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...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Silvia Burti [verfasserIn] Alessandro Zotti [verfasserIn] Federico Bonsembiante [verfasserIn] Barbara Contiero [verfasserIn] Tommaso Banzato [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	decision tree HCC (hepatic cellular carcinoma) contrast - enhanced CT computed tomography focal liver lesion

Übergeordnetes Werk:	In: Frontiers in Veterinary Science - Frontiers Media S.A., 2015, 8(2021)
Übergeordnetes Werk:	volume:8 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fvets.2021.611556

Katalog-ID:	DOAJ076406342

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spelling	10.3389/fvets.2021.611556 doi (DE-627)DOAJ076406342 (DE-599)DOAJ12100bb9ad45403bba60a2b7481b747b DE-627 ger DE-627 rakwb eng SF600-1100 Silvia Burti verfasserin aut Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. decision tree HCC (hepatic cellular carcinoma) contrast - enhanced CT computed tomography focal liver lesion Veterinary medicine Alessandro Zotti verfasserin aut Federico Bonsembiante verfasserin aut Federico Bonsembiante verfasserin aut Barbara Contiero verfasserin aut Tommaso Banzato verfasserin aut In Frontiers in Veterinary Science Frontiers Media S.A., 2015 8(2021) (DE-627)835029417 (DE-600)2834243-4 22971769 nnns volume:8 year:2021 https://doi.org/10.3389/fvets.2021.611556 kostenfrei https://doaj.org/article/12100bb9ad45403bba60a2b7481b747b kostenfrei https://www.frontiersin.org/articles/10.3389/fvets.2021.611556/full kostenfrei https://doaj.org/toc/2297-1769 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021
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allfieldsGer	10.3389/fvets.2021.611556 doi (DE-627)DOAJ076406342 (DE-599)DOAJ12100bb9ad45403bba60a2b7481b747b DE-627 ger DE-627 rakwb eng SF600-1100 Silvia Burti verfasserin aut Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. decision tree HCC (hepatic cellular carcinoma) contrast - enhanced CT computed tomography focal liver lesion Veterinary medicine Alessandro Zotti verfasserin aut Federico Bonsembiante verfasserin aut Federico Bonsembiante verfasserin aut Barbara Contiero verfasserin aut Tommaso Banzato verfasserin aut In Frontiers in Veterinary Science Frontiers Media S.A., 2015 8(2021) (DE-627)835029417 (DE-600)2834243-4 22971769 nnns volume:8 year:2021 https://doi.org/10.3389/fvets.2021.611556 kostenfrei https://doaj.org/article/12100bb9ad45403bba60a2b7481b747b kostenfrei https://www.frontiersin.org/articles/10.3389/fvets.2021.611556/full kostenfrei https://doaj.org/toc/2297-1769 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021
allfieldsSound	10.3389/fvets.2021.611556 doi (DE-627)DOAJ076406342 (DE-599)DOAJ12100bb9ad45403bba60a2b7481b747b DE-627 ger DE-627 rakwb eng SF600-1100 Silvia Burti verfasserin aut Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. decision tree HCC (hepatic cellular carcinoma) contrast - enhanced CT computed tomography focal liver lesion Veterinary medicine Alessandro Zotti verfasserin aut Federico Bonsembiante verfasserin aut Federico Bonsembiante verfasserin aut Barbara Contiero verfasserin aut Tommaso Banzato verfasserin aut In Frontiers in Veterinary Science Frontiers Media S.A., 2015 8(2021) (DE-627)835029417 (DE-600)2834243-4 22971769 nnns volume:8 year:2021 https://doi.org/10.3389/fvets.2021.611556 kostenfrei https://doaj.org/article/12100bb9ad45403bba60a2b7481b747b kostenfrei https://www.frontiersin.org/articles/10.3389/fvets.2021.611556/full kostenfrei https://doaj.org/toc/2297-1769 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021
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callnumber-first	S - Agriculture
author	Silvia Burti
spellingShingle	Silvia Burti misc SF600-1100 misc decision tree misc HCC (hepatic cellular carcinoma) misc contrast - enhanced CT misc computed tomography misc focal liver lesion misc Veterinary medicine Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
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topic_title	SF600-1100 Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases decision tree HCC (hepatic cellular carcinoma) contrast - enhanced CT computed tomography focal liver lesion
topic	misc SF600-1100 misc decision tree misc HCC (hepatic cellular carcinoma) misc contrast - enhanced CT misc computed tomography misc focal liver lesion misc Veterinary medicine
topic_unstemmed	misc SF600-1100 misc decision tree misc HCC (hepatic cellular carcinoma) misc contrast - enhanced CT misc computed tomography misc focal liver lesion misc Veterinary medicine
topic_browse	misc SF600-1100 misc decision tree misc HCC (hepatic cellular carcinoma) misc contrast - enhanced CT misc computed tomography misc focal liver lesion misc Veterinary medicine
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title	Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
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title_full	Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
author_sort	Silvia Burti
journal	Frontiers in Veterinary Science
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author_browse	Silvia Burti Alessandro Zotti Federico Bonsembiante Barbara Contiero Tommaso Banzato
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title_sort	diagnostic accuracy of delayed phase post contrast computed tomographic images in the diagnosis of focal liver lesions in dogs: 69 cases
callnumber	SF600-1100
title_auth	Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
url	https://doi.org/10.3389/fvets.2021.611556 https://doaj.org/article/12100bb9ad45403bba60a2b7481b747b https://www.frontiersin.org/articles/10.3389/fvets.2021.611556/full https://doaj.org/toc/2297-1769
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author2	Alessandro Zotti Federico Bonsembiante Barbara Contiero Tommaso Banzato
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up_date	2024-07-03T20:19:59.319Z
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Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases

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