{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T22:44:27Z","timestamp":1782600267626,"version":"3.54.5"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Health (RRC)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Purpose: to predict vestibular schwannoma (VS) response to radiosurgery by applying machine learning (ML) algorithms on radiomic features extracted from pre-treatment magnetic resonance (MR) images. Methods: patients with VS treated with radiosurgery in two Centers from 2004 to 2016 were retrospectively evaluated. Brain T1-weighted contrast-enhanced MR images were acquired before and at 24 and 36 months after treatment. Clinical and treatment data were collected contextually. Treatment responses were assessed considering the VS volume variation based on pre- and post-radiosurgery MR images at both time points. Tumors were semi-automatically segmented and radiomic features were extracted. Four ML algorithms (Random Forest, Support Vector Machine, Neural Network, and extreme Gradient Boosting) were trained and tested for treatment response (i.e., increased or non-increased tumor volume) using nested cross-validation. For training, feature selection was performed using the Least Absolute Shrinkage and Selection Operator, and the selected features were used as input to separately build the four ML classification algorithms. To overcome class imbalance during training, Synthetic Minority Oversampling Technique was used. Finally, trained models were tested on the corresponding held out set of patients to evaluate balanced accuracy, sensitivity, and specificity. Results: 108 patients treated with Cyberknife\u00ae were retrieved; an increased tumor volume was observed at 24 months in 12 patients, and at 36 months in another group of 12 patients. The Neural Network was the best predictive algorithm for response at 24 (balanced accuracy 73% \u00b1 18%, specificity 85% \u00b1 12%, sensitivity 60% \u00b1 42%) and 36 months (balanced accuracy 65% \u00b1 12%, specificity 83% \u00b1 9%, sensitivity 47% \u00b1 27%). Conclusions: radiomics may predict VS response to radiosurgery avoiding long-term follow-up as well as unnecessary treatment.<\/jats:p>","DOI":"10.3390\/jpm13050808","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T03:32:27Z","timestamp":1683689547000},"page":"808","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Development of Predictive Models for the Response of Vestibular Schwannoma Treated with Cyberknife\u00ae: A Feasibility Study Based on Radiomics and Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6756-4749","authenticated-orcid":false,"given":"Isa","family":"Bossi Zanetti","sequence":"first","affiliation":[{"name":"Department of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano S.p.A., Via Saint Bon 20, 20147 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"De Martin","sequence":"additional","affiliation":[{"name":"Health Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6555-1784","authenticated-orcid":false,"given":"Riccardo","family":"Pascuzzo","sequence":"additional","affiliation":[{"name":"Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natascha Claudia","family":"D\u2019Amico","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano S.p.A., Via Saint Bon 20, 20147 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3249-766X","authenticated-orcid":false,"given":"Sara","family":"Morlino","sequence":"additional","affiliation":[{"name":"Radiotherapy Unit, Neurosurgery Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irene","family":"Cane","sequence":"additional","affiliation":[{"name":"Radiotherapy Unit, Neurosurgery Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1289-9573","authenticated-orcid":false,"given":"Domenico","family":"Aquino","sequence":"additional","affiliation":[{"name":"Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Al\u00ec","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano S.p.A., Via Saint Bon 20, 20147 Milan, Italy"},{"name":"Bracco Imaging S.p.A., Via Caduti di Marcinelle 13, Milan 20134, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7401-1971","authenticated-orcid":false,"given":"Michaela","family":"Cellina","sequence":"additional","affiliation":[{"name":"Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, Milan 2021, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giancarlo","family":"Beltramo","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano S.p.A., Via Saint Bon 20, 20147 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Fariselli","sequence":"additional","affiliation":[{"name":"Radiotherapy Unit, Neurosurgery Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"ref_1","first-page":"e002354","article-title":"Long-term hearing outcome after radiosurgery for vestibular schwannoma: A systematic review and meta-analysis","volume":"3","author":"Balossier","year":"2023","journal-title":"Neurosurgery"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1093\/neuonc\/noz153","article-title":"EANO guideline on the diagnosis and treatment of vestibular schwannoma","volume":"22","author":"Goldbrunner","year":"2020","journal-title":"Neuro-Oncology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1017\/S0022215118001342","article-title":"Non-contrast magnetic resonance imaging for monitoring patients with acoustic neuroma","volume":"132","author":"Forgues","year":"2018","journal-title":"J. Laryngol. Otol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e001345","DOI":"10.1136\/bmjopen-2012-001345","article-title":"What intervention is best practice for vestibular schwannomas? A systematic review of controlled studies","volume":"3","author":"Wolbers","year":"2013","journal-title":"BMJ Open"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.amjoto.2016.09.019","article-title":"Development of an evidence-based decision pathway for vestibular schwannoma treatment options","volume":"38","author":"Linkov","year":"2017","journal-title":"Am. J. Otolaryngol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e194","DOI":"10.1016\/j.wneu.2018.04.149","article-title":"(Acoustic neuroma treated with stereotactic radiosurgery: Follow-up of 335 patients","volume":"116","author":"Hellerbach","year":"2018","journal-title":"World Neurosurg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1007\/s10143-020-01279-2","article-title":"Stereotactic radiosurgery for management of vestibular schwannoma: A short review","volume":"44","author":"Buss","year":"2021","journal-title":"Neurosurg. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"123","DOI":"10.3171\/2014.8.GKS141506","article-title":"Gamma Knife radiosurgery for vestibular schwannoma: Clinical results at long-term follow-up in a series of 379 patients","volume":"121","author":"Boari","year":"2014","journal-title":"J. Neurosurg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.jocn.2019.01.046","article-title":"CyberKnife radiosurgery for acoustic neuromas: Tumor control and clinical outcomes","volume":"63","author":"Przybylowski","year":"2019","journal-title":"J. Clin. Neurosci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1007\/s00405-009-1140-3","article-title":"Quality of life after gamma knife radiosurgery treatment in patients with a vestibular schwannoma: The patient\u2019s perspective","volume":"267","author":"Timmer","year":"2010","journal-title":"Eur. Arch. Oto-Rhino-Laryngol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"855","DOI":"10.3171\/2008.10.JNS081014","article-title":"Prospective comparison of quality of life before and after observation, radiation, or surgery for vestibular schwannomas","volume":"111","author":"Akagami","year":"2009","journal-title":"J. Neurosurg."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1007\/s10143-021-01604-3","article-title":"Is salvage surgery for large vestibular schwannomas after failed gamma knife radiosurgery more challenging?","volume":"45","author":"Troude","year":"2022","journal-title":"Neurosurg. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.ymeth.2020.06.003","article-title":"Radiomics in neuro-oncology: Basics, workflow, and applications","volume":"188","author":"Lohmann","year":"2021","journal-title":"Methods"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s13244-021-01102-6","article-title":"Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging","volume":"12","author":"Alksas","year":"2021","journal-title":"Insights Into Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1007\/s00066-020-01626-8","article-title":"Applications of radiomics and machine learning for radiotherapy of malignant brain tumors","volume":"196","author":"Kocher","year":"2020","journal-title":"Strahlenther. Und Onkol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e1321","DOI":"10.1097\/MAO.0000000000002886","article-title":"Radiomics-based prediction of long-term treatment response of vestibular schwannomas following stereotactic radiosurgery","volume":"41","author":"Langenhuizen","year":"2020","journal-title":"Otol. Neurotol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1038\/s41598-021-82665-8","article-title":"Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery","volume":"11","author":"Lee","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.radonc.2020.10.041","article-title":"Prediction of pseudoprogression and long-term outcome of vestibular schwannoma after Gamma Knife radiosurgery based on preradiosurgical MR radiomics","volume":"155","author":"Yang","year":"2021","journal-title":"Radiother. Oncol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5430\/jst.v9n2p1","article-title":"Radiomic assessment of the progression of acoustic neuroma after gamma knife stereotactic radiosurgery","volume":"9","author":"Narayanasamy","year":"2019","journal-title":"J. Solid Tumors"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.1007\/s00330-017-5165-5","article-title":"To share or not to share? Expected pros and cons of data sharing in radiological research","volume":"28","author":"Sardanelli","year":"2018","journal-title":"Eur. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"(1995). Committee on Hearing and Equilibrium guidelines for the evaluation of hearing preservation in acoustic neuroma (vestibular schwannoma). American Academy of Otolaryngology-Head and Neck Surgery Foundation, INC. Otolaryngol. Head Neck Surg., 113, 179\u2013180.","DOI":"10.1016\/S0194-5998(95)70101-X"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","article-title":"3D Slicer as an image computing platform for the Quantitative Imaging Network","volume":"30","author":"Fedorov","year":"2012","journal-title":"Magn. Reson. Imaging"},{"key":"ref_23","unstructured":"Juntu, J., Sijbers, J., Dyck, D.V., and Gielen, J. (2005). Computer Recognition Systems, Springer."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1006\/nimg.2000.0582","article-title":"Voxel-based morphometry\u2014The methods","volume":"11","author":"Ashburner","year":"2000","journal-title":"Neuroimage"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"Fedorov","year":"2017","journal-title":"Cancer Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1016\/j.patcog.2011.01.015","article-title":"A multi-objective optimisation approach for class imbalance learning","volume":"44","author":"Soda","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, P., Liu, X., and Huang, Y. (2021). Data Augmentation for Medical MR Image Using Generative Adversarial Networks. arXiv.","DOI":"10.1155\/2021\/1230279"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1007\/s00330-017-5154-8","article-title":"A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images","volume":"28","author":"Zhang","year":"2018","journal-title":"Eur. Radiol."}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/13\/5\/808\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:31:46Z","timestamp":1760124706000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/13\/5\/808"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,10]]},"references-count":29,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["jpm13050808"],"URL":"https:\/\/doi.org\/10.3390\/jpm13050808","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,10]]}}}