Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage

Abstract It is challenging to distinguish between primary and secondary intracranial hemorrhage (ICH) purely by imaging data, and the two forms of ICHs are treated differently. This study aims to evaluate the potential of CT-based machine learning to identify the etiology of ICHs and compare the eff...
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Autor*in:	Jianbo Lyu [verfasserIn] Zhaohui Xu [verfasserIn] HaiYan Sun [verfasserIn] Fangbing Zhai [verfasserIn] Xiaofeng Qu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Übergeordnetes Werk:	In: Scientific Reports - Nature Portfolio, 2011, 13(2023), 1, Seite 11
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:1 ; pages:11

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1038/s41598-023-30678-w

Katalog-ID:	DOAJ087745976

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allfields	10.1038/s41598-023-30678-w doi (DE-627)DOAJ087745976 (DE-599)DOAJd816b88f20ac4190857937d87f72d9e2 DE-627 ger DE-627 rakwb eng Jianbo Lyu verfasserin aut Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract It is challenging to distinguish between primary and secondary intracranial hemorrhage (ICH) purely by imaging data, and the two forms of ICHs are treated differently. This study aims to evaluate the potential of CT-based machine learning to identify the etiology of ICHs and compare the effectiveness of two regions of interest (ROI) sketching methods. A total of 1702 radiomic features were extracted from the CT brain images of 238 patients with acute ICH. We used the Select K Best method, least absolute shrinkage, and selection operator logistic regression to select the most discriminable features with a support vector machine to build a classifier model. Then, a ten-fold cross-validation strategy was employed to evaluate the performance of the classifier. From all quantitative CT-based imaging features obtained by two sketch methods, eighteen features were selected respectively. The radiomics model outperformed radiologists in distinguishing between primary and secondary ICH in both the volume of interest and the three-layer ROI sketches. As a result, a machine learning-based CT radiomics model can improve the accuracy of identifying primary and secondary ICH. A three-layer ROI sketch can identify primary versus secondary ICH based on the CT radiomics method. Medicine R Science Q Zhaohui Xu verfasserin aut HaiYan Sun verfasserin aut Fangbing Zhai verfasserin aut Xiaofeng Qu verfasserin aut In Scientific Reports Nature Portfolio, 2011 13(2023), 1, Seite 11 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:13 year:2023 number:1 pages:11 https://doi.org/10.1038/s41598-023-30678-w kostenfrei https://doaj.org/article/d816b88f20ac4190857937d87f72d9e2 kostenfrei https://doi.org/10.1038/s41598-023-30678-w kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 11
spelling	10.1038/s41598-023-30678-w doi (DE-627)DOAJ087745976 (DE-599)DOAJd816b88f20ac4190857937d87f72d9e2 DE-627 ger DE-627 rakwb eng Jianbo Lyu verfasserin aut Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract It is challenging to distinguish between primary and secondary intracranial hemorrhage (ICH) purely by imaging data, and the two forms of ICHs are treated differently. This study aims to evaluate the potential of CT-based machine learning to identify the etiology of ICHs and compare the effectiveness of two regions of interest (ROI) sketching methods. A total of 1702 radiomic features were extracted from the CT brain images of 238 patients with acute ICH. We used the Select K Best method, least absolute shrinkage, and selection operator logistic regression to select the most discriminable features with a support vector machine to build a classifier model. Then, a ten-fold cross-validation strategy was employed to evaluate the performance of the classifier. From all quantitative CT-based imaging features obtained by two sketch methods, eighteen features were selected respectively. The radiomics model outperformed radiologists in distinguishing between primary and secondary ICH in both the volume of interest and the three-layer ROI sketches. As a result, a machine learning-based CT radiomics model can improve the accuracy of identifying primary and secondary ICH. A three-layer ROI sketch can identify primary versus secondary ICH based on the CT radiomics method. Medicine R Science Q Zhaohui Xu verfasserin aut HaiYan Sun verfasserin aut Fangbing Zhai verfasserin aut Xiaofeng Qu verfasserin aut In Scientific Reports Nature Portfolio, 2011 13(2023), 1, Seite 11 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:13 year:2023 number:1 pages:11 https://doi.org/10.1038/s41598-023-30678-w kostenfrei https://doaj.org/article/d816b88f20ac4190857937d87f72d9e2 kostenfrei https://doi.org/10.1038/s41598-023-30678-w kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 11
allfields_unstemmed	10.1038/s41598-023-30678-w doi (DE-627)DOAJ087745976 (DE-599)DOAJd816b88f20ac4190857937d87f72d9e2 DE-627 ger DE-627 rakwb eng Jianbo Lyu verfasserin aut Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract It is challenging to distinguish between primary and secondary intracranial hemorrhage (ICH) purely by imaging data, and the two forms of ICHs are treated differently. This study aims to evaluate the potential of CT-based machine learning to identify the etiology of ICHs and compare the effectiveness of two regions of interest (ROI) sketching methods. A total of 1702 radiomic features were extracted from the CT brain images of 238 patients with acute ICH. We used the Select K Best method, least absolute shrinkage, and selection operator logistic regression to select the most discriminable features with a support vector machine to build a classifier model. Then, a ten-fold cross-validation strategy was employed to evaluate the performance of the classifier. From all quantitative CT-based imaging features obtained by two sketch methods, eighteen features were selected respectively. The radiomics model outperformed radiologists in distinguishing between primary and secondary ICH in both the volume of interest and the three-layer ROI sketches. As a result, a machine learning-based CT radiomics model can improve the accuracy of identifying primary and secondary ICH. A three-layer ROI sketch can identify primary versus secondary ICH based on the CT radiomics method. Medicine R Science Q Zhaohui Xu verfasserin aut HaiYan Sun verfasserin aut Fangbing Zhai verfasserin aut Xiaofeng Qu verfasserin aut In Scientific Reports Nature Portfolio, 2011 13(2023), 1, Seite 11 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:13 year:2023 number:1 pages:11 https://doi.org/10.1038/s41598-023-30678-w kostenfrei https://doaj.org/article/d816b88f20ac4190857937d87f72d9e2 kostenfrei https://doi.org/10.1038/s41598-023-30678-w kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 11
allfieldsGer	10.1038/s41598-023-30678-w doi (DE-627)DOAJ087745976 (DE-599)DOAJd816b88f20ac4190857937d87f72d9e2 DE-627 ger DE-627 rakwb eng Jianbo Lyu verfasserin aut Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract It is challenging to distinguish between primary and secondary intracranial hemorrhage (ICH) purely by imaging data, and the two forms of ICHs are treated differently. This study aims to evaluate the potential of CT-based machine learning to identify the etiology of ICHs and compare the effectiveness of two regions of interest (ROI) sketching methods. A total of 1702 radiomic features were extracted from the CT brain images of 238 patients with acute ICH. We used the Select K Best method, least absolute shrinkage, and selection operator logistic regression to select the most discriminable features with a support vector machine to build a classifier model. Then, a ten-fold cross-validation strategy was employed to evaluate the performance of the classifier. From all quantitative CT-based imaging features obtained by two sketch methods, eighteen features were selected respectively. The radiomics model outperformed radiologists in distinguishing between primary and secondary ICH in both the volume of interest and the three-layer ROI sketches. As a result, a machine learning-based CT radiomics model can improve the accuracy of identifying primary and secondary ICH. A three-layer ROI sketch can identify primary versus secondary ICH based on the CT radiomics method. Medicine R Science Q Zhaohui Xu verfasserin aut HaiYan Sun verfasserin aut Fangbing Zhai verfasserin aut Xiaofeng Qu verfasserin aut In Scientific Reports Nature Portfolio, 2011 13(2023), 1, Seite 11 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:13 year:2023 number:1 pages:11 https://doi.org/10.1038/s41598-023-30678-w kostenfrei https://doaj.org/article/d816b88f20ac4190857937d87f72d9e2 kostenfrei https://doi.org/10.1038/s41598-023-30678-w kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 11
allfieldsSound	10.1038/s41598-023-30678-w doi (DE-627)DOAJ087745976 (DE-599)DOAJd816b88f20ac4190857937d87f72d9e2 DE-627 ger DE-627 rakwb eng Jianbo Lyu verfasserin aut Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract It is challenging to distinguish between primary and secondary intracranial hemorrhage (ICH) purely by imaging data, and the two forms of ICHs are treated differently. This study aims to evaluate the potential of CT-based machine learning to identify the etiology of ICHs and compare the effectiveness of two regions of interest (ROI) sketching methods. A total of 1702 radiomic features were extracted from the CT brain images of 238 patients with acute ICH. We used the Select K Best method, least absolute shrinkage, and selection operator logistic regression to select the most discriminable features with a support vector machine to build a classifier model. Then, a ten-fold cross-validation strategy was employed to evaluate the performance of the classifier. From all quantitative CT-based imaging features obtained by two sketch methods, eighteen features were selected respectively. The radiomics model outperformed radiologists in distinguishing between primary and secondary ICH in both the volume of interest and the three-layer ROI sketches. As a result, a machine learning-based CT radiomics model can improve the accuracy of identifying primary and secondary ICH. A three-layer ROI sketch can identify primary versus secondary ICH based on the CT radiomics method. Medicine R Science Q Zhaohui Xu verfasserin aut HaiYan Sun verfasserin aut Fangbing Zhai verfasserin aut Xiaofeng Qu verfasserin aut In Scientific Reports Nature Portfolio, 2011 13(2023), 1, Seite 11 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:13 year:2023 number:1 pages:11 https://doi.org/10.1038/s41598-023-30678-w kostenfrei https://doaj.org/article/d816b88f20ac4190857937d87f72d9e2 kostenfrei https://doi.org/10.1038/s41598-023-30678-w kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 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_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 1 11
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author	Jianbo Lyu
spellingShingle	Jianbo Lyu misc Medicine misc R misc Science misc Q Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage
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topic_title	Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage
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title	Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage
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title_full	Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage
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title_sort	machine learning-based ct radiomics model to discriminate the primary and secondary intracranial hemorrhage
title_auth	Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage
abstract	Abstract It is challenging to distinguish between primary and secondary intracranial hemorrhage (ICH) purely by imaging data, and the two forms of ICHs are treated differently. This study aims to evaluate the potential of CT-based machine learning to identify the etiology of ICHs and compare the effectiveness of two regions of interest (ROI) sketching methods. A total of 1702 radiomic features were extracted from the CT brain images of 238 patients with acute ICH. We used the Select K Best method, least absolute shrinkage, and selection operator logistic regression to select the most discriminable features with a support vector machine to build a classifier model. Then, a ten-fold cross-validation strategy was employed to evaluate the performance of the classifier. From all quantitative CT-based imaging features obtained by two sketch methods, eighteen features were selected respectively. The radiomics model outperformed radiologists in distinguishing between primary and secondary ICH in both the volume of interest and the three-layer ROI sketches. As a result, a machine learning-based CT radiomics model can improve the accuracy of identifying primary and secondary ICH. A three-layer ROI sketch can identify primary versus secondary ICH based on the CT radiomics method.
abstractGer	Abstract It is challenging to distinguish between primary and secondary intracranial hemorrhage (ICH) purely by imaging data, and the two forms of ICHs are treated differently. This study aims to evaluate the potential of CT-based machine learning to identify the etiology of ICHs and compare the effectiveness of two regions of interest (ROI) sketching methods. A total of 1702 radiomic features were extracted from the CT brain images of 238 patients with acute ICH. We used the Select K Best method, least absolute shrinkage, and selection operator logistic regression to select the most discriminable features with a support vector machine to build a classifier model. Then, a ten-fold cross-validation strategy was employed to evaluate the performance of the classifier. From all quantitative CT-based imaging features obtained by two sketch methods, eighteen features were selected respectively. The radiomics model outperformed radiologists in distinguishing between primary and secondary ICH in both the volume of interest and the three-layer ROI sketches. As a result, a machine learning-based CT radiomics model can improve the accuracy of identifying primary and secondary ICH. A three-layer ROI sketch can identify primary versus secondary ICH based on the CT radiomics method.
abstract_unstemmed	Abstract It is challenging to distinguish between primary and secondary intracranial hemorrhage (ICH) purely by imaging data, and the two forms of ICHs are treated differently. This study aims to evaluate the potential of CT-based machine learning to identify the etiology of ICHs and compare the effectiveness of two regions of interest (ROI) sketching methods. A total of 1702 radiomic features were extracted from the CT brain images of 238 patients with acute ICH. We used the Select K Best method, least absolute shrinkage, and selection operator logistic regression to select the most discriminable features with a support vector machine to build a classifier model. Then, a ten-fold cross-validation strategy was employed to evaluate the performance of the classifier. From all quantitative CT-based imaging features obtained by two sketch methods, eighteen features were selected respectively. The radiomics model outperformed radiologists in distinguishing between primary and secondary ICH in both the volume of interest and the three-layer ROI sketches. As a result, a machine learning-based CT radiomics model can improve the accuracy of identifying primary and secondary ICH. A three-layer ROI sketch can identify primary versus secondary ICH based on the CT radiomics method.
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title_short	Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage
url	https://doi.org/10.1038/s41598-023-30678-w https://doaj.org/article/d816b88f20ac4190857937d87f72d9e2 https://doaj.org/toc/2045-2322
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