Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery

Abstract Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensive imaging follow-up. Our objective in thi...

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Autores principales:	Cheng-chia Lee, Wei-Kai Lee, Chih-Chun Wu, Chia-Feng Lu, Huai-Che Yang, Yu-Wei Chen, Wen-Yuh Chung, Yong-Sin Hu, Hsiu-Mei Wu, Yu-Te Wu, Wan-Yuo Guo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/04c4e0b7b3ae4e1497bca19363a1e746
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spelling	oai:doaj.org-article:04c4e0b7b3ae4e1497bca19363a1e7462021-12-02T14:06:55ZApplying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery10.1038/s41598-021-82665-82045-2322https://doaj.org/article/04c4e0b7b3ae4e1497bca19363a1e7462021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82665-8https://doaj.org/toc/2045-2322Abstract Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensive imaging follow-up. Our objective in this study was to establish an algorithm by which to automate the volumetric measurement of vestibular schwannoma (VS) using a series of parametric MR images following radiosurgery. Based on a sample of 861 consecutive patients who underwent Gamma Knife radiosurgery (GKRS) between 1993 and 2008, the proposed end-to-end deep-learning scheme with automated pre-processing pipeline was applied to a series of 1290 MR examinations (T1W+C, and T2W parametric MR images). All of which were performed under consistent imaging acquisition protocols. The relative volume difference (RVD) between AI-based volumetric measurements and clinical measurements performed by expert radiologists were + 1.74%, − 0.31%, − 0.44%, − 0.19%, − 0.01%, and + 0.26% at each follow-up time point, regardless of the state of the tumor (progressed, pseudo-progressed, or regressed). This study outlines an approach to the evaluation of treatment responses via novel volumetric measurement algorithm, and can be used longitudinally following GKRS for VS. The proposed deep learning AI scheme is applicable to longitudinal follow-up assessments following a variety of therapeutic interventions.Cheng-chia LeeWei-Kai LeeChih-Chun WuChia-Feng LuHuai-Che YangYu-Wei ChenWen-Yuh ChungYong-Sin HuHsiu-Mei WuYu-Te WuWan-Yuo GuoNature 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 Cheng-chia Lee Wei-Kai Lee Chih-Chun Wu Chia-Feng Lu Huai-Che Yang Yu-Wei Chen Wen-Yuh Chung Yong-Sin Hu Hsiu-Mei Wu Yu-Te Wu Wan-Yuo Guo Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery
description	Abstract Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensive imaging follow-up. Our objective in this study was to establish an algorithm by which to automate the volumetric measurement of vestibular schwannoma (VS) using a series of parametric MR images following radiosurgery. Based on a sample of 861 consecutive patients who underwent Gamma Knife radiosurgery (GKRS) between 1993 and 2008, the proposed end-to-end deep-learning scheme with automated pre-processing pipeline was applied to a series of 1290 MR examinations (T1W+C, and T2W parametric MR images). All of which were performed under consistent imaging acquisition protocols. The relative volume difference (RVD) between AI-based volumetric measurements and clinical measurements performed by expert radiologists were + 1.74%, − 0.31%, − 0.44%, − 0.19%, − 0.01%, and + 0.26% at each follow-up time point, regardless of the state of the tumor (progressed, pseudo-progressed, or regressed). This study outlines an approach to the evaluation of treatment responses via novel volumetric measurement algorithm, and can be used longitudinally following GKRS for VS. The proposed deep learning AI scheme is applicable to longitudinal follow-up assessments following a variety of therapeutic interventions.
format	article
author	Cheng-chia Lee Wei-Kai Lee Chih-Chun Wu Chia-Feng Lu Huai-Che Yang Yu-Wei Chen Wen-Yuh Chung Yong-Sin Hu Hsiu-Mei Wu Yu-Te Wu Wan-Yuo Guo
author_facet	Cheng-chia Lee Wei-Kai Lee Chih-Chun Wu Chia-Feng Lu Huai-Che Yang Yu-Wei Chen Wen-Yuh Chung Yong-Sin Hu Hsiu-Mei Wu Yu-Te Wu Wan-Yuo Guo
author_sort	Cheng-chia Lee
title	Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery
title_short	Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery
title_full	Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery
title_fullStr	Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery
title_full_unstemmed	Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery
title_sort	applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/04c4e0b7b3ae4e1497bca19363a1e746
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Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery

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