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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-82665-8 |
| _version_ | 1818432375044964352 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T16:04:12Z |
| format | Article |
| id | doaj.art-04c4e0b7b3ae4e1497bca19363a1e746 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T16:04:12Z |
| publishDate | 2021-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-04c4e0b7b3ae4e1497bca19363a1e7462022-12-21T22:55:08ZengNature PortfolioScientific Reports2045-23222021-02-0111111010.1038/s41598-021-82665-8Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgeryCheng-chia Lee0Wei-Kai Lee1Chih-Chun Wu2Chia-Feng Lu3Huai-Che Yang4Yu-Wei Chen5Wen-Yuh Chung6Yong-Sin Hu7Hsiu-Mei Wu8Yu-Te Wu9Wan-Yuo Guo10School of Medicine, National Yang-Ming UniversityDepartment of Biomedical Imaging and Radiological Sciences, National Yang-Ming UniversityDepartment of Radiology, Taipei Veteran General HospitalDepartment of Biomedical Imaging and Radiological Sciences, National Yang-Ming UniversitySchool of Medicine, National Yang-Ming UniversityDepartment of Neurosurgery, Neurological Institute, Taipei Veteran General HospitalSchool of Medicine, National Yang-Ming UniversityDepartment of Radiology, Taipei Veteran General HospitalDepartment of Radiology, Taipei Veteran General HospitalDepartment of Biomedical Imaging and Radiological Sciences, National Yang-Ming UniversityDepartment of Radiology, Taipei Veteran General HospitalAbstract 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.https://doi.org/10.1038/s41598-021-82665-8 |
| spellingShingle | 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 Scientific Reports |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41598-021-82665-8 |
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