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

The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases we...
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:	2022

Schlagwörter:	spleen computed tomography focal lesion sarcoma decision tree factorial discriminant analysis

Übergeordnetes Werk:	In: Frontiers in Veterinary Science - Frontiers Media S.A., 2015, 9(2022)
Übergeordnetes Werk:	volume:9 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fvets.2022.872618

Katalog-ID:	DOAJ038755513

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allfields	10.3389/fvets.2022.872618 doi (DE-627)DOAJ038755513 (DE-599)DOAJfe71f6c6120548c8800af9291d3cc05e DE-627 ger DE-627 rakwb eng SF600-1100 Silvia Burti verfasserin aut A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases were divided into four groups, based on the results of cytopatholoy or hystopathology, namely: nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT). Several qualitative and quantitative CT features were described for each case. The relationship occurring between each individual CT feature and the histopathological groups was explred by means of c chi-square test for the count data and by means of Kruskal-Wallis or ANOVA for the continuous data. Furthermore, the main features of each group were described using factorial discriminant analysis, and a decision tree for lesion classification was then developed. Sarcomas were characterised by large dimensions, a cystic appearance and an overall low post contrast-enhancement. NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. A definitive FSL diagnosis can only be obtained by microscopic examination of the spleen. spleen computed tomography focal lesion sarcoma decision tree factorial discriminant analysis 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 9(2022) (DE-627)835029417 (DE-600)2834243-4 22971769 nnns volume:9 year:2022 https://doi.org/10.3389/fvets.2022.872618 kostenfrei https://doaj.org/article/fe71f6c6120548c8800af9291d3cc05e kostenfrei https://www.frontiersin.org/articles/10.3389/fvets.2022.872618/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 9 2022
spelling	10.3389/fvets.2022.872618 doi (DE-627)DOAJ038755513 (DE-599)DOAJfe71f6c6120548c8800af9291d3cc05e DE-627 ger DE-627 rakwb eng SF600-1100 Silvia Burti verfasserin aut A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases were divided into four groups, based on the results of cytopatholoy or hystopathology, namely: nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT). Several qualitative and quantitative CT features were described for each case. The relationship occurring between each individual CT feature and the histopathological groups was explred by means of c chi-square test for the count data and by means of Kruskal-Wallis or ANOVA for the continuous data. Furthermore, the main features of each group were described using factorial discriminant analysis, and a decision tree for lesion classification was then developed. Sarcomas were characterised by large dimensions, a cystic appearance and an overall low post contrast-enhancement. NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. A definitive FSL diagnosis can only be obtained by microscopic examination of the spleen. spleen computed tomography focal lesion sarcoma decision tree factorial discriminant analysis 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 9(2022) (DE-627)835029417 (DE-600)2834243-4 22971769 nnns volume:9 year:2022 https://doi.org/10.3389/fvets.2022.872618 kostenfrei https://doaj.org/article/fe71f6c6120548c8800af9291d3cc05e kostenfrei https://www.frontiersin.org/articles/10.3389/fvets.2022.872618/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 9 2022
allfields_unstemmed	10.3389/fvets.2022.872618 doi (DE-627)DOAJ038755513 (DE-599)DOAJfe71f6c6120548c8800af9291d3cc05e DE-627 ger DE-627 rakwb eng SF600-1100 Silvia Burti verfasserin aut A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases were divided into four groups, based on the results of cytopatholoy or hystopathology, namely: nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT). Several qualitative and quantitative CT features were described for each case. The relationship occurring between each individual CT feature and the histopathological groups was explred by means of c chi-square test for the count data and by means of Kruskal-Wallis or ANOVA for the continuous data. Furthermore, the main features of each group were described using factorial discriminant analysis, and a decision tree for lesion classification was then developed. Sarcomas were characterised by large dimensions, a cystic appearance and an overall low post contrast-enhancement. NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. A definitive FSL diagnosis can only be obtained by microscopic examination of the spleen. spleen computed tomography focal lesion sarcoma decision tree factorial discriminant analysis 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 9(2022) (DE-627)835029417 (DE-600)2834243-4 22971769 nnns volume:9 year:2022 https://doi.org/10.3389/fvets.2022.872618 kostenfrei https://doaj.org/article/fe71f6c6120548c8800af9291d3cc05e kostenfrei https://www.frontiersin.org/articles/10.3389/fvets.2022.872618/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 9 2022
allfieldsGer	10.3389/fvets.2022.872618 doi (DE-627)DOAJ038755513 (DE-599)DOAJfe71f6c6120548c8800af9291d3cc05e DE-627 ger DE-627 rakwb eng SF600-1100 Silvia Burti verfasserin aut A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases were divided into four groups, based on the results of cytopatholoy or hystopathology, namely: nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT). Several qualitative and quantitative CT features were described for each case. The relationship occurring between each individual CT feature and the histopathological groups was explred by means of c chi-square test for the count data and by means of Kruskal-Wallis or ANOVA for the continuous data. Furthermore, the main features of each group were described using factorial discriminant analysis, and a decision tree for lesion classification was then developed. Sarcomas were characterised by large dimensions, a cystic appearance and an overall low post contrast-enhancement. NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. A definitive FSL diagnosis can only be obtained by microscopic examination of the spleen. spleen computed tomography focal lesion sarcoma decision tree factorial discriminant analysis 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 9(2022) (DE-627)835029417 (DE-600)2834243-4 22971769 nnns volume:9 year:2022 https://doi.org/10.3389/fvets.2022.872618 kostenfrei https://doaj.org/article/fe71f6c6120548c8800af9291d3cc05e kostenfrei https://www.frontiersin.org/articles/10.3389/fvets.2022.872618/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 9 2022
allfieldsSound	10.3389/fvets.2022.872618 doi (DE-627)DOAJ038755513 (DE-599)DOAJfe71f6c6120548c8800af9291d3cc05e DE-627 ger DE-627 rakwb eng SF600-1100 Silvia Burti verfasserin aut A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases were divided into four groups, based on the results of cytopatholoy or hystopathology, namely: nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT). Several qualitative and quantitative CT features were described for each case. The relationship occurring between each individual CT feature and the histopathological groups was explred by means of c chi-square test for the count data and by means of Kruskal-Wallis or ANOVA for the continuous data. Furthermore, the main features of each group were described using factorial discriminant analysis, and a decision tree for lesion classification was then developed. Sarcomas were characterised by large dimensions, a cystic appearance and an overall low post contrast-enhancement. NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. A definitive FSL diagnosis can only be obtained by microscopic examination of the spleen. spleen computed tomography focal lesion sarcoma decision tree factorial discriminant analysis 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 9(2022) (DE-627)835029417 (DE-600)2834243-4 22971769 nnns volume:9 year:2022 https://doi.org/10.3389/fvets.2022.872618 kostenfrei https://doaj.org/article/fe71f6c6120548c8800af9291d3cc05e kostenfrei https://www.frontiersin.org/articles/10.3389/fvets.2022.872618/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 9 2022
language	English
source	In Frontiers in Veterinary Science 9(2022) volume:9 year:2022
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topic_facet	spleen computed tomography focal lesion sarcoma decision tree factorial discriminant analysis Veterinary medicine
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authorswithroles_txt_mv	Silvia Burti @@aut@@ Alessandro Zotti @@aut@@ Federico Bonsembiante @@aut@@ Barbara Contiero @@aut@@ Tommaso Banzato @@aut@@
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NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. 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callnumber-first	S - Agriculture
author	Silvia Burti
spellingShingle	Silvia Burti misc SF600-1100 misc spleen misc computed tomography misc focal lesion misc sarcoma misc decision tree misc factorial discriminant analysis misc Veterinary medicine A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features
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topic_title	SF600-1100 A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features spleen computed tomography focal lesion sarcoma decision tree factorial discriminant analysis
topic	misc SF600-1100 misc spleen misc computed tomography misc focal lesion misc sarcoma misc decision tree misc factorial discriminant analysis misc Veterinary medicine
topic_unstemmed	misc SF600-1100 misc spleen misc computed tomography misc focal lesion misc sarcoma misc decision tree misc factorial discriminant analysis misc Veterinary medicine
topic_browse	misc SF600-1100 misc spleen misc computed tomography misc focal lesion misc sarcoma misc decision tree misc factorial discriminant analysis misc Veterinary medicine
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title	A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features
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title_full	A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features
author_sort	Silvia Burti
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author_browse	Silvia Burti Alessandro Zotti Federico Bonsembiante Barbara Contiero Tommaso Banzato
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title_sort	machine learning-based approach for classification of focal splenic lesions based on their ct features
callnumber	SF600-1100
title_auth	A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features
abstract	The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases were divided into four groups, based on the results of cytopatholoy or hystopathology, namely: nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT). Several qualitative and quantitative CT features were described for each case. The relationship occurring between each individual CT feature and the histopathological groups was explred by means of c chi-square test for the count data and by means of Kruskal-Wallis or ANOVA for the continuous data. Furthermore, the main features of each group were described using factorial discriminant analysis, and a decision tree for lesion classification was then developed. Sarcomas were characterised by large dimensions, a cystic appearance and an overall low post contrast-enhancement. NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. A definitive FSL diagnosis can only be obtained by microscopic examination of the spleen.
abstractGer	The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases were divided into four groups, based on the results of cytopatholoy or hystopathology, namely: nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT). Several qualitative and quantitative CT features were described for each case. The relationship occurring between each individual CT feature and the histopathological groups was explred by means of c chi-square test for the count data and by means of Kruskal-Wallis or ANOVA for the continuous data. Furthermore, the main features of each group were described using factorial discriminant analysis, and a decision tree for lesion classification was then developed. Sarcomas were characterised by large dimensions, a cystic appearance and an overall low post contrast-enhancement. NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. A definitive FSL diagnosis can only be obtained by microscopic examination of the spleen.
abstract_unstemmed	The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases were divided into four groups, based on the results of cytopatholoy or hystopathology, namely: nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT). Several qualitative and quantitative CT features were described for each case. The relationship occurring between each individual CT feature and the histopathological groups was explred by means of c chi-square test for the count data and by means of Kruskal-Wallis or ANOVA for the continuous data. Furthermore, the main features of each group were described using factorial discriminant analysis, and a decision tree for lesion classification was then developed. Sarcomas were characterised by large dimensions, a cystic appearance and an overall low post contrast-enhancement. NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. A definitive FSL diagnosis can only be obtained by microscopic examination of the spleen.
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title_short	A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features
url	https://doi.org/10.3389/fvets.2022.872618 https://doaj.org/article/fe71f6c6120548c8800af9291d3cc05e https://www.frontiersin.org/articles/10.3389/fvets.2022.872618/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	2024-07-03T19:42:17.890Z
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A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features

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