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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\u2009+\u20091.74%,\u2009\u2212\u20090.31%,\u2009\u2212\u20090.44%,\u2009\u2212\u20090.19%,\u2009\u2212\u20090.01%, and\u2009+\u20090.26% 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.<\/jats:p>","DOI":"10.1038\/s41598-021-82665-8","type":"journal-article","created":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T11:08:10Z","timestamp":1612436890000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery"],"prefix":"10.1038","volume":"11","author":[{"given":"Cheng-chia","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Kai","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih-Chun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chia-Feng","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huai-Che","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Wei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Yuh","family":"Chung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong-Sin","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsiu-Mei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Te","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wan-Yuo","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,2,4]]},"reference":[{"key":"82665_CR1","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1148\/radiol.2019190613","volume":"291","author":"CP Langlotz","year":"2019","unstructured":"Langlotz, C. 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National Yang-Ming University and Taipei Veteran General Hospital are the patent applicants, and the authors (CC Lee, WK Lee, CC Wu, CF Lu, HC Yang, WY Chung, HM Wu, YT Wu, and WY Guo, except YS Hu and YW Chen) are the inventors of this patent. The application number is 16\/939,881, and the current status is under official review.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"3106"}}