{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T17:02:46Z","timestamp":1789578166716,"version":"build-2803163510"},"reference-count":39,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T00:00:00Z","timestamp":1670630400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["5T32DC000040\u201027"],"award-info":[{"award-number":["5T32DC000040\u201027"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>To develop a deep\u2010learning\u2010based multi\u2010task (DMT) model for joint tumor enlargement prediction (TEP) and automatic tumor segmentation (TS) for vestibular schwannoma (VS) patients using their initial diagnostic contrast\u2010enhanced T1\u2010weighted (ceT1) magnetic resonance images (MRIs).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Initial ceT1 MRIs for VS patients meeting the inclusion\/exclusion criteria of this study were retrospectively collected. VSs on the initial MRIs and their first follow\u2010up scans were manually contoured. Tumor volume and enlargement ratio were measured based on expert contours. A DMT model was constructed for jointly TS and TEP. The manually segmented VS volume on the initial scan and the tumor enlargement label (\u226520% volumetric growth) were used as the ground truth for training and evaluating the TS and TEP modules, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We performed 5\u2010fold cross\u2010validation with the eligible patients (n\u00a0=\u00a0103). Median segmentation dice coefficient, prediction sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were measured and achieved the following values: 84.20%, 0.68, 0.78, 0.72, and 0.77, respectively. The segmentation result is significantly better than the separate TS network (dice coefficient of 83.13%, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.03) and marginally lower than the state\u2010of\u2010the\u2010art segmentation model nnU\u2010Net (dice coefficient of 86.45%, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.16). The TEP performance is significantly better than the single\u2010task prediction model (AUC\u00a0=\u00a00.60, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.01) and marginally better than a radiomics\u2010based prediction model (AUC\u00a0=\u00a00.70, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.17).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed DMT model is of higher learning efficiency and achieves promising performance on TEP and TS. The proposed technology has the potential to improve VS patient management.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>NA <jats:italic>Laryngoscope<\/jats:italic>, 133:2754\u20132760, 2023<\/jats:p><\/jats:sec>","DOI":"10.1002\/lary.30516","type":"journal-article","created":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T15:00:05Z","timestamp":1670684405000},"page":"2754-2760","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi\u2010task Model"],"prefix":"10.1002","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2155-1445","authenticated-orcid":false,"given":"Kai","family":"Wang","sequence":"first","affiliation":[{"name":"The Department of Radiation Oncology University of Texas Southwestern Medical Center  Dallas Texas 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