{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T12:09:06Z","timestamp":1791374946503,"version":"4.3.1"},"reference-count":48,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001826","name":"ZonMw","doi-asserted-by":"publisher","award":["10070012010006"],"award-info":[{"award-number":["10070012010006"]}],"id":[{"id":"10.13039\/501100001826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Vestibular schwannomas (VS) are benign intracranial tumors in the cerebellopontine angle. For most small\u2010sized and medium\u2010sized tumors, a wait\u2010and\u2010scan (W&amp;S) approach is employed, since active treatment does not necessarily lead to symptom alleviation and up to 60% of VS tumors remain stable or regress during their natural course. At diagnosis, it is currently not possible to reliably predict tumor behavior and whether the tumor will grow or not, although some studies employing dynamic contrast\u2010enhanced (DCE) magnetic resonance imaging (MRI) or artificial intelligence (AI) suggest potential evidence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>\n                      Personalized clinical decision\u2010making for VS patients may be improved if tumor growth can be predicted. This study therefore prospectively investigates the use of both structural T2\u2010weighted MRI and DCE\u2010derived microvascular biomarker maps (K\n                      <jats:sup>trans<\/jats:sup>\n                      , v\n                      <jats:sub>e<\/jats:sub>\n                      , and v\n                      <jats:sub>p<\/jats:sub>\n                      ) in combination with machine learning techniques for the short\u2010term prognosis of VS.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Newly diagnosed patients with unilateral sporadic VS and considered for an initial W&amp;S strategy were enrolled between January 2021 and January 2023. Study participants underwent MR imaging, including both T2\u2010weighted and DCE\u2010imaging, within 3\u20136 months after diagnosis and were followed\u2010up according to the usual standard\u2010of\u2010care regimen. The DCE\u2010derived parameter maps (K\n                      <jats:sup>trans<\/jats:sup>\n                      , v\n                      <jats:sub>e<\/jats:sub>\n                      , and v\n                      <jats:sub>p<\/jats:sub>\n                      ) were calculated using established pipelines. Radiomic features are extracted from the structural MR images and the three DCE\u2010derived parameter maps. The resulting feature vector is reduced in dimensionality, using an\n                      <jats:italic>F<\/jats:italic>\n                      test and principal component analysis (PCA). A support vector machine (SVM) model is subsequently trained for the classification of tumor growth. The results of the\n                      <jats:italic>F<\/jats:italic>\n                      test are employed to assess the predictive value of each calculated feature.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 110 patients are analyzed for this study, of which 70 (64%) exhibited growth during follow\u2010up. After fivefold cross\u2010validation, the SVM model yields a mean accuracy of 89.0%, sensitivity of 90.0%, specificity of 87.7%, and AUC of 0.89 at the optimized cut\u2010off probability for the prediction of tumor growth. The K\n                      <jats:sup>trans<\/jats:sup>\n                      and v\n                      <jats:sub>e<\/jats:sub>\n                      parameter maps provide most of the features to the model, comprising mostly of complex radiomics, as opposed to first\u2010order statistics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The developed model demonstrates the high potential of the combination of machine learning techniques and dynamic MRI for the prediction of tumor growth in VS patients. The DCE\u2010derived parameters show high predictive value and provide insight into possible links between tumor biology and growth mechanisms. Complex radiomic features have revealed to be superior to first\u2010order statistics as predictors for VS tumor growth, while preserving a degree of model explainability. Prior to clinical implementation, the reproducibility of the radiomics and model itself should be externally validated.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70224","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T08:55:58Z","timestamp":1766998558000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Computer\u2010aided prediction of growth in vestibular schwannomas based on both structural and dynamic contrast\u2010enhanced MR imaging"],"prefix":"10.1002","volume":"53","author":[{"given":"Stefan","family":"Cornelissen","sequence":"first","affiliation":[{"name":"Gamma Knife Center Department of Neurosurgery Elisabeth\u2010TweeSteden Hospital  Tilburg The Netherlands"},{"name":"Department of Electrical Engineering Eindhoven University of Technology  Eindhoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sammy M.","family":"Schouten","sequence":"additional","affiliation":[{"name":"Gamma Knife Center Department of Neurosurgery Elisabeth\u2010TweeSteden Hospital  Tilburg The Netherlands"},{"name":"Department of Otolaryngology Radboud University Medical Center  Nijmegen The Netherlands"},{"name":"Department of Otolaryngology Maastricht University Medical Centre+  Maastricht The Netherlands"},{"name":"Dutch Academic Alliance Skull Base Pathology Radboud University Medical Center\/Maastricht University Medical Centre+  Nijmegen\/Maastricht The Netherlands"},{"name":"Mental Health &amp; Neuroscience Research Institute Maastricht University  Maastricht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Lewis","sequence":"additional","affiliation":[{"name":"Geoffrey Jefferson Brain Research Centre University of Manchester  Manchester UK"},{"name":"Division of Cancer Sciences University of Manchester  Manchester UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ka\u2010Loh","family":"Li","sequence":"additional","affiliation":[{"name":"Geoffrey Jefferson Brain Research Centre University of Manchester  Manchester UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoping","family":"Zhu","sequence":"additional","affiliation":[{"name":"Geoffrey Jefferson Brain Research Centre University of Manchester  Manchester UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marnix C.","family":"Maas","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Radboud University Medical Center  Nijmegen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sjoert","family":"Pegge","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Radboud University Medical Center  Nijmegen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thijs T. G.","family":"Jansen","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology Radboud University Medical Center  Nijmegen The Netherlands"},{"name":"Dutch Academic Alliance Skull Base Pathology Radboud University Medical Center\/Maastricht University Medical Centre+  Nijmegen\/Maastricht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jef J. S.","family":"Mulder","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology Radboud University Medical Center  Nijmegen The Netherlands"},{"name":"Dutch Academic Alliance Skull Base Pathology Radboud University Medical Center\/Maastricht University Medical Centre+  Nijmegen\/Maastricht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick P. J. H.","family":"Langenhuizen","sequence":"additional","affiliation":[{"name":"Gamma Knife Center Department of Neurosurgery Elisabeth\u2010TweeSteden Hospital  Tilburg The Netherlands"},{"name":"Department of Electrical Engineering Eindhoven University of Technology  Eindhoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew T.","family":"King","sequence":"additional","affiliation":[{"name":"Geoffrey Jefferson Brain Research Centre University of Manchester  Manchester UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeroen B.","family":"Verheul","sequence":"additional","affiliation":[{"name":"Gamma Knife Center Department of Neurosurgery Elisabeth\u2010TweeSteden Hospital  Tilburg The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henricus P. M.","family":"Kunst","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology Radboud University Medical Center  Nijmegen The Netherlands"},{"name":"Department of Otolaryngology Maastricht University Medical Centre+  Maastricht The Netherlands"},{"name":"Dutch Academic Alliance Skull Base Pathology Radboud University Medical Center\/Maastricht University Medical Centre+  Nijmegen\/Maastricht The Netherlands"},{"name":"Mental Health &amp; Neuroscience Research Institute Maastricht University  Maastricht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter H. N.","family":"De With","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Eindhoven University of Technology  Eindhoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/01945998211042006"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra2020394"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/0194599815590105"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/noajnl\/vdad144"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/MAO.0000000000003457"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/MAO.0000000000001914"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1227\/NEU.0000000000001414"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1097\/00129492\u2010200606000\u201000018"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/neuonc\/noab303"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/neuonc\/noz153"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/coa.13661"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/lary.30516"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16213718"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/neuonc\/noae252"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1097\/MAO.0000000000003043"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.wneu.2022.07.058"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/neuonc\/noy177"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010021\u201008517\u20101"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.3171\/2020.3.JNS193230"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11604\u2010021\u201001245\u2010y"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1522\u20102586(199909)10:3<223::AID\u2010JMRI2>3.0.CO;2\u2010S"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13678"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1097\/MAO.0000000000002886"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14042"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.10.041"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1097\/MAO.0000000000002938"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.015"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1101\/2024.03.15.24304080"},{"issue":"85","key":"e_1_2_8_30_1","first-page":"2825","article-title":"Scikit\u2010learn: machine learning in python","volume":"12","author":"Pedregosa F","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1158\/0008\u20105472.CAN\u201017\u20100339"},{"key":"e_1_2_8_32_1","doi-asserted-by":"crossref","unstructured":"ZwanenburgA LegerS Valli\u00e8resM L\u00f6ckS.Image biomarker standardisation initiative. Published online2016. doi:10.1148\/radiol.2020191145","DOI":"10.1148\/radiol.2020191145"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2007.12.031"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.1093\/JAMIA\/OCZ228"},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X06295361"},{"key":"e_1_2_8_36_1","doi-asserted-by":"publisher","DOI":"10.1227\/neu.0000000000003170"},{"key":"e_1_2_8_37_1","doi-asserted-by":"publisher","DOI":"10.1002\/lary.29309"},{"key":"e_1_2_8_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00405\u2010024\u201008540\u20100"},{"key":"e_1_2_8_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00405\u2010023\u201008410\u20101"},{"key":"e_1_2_8_40_1","doi-asserted-by":"publisher","DOI":"10.1055\/s\u20100042\u20101743613"},{"key":"e_1_2_8_41_1","doi-asserted-by":"publisher","DOI":"10.3171\/2021.2.JNS203609"},{"key":"e_1_2_8_42_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010021\u201095022\u20106"},{"key":"e_1_2_8_43_1","doi-asserted-by":"publisher","DOI":"10.1097\/MAO.0b013e31827c9fbf"},{"key":"e_1_2_8_44_1","doi-asserted-by":"publisher","DOI":"10.1097\/MAO.0000000000001304"},{"key":"e_1_2_8_45_1","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2024.1365727"},{"key":"e_1_2_8_46_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11020369"},{"key":"e_1_2_8_47_1","doi-asserted-by":"crossref","unstructured":"ShiriI HajianfarG SohrabiA et\u00a0al.Repeatability of Radiomic Features in Magnetic Resonance Imaging of Glioblastoma: Test\u2013Retest and Image Registration Analyses. Vol47.;2020. doi:10.1002\/mp.14368","DOI":"10.1002\/mp.14368"},{"key":"e_1_2_8_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2019.03.001"},{"key":"e_1_2_8_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010024\u201010970\u20107"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.70224","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mp.70224","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.70224","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T01:55:28Z","timestamp":1773885328000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.70224"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,29]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1002\/mp.70224"],"URL":"https:\/\/doi.org\/10.1002\/mp.70224","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,29]]},"assertion":[{"value":"2025-04-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-03","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70224"}}