthediagnosisoffocalliverlesionsindogs69cases AT barbaracontiero diagnosticaccuracyofdelayedphasepostcontrastcomputedtomographicimagesinthediagnosisoffocalliverlesionsindogs69cases AT tommasobanzato diagnosticaccuracyofdelayedphasepostcontrastcomputedtomographicimagesinthediagnosisoffocalliverlesionsindogs69cases
_version_	1718444820761935872

Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases

Ejemplares similares