{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T05:32:23Z","timestamp":1777008743356,"version":"3.51.4"},"reference-count":24,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2020,4,18]],"date-time":"2020-04-18T00:00:00Z","timestamp":1587168000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:sec><jats:title>Objectives\/Hypothesis<\/jats:title><jats:p>To determine if an automated vestibular schwannoma (VS) segmentation model has comparable performance to using the greatest linear dimension to detect growth.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Design<\/jats:title><jats:p>Case\u2010control Study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Patients were selected from an internal database who had an initial gadolinium\u2010enhanced T1\u2010weighted magnetic resonance imaging scan and a follow\u2010up scan captured at least 5\u2009months later. Two observers manually segmented the VS to compute volumes, and one observer's segmentations were used to train a convolutional neural network model to automatically segment the VS and determine the volume. The results of automatic segmentation were compared to the observer whose measurements were not used in model development to measure agreement. We then examined the sensitivity, specificity, and area under the receiver\u2010operating characteristic curve (AUC) to compare automated volumetric growth detection versus using the greatest linear dimension. Growth detection determined by the external observer's measurements served as the gold standard.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 65 patients and 130 scans were studied. The automated method of segmentation demonstrated excellent agreement with the observer whose measurements were not used for model development for the initial scan (interclass correlational coefficient [ICC] =\u20090.995; 95% confidence interval [CI]: 0.991\u20100.997) and follow\u2010up scan (ICC = 0.960; 95% CI: 0.935\u20130.975). The automated method of segmentation demonstrated increased sensitivity (72.2% vs. 63.9%), specificity (79.3% vs. 69.0%), and AUC (0.822 vs. 0.701) compared to using the greatest linear dimension for growth detection.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>In detecting VS growth, a convolutional neural network model outperformed using the greatest linear dimension, demonstrating a potential application of artificial intelligence methods to VS surveillance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>4 <jats:italic>Laryngoscope<\/jats:italic>, 131:E619\u2013E624, 2021<\/jats:p><\/jats:sec>","DOI":"10.1002\/lary.28695","type":"journal-article","created":{"date-parts":[[2020,4,18]],"date-time":"2020-04-18T13:44:40Z","timestamp":1587217480000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Automated Detection of Vestibular Schwannoma Growth Using a Two\u2010Dimensional <scp>U\u2010Net<\/scp> Convolutional Neural Network"],"prefix":"10.1002","volume":"131","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6333-2385","authenticated-orcid":false,"given":"Nicholas A.","family":"George\u2010Jones","sequence":"first","affiliation":[{"name":"Department of Otolaryngology\u2013Head and Neck Surgery University of Texas Southwestern Medical Center  Dallas Texas"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacob B.","family":"Hunter","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2013Head and Neck Surgery University of Texas Southwestern Medical Center  Dallas Texas"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2020,4,18]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1308\/rcsann.2017.0217"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/00129492-200606000-00018"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/MAO.0000000000001219"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0194-5998(95)70101-X"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.wjorl.2016.10.002"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00234-009-0529-4"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/0194599818770413"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1177\/0194599812447766"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1749-4486.2010.02099.x"},{"key":"e_1_2_6_11_1","first-page":"459","article-title":"Using Bayesian tissue classification to improve the accuracy of vestibular schwannoma volume and growth measurement","volume":"23","author":"Vokurka EA","year":"2002","journal-title":"Am J Neuroradiol"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00405-018-4865-z"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"e_1_2_6_14_1","article-title":"U\u2010net: convolutional networks for biomedical image segmentation. 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