Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification

Abstract Rupture risk stratification is critical for incidentally detected intracranial aneurysms. Here we developed and validated an institutional nomogram to solve this issue. We reviewed the imaging and clinical databases for aneurysms from January 2015 to September 2018. Aneurysms were reconstru...

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Autores principales:	QingLin Liu, Peng Jiang, YuHua Jiang, HuiJian Ge, ShaoLin Li, HengWei Jin, Peng Liu, YouXiang Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b7bb08caf05147fa909ea854ad5b1623
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spelling	oai:doaj.org-article:b7bb08caf05147fa909ea854ad5b16232021-12-02T16:15:07ZDevelopment and validation of an institutional nomogram for aiding aneurysm rupture risk stratification10.1038/s41598-021-93286-62045-2322https://doaj.org/article/b7bb08caf05147fa909ea854ad5b16232021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93286-6https://doaj.org/toc/2045-2322Abstract Rupture risk stratification is critical for incidentally detected intracranial aneurysms. Here we developed and validated an institutional nomogram to solve this issue. We reviewed the imaging and clinical databases for aneurysms from January 2015 to September 2018. Aneurysms were reconstructed and morphological features were extracted by the Pyradiomics in python. Multiple logistic regression was performed to develop the nomogram. The consistency of the nomogram predicted rupture risks and PHASES scores was assessed. The performance of the nomogram was evaluated by the discrimination, calibration, and decision curve analysis (DCA). 719 aneurysms were enrolled in this study. For each aneurysm, twelve morphological and nine clinical features were obtained. After logistic regression, seven features were enrolled in the nomogram, which were SurfaceVolumeRatio, Flatness, Age, Hyperlipemia, Smoker, Multiple aneurysms, and Location of the aneurysm. The nomogram had a positive and close correlation with PHASES score in predicting aneurysm rupture risks. AUCs of the nomogram in discriminating aneurysm rupture status was 0.837 in a separate testing set. The calibration curves fitted well and DCA demonstrated positive net benefits of the nomogram in guiding clinical decisions. In conclusion, Pyradiomics derived morphological features based institutional nomogram was useful for aneurysm rupture risk stratification.QingLin LiuPeng JiangYuHua JiangHuiJian GeShaoLin LiHengWei JinPeng LiuYouXiang LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q QingLin Liu Peng Jiang YuHua Jiang HuiJian Ge ShaoLin Li HengWei Jin Peng Liu YouXiang Li Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification
description	Abstract Rupture risk stratification is critical for incidentally detected intracranial aneurysms. Here we developed and validated an institutional nomogram to solve this issue. We reviewed the imaging and clinical databases for aneurysms from January 2015 to September 2018. Aneurysms were reconstructed and morphological features were extracted by the Pyradiomics in python. Multiple logistic regression was performed to develop the nomogram. The consistency of the nomogram predicted rupture risks and PHASES scores was assessed. The performance of the nomogram was evaluated by the discrimination, calibration, and decision curve analysis (DCA). 719 aneurysms were enrolled in this study. For each aneurysm, twelve morphological and nine clinical features were obtained. After logistic regression, seven features were enrolled in the nomogram, which were SurfaceVolumeRatio, Flatness, Age, Hyperlipemia, Smoker, Multiple aneurysms, and Location of the aneurysm. The nomogram had a positive and close correlation with PHASES score in predicting aneurysm rupture risks. AUCs of the nomogram in discriminating aneurysm rupture status was 0.837 in a separate testing set. The calibration curves fitted well and DCA demonstrated positive net benefits of the nomogram in guiding clinical decisions. In conclusion, Pyradiomics derived morphological features based institutional nomogram was useful for aneurysm rupture risk stratification.
format	article
author	QingLin Liu Peng Jiang YuHua Jiang HuiJian Ge ShaoLin Li HengWei Jin Peng Liu YouXiang Li
author_facet	QingLin Liu Peng Jiang YuHua Jiang HuiJian Ge ShaoLin Li HengWei Jin Peng Liu YouXiang Li
author_sort	QingLin Liu
title	Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification
title_short	Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification
title_full	Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification
title_fullStr	Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification
title_full_unstemmed	Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification
title_sort	development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b7bb08caf05147fa909ea854ad5b1623
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Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification

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