{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T01:32:38Z","timestamp":1794274358392,"version":"4.9.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Neurol"],"DOI":"10.1186\/s12883-025-04093-9","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T10:01:35Z","timestamp":1758880895000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine learning-based models in prediction of the radiological outcomes of vestibular schwannoma following stereotactic radiosurgery: a systematic review and meta-analysis"],"prefix":"10.1186","volume":"25","author":[{"given":"Bardia","family":"Hajikarimloo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Ali","family":"Nazari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Amin","family":"Habibi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pourya","family":"Taghipour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyyed-Ali","family":"Alaei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amirreza","family":"Khalaji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rana","family":"Hashemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Mohammadzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salem M.","family":"Tos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"4093_CR1","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1097\/MAO.0000000000002542","volume":"41","author":"VC Pandrangi","year":"2020","unstructured":"Pandrangi VC, Han AY, Alonso JE, Peng KA, St John MA. An update on epidemiology and management trends of vestibular schwannomas. Otol Neurotol. 2020;41:411\u20137.","journal-title":"Otol Neurotol"},{"key":"4093_CR2","doi-asserted-by":"publisher","first-page":"110905","DOI":"10.1016\/j.jocn.2024.110905","volume":"130","author":"B Hajikarimloo","year":"2024","unstructured":"Hajikarimloo B, Habibi MA, Sabbagh Alvani M, Zare AH, Tos SM, Sheehan JP. Repeat stereotactic radiosurgery in vestibular schwannoma patients: a systematic review and meta-analysis. J Clin Neurosci. 2024;130:110905.","journal-title":"J Clin Neurosci"},{"key":"4093_CR3","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1227\/neu.0000000000002354","volume":"92","author":"A Balossier","year":"2023","unstructured":"Balossier A, Tuleasca C, Delsanti C, Troude L, Thomassin J-M, Roche P-H, et al. Long-term hearing outcome after radiosurgery for vestibular schwannoma: a systematic review and meta-analysis. Neurosurgery. 2023;92:1130\u201341.","journal-title":"Neurosurgery"},{"key":"4093_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2024.04.004","author":"O Bin-Alamer","year":"2024","unstructured":"Bin-Alamer O, Abou-Al-Shaar H, Peker S, Samanci Y, Pelcher I, Begley S, et al. Vestibular schwannoma international study of active surveillance versus stereotactic radiosurgery: the VISAS study. Int J Radiat Oncol Biol Phys. 2024. https:\/\/doi.org\/10.1016\/j.ijrobp.2024.04.004","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"4093_CR5","doi-asserted-by":"publisher","first-page":"934","DOI":"10.1227\/neu.0000000000002436","volume":"92","author":"O Bin-Alamer","year":"2023","unstructured":"Bin-Alamer O, Faramand A, Alarifi NA, Wei Z, Mallela AN, Lu VM, et al. Stereotactic radiosurgery for vestibular schwannoma in neurofibromatosis type 2: an international multicenter case series of response and malignant transformation risk. Neurosurgery. 2023;92:934\u201344.","journal-title":"Neurosurgery"},{"key":"4093_CR6","doi-asserted-by":"publisher","first-page":"3177","DOI":"10.1007\/s10143-021-01528-y","volume":"44","author":"A Balossier","year":"2021","unstructured":"Balossier A, R\u00e9gis J, Reyns N, Roche P-H, Daniel RT, George M, et al. Repeat stereotactic radiosurgery for progressive vestibular schwannomas after previous radiosurgery: a systematic review and meta-analysis. Neurosurg Rev. 2021;44:3177\u201388.","journal-title":"Neurosurg Rev"},{"key":"4093_CR7","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1097\/MAO.0000000000001536","volume":"38","author":"H-J Lee","year":"2017","unstructured":"Lee H-J, Kim MJ, Koh SH, Chang WS, Moon IS. Comparing outcomes following salvage microsurgery in vestibular schwannoma patients failing gamma-knife radiosurgery or microsurgery. Otol Neurotol. 2017;38:1339\u201344.","journal-title":"Otol Neurotol"},{"key":"4093_CR8","doi-asserted-by":"publisher","first-page":"e1321","DOI":"10.1097\/MAO.0000000000002886","volume":"41","author":"PPJH Langenhuizen","year":"2020","unstructured":"Langenhuizen PPJH, Zinger S, Leenstra S, Kunst HPM, Mulder JJS, Hanssens PEJ, et al. Radiomics-based prediction of long-term treatment response of vestibular schwannomas following stereotactic radiosurgery. Otol Neurotol. 2020;41:e1321\u20137.","journal-title":"Otol Neurotol"},{"key":"4093_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.wneu.2024.10.089","author":"B Hajikarimloo","year":"2024","unstructured":"Hajikarimloo B, Tos SM, Alvani MS, Rafiei MA, Akbarzadeh D, ShahirEftekhar M, et al. Application of artificial intelligence in prediction of Ki-67 index in meningiomas: a systematic review and meta-analysis. World Neurosurg. 2024. https:\/\/doi.org\/10.1016\/j.wneu.2024.10.089","journal-title":"World Neurosurg"},{"key":"4093_CR10","doi-asserted-by":"crossref","unstructured":"Hajikarimloo B, Habibi MA, Alvani MS, Meinagh SO, Kooshki A, Afkhami-Ardakani O et al. Machine learning-based models for prediction of survival in medulloblastoma: a systematic review and meta-analysis. Neurol Sci. 2025;46:689\u201396.","DOI":"10.1007\/s10072-024-07879-w"},{"key":"4093_CR11","first-page":"21","volume":"2","author":"N Raissi Dehkordi","year":"2024","unstructured":"Raissi Dehkordi N, Raissi Dehkordi N, Karimi Toudeshki K, Farjoo MH. Artificial intelligence in diagnosis of long QT syndrome: a review of current state, challenges, and future perspectives. Mayo Clin Proceedings: Digit Health. 2024;2:21\u201331.","journal-title":"Mayo Clin Proceedings: Digit Health"},{"key":"4093_CR12","doi-asserted-by":"crossref","unstructured":"Bossi Zanetti I, De Martin E, Pascuzzo R, D\u2019Amico NC, Morlino S, Cane I et al. Development of predictive models for the response of vestibular schwannoma treated with Cyberknife\u00ae: a feasibility study based on radiomics and machine learning. J Pers Med. 2023;13.","DOI":"10.3390\/jpm13050808"},{"key":"4093_CR13","doi-asserted-by":"crossref","unstructured":"DrAmico NC, Sicilia R, Cordelli E, Valbusa G, Grossi E, Zanetti IB et al. Radiomics for Predicting CyberKnife response in acoustic neuroma: a pilot study. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE; 2018.","DOI":"10.1109\/BIBM.2018.8621276"},{"key":"4093_CR14","doi-asserted-by":"publisher","first-page":"e348","DOI":"10.1097\/MAO.0000000000002938","volume":"42","author":"NA George-Jones","year":"2021","unstructured":"George-Jones NA, Wang K, Wang J, Hunter JB. Prediction of vestibular schwannoma enlargement after radiosurgery using tumor shape and MRI texture features. Otol Neurotol. 2021;42:e348\u201354.","journal-title":"Otol Neurotol"},{"key":"4093_CR15","doi-asserted-by":"crossref","unstructured":"Langenhuizen P, Legters M, Zinger S, Verheul J, de With PN, Leenstra S. MRI textures as outcome predictor for gamma knife radiosurgery on vestibular schwannoma. In: Mori K, Petrick N, editorsMedical Imaging. 2018: Computer-Aided Diagnosis. SPIE; 2018.","DOI":"10.1117\/12.2293464"},{"key":"4093_CR16","doi-asserted-by":"crossref","unstructured":"Langenhuizen P, van Gorp H, Zinger S, Verheul J, Leenstra S, de With PHN. Dose distribution as outcome predictor for gamma knife radiosurgery on vestibular schwannoma. In: Hahn HK, Mori K, editorsMedical Imaging. 2019: Computer-Aided Diagnosis. SPIE; 2019.","DOI":"10.1117\/12.2512472"},{"key":"4093_CR17","doi-asserted-by":"publisher","first-page":"1692","DOI":"10.1002\/mp.14042","volume":"47","author":"PPJH Langenhuizen","year":"2020","unstructured":"Langenhuizen PPJH, Sebregts SHP, Zinger S, Leenstra S, Verheul JB, de With PHN. Prediction of transient tumor enlargement using MRI tumor texture after radiosurgery on vestibular schwannoma. Med Phys. 2020;47:1692\u2013701.","journal-title":"Med Phys"},{"key":"4093_CR18","doi-asserted-by":"publisher","first-page":"3615","DOI":"10.1002\/cam4.955","volume":"5","author":"S Lee","year":"2016","unstructured":"Lee S, Seo S-W, Hwang J, Seol HJ, Nam D-H, Lee J-I, et al. Analysis of risk factors to predict communicating hydrocephalus following gamma knife radiosurgery for intracranial schwannoma. Cancer Med. 2016;5:3615\u201321.","journal-title":"Cancer Med"},{"key":"4093_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5430\/jst.v9n2p1","volume":"9","author":"G Narayanasamy","year":"2019","unstructured":"Narayanasamy G, Zhang G, Siegel E, Campbell G, Moros EG, Galhardo EP, et al. Radiomic assessment of the progression of acoustic neuroma after gamma knife stereotactic radiosurgery. J Solid Tumors. 2019;9:1.","journal-title":"J Solid Tumors"},{"key":"4093_CR20","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.radonc.2020.10.041","volume":"155","author":"H-C Yang","year":"2021","unstructured":"Yang H-C, Wu C-C, Lee C-C, Huang H-E, Lee W-K, Chung W-Y, et al. Prediction of pseudoprogression and long-term outcome of vestibular schwannoma after gamma knife radiosurgery based on preradiosurgical MR radiomics. Radiother Oncol. 2021;155:123\u201330.","journal-title":"Radiother Oncol"},{"key":"4093_CR21","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1227\/neu.0000000000002757","volume":"94","author":"YM Zohdy","year":"2024","unstructured":"Zohdy YM, Alawieh AM, Bray D, Pradilla G, Garzon-Muvdi T, Ashram YA. An artificial neural network model for predicting postoperative facial nerve outcomes after vestibular schwannoma surgery. Neurosurgery. 2024;94:805\u201312.","journal-title":"Neurosurgery"},{"key":"4093_CR22","doi-asserted-by":"publisher","first-page":"e60","DOI":"10.1016\/j.wneu.2023.03.090","volume":"176","author":"Y Yu","year":"2023","unstructured":"Yu Y, Song G, Zhao Y, Liang J, Liu Q. Prediction of vestibular schwannoma surgical outcome using deep neural network. World Neurosurg. 2023;176:e60\u20137.","journal-title":"World Neurosurg"},{"key":"4093_CR23","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1227\/neu.0000000000001998","volume":"91","author":"OY Tang","year":"2022","unstructured":"Tang OY, Bajaj AI, Zhao K, Rivera Perla KM, Ying Y-LM, Jyung RW, et al. Association of patient frailty with vestibular schwannoma resection outcomes and machine learning development of a vestibular schwannoma risk stratification score. Neurosurgery. 2022;91:312\u201321.","journal-title":"Neurosurgery"},{"key":"4093_CR24","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s11060-024-04844-7","volume":"171","author":"L Przepiorka","year":"2025","unstructured":"Przepiorka L, Kujawski S, W\u00f3jtowicz K, Maj E, Marchel A, Kunert P. Development and application of explainable artificial intelligence using machine learning classification for long-term facial nerve function after vestibular schwannoma surgery. J Neurooncol. 2025;171:165\u201377.","journal-title":"J Neurooncol"},{"key":"4093_CR25","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.","journal-title":"BMJ"},{"key":"4093_CR26","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155:529\u201336.","journal-title":"Ann Intern Med"},{"key":"4093_CR27","doi-asserted-by":"publisher","first-page":"1785","DOI":"10.1177\/0962280216669183","volume":"27","author":"D Luo","year":"2018","unstructured":"Luo D, Wan X, Liu J, Tong T. Optimally estimating the sample mean from the sample size, median, mid-range, and\/or mid-quartile range. Stat Methods Med Res. 2018;27:1785\u2013805.","journal-title":"Stat Methods Med Res"},{"key":"4093_CR28","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1097\/ALN.0000000000003871","volume":"135","author":"SJ Staffa","year":"2021","unstructured":"Staffa SJ, Zurakowski D. Statistical development and validation of clinical prediction models. Anesthesiology. 2021;135:396\u2013405.","journal-title":"Anesthesiology"},{"key":"4093_CR29","doi-asserted-by":"publisher","first-page":"817071","DOI":"10.3389\/fneur.2021.817071","volume":"12","author":"Y Sun","year":"2021","unstructured":"Sun Y, Yang J, Li T, Gao K, Tong X. Nomogram for predicting facial nerve outcomes after surgical resection of vestibular schwannoma. Front Neurol. 2021;12:817071.","journal-title":"Front Neurol"},{"key":"4093_CR30","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.3171\/2024.4.JNS232208","volume":"141","author":"MK Harris","year":"2024","unstructured":"Harris MK, Macielak RJ, Kaul VF, Zhang L, Moshtaghi O, Gao TZ, et al. A nomogram to predict long-term facial nerve function after vestibular schwannoma resection: a contemporary multi-institutional study. J Neurosurg. 2024;141:1667\u201374.","journal-title":"J Neurosurg"},{"key":"4093_CR31","doi-asserted-by":"publisher","first-page":"e14526","DOI":"10.1111\/cns.14526","volume":"30","author":"X Shi","year":"2024","unstructured":"Shi X, Liu Y, Zhang Z, Tao B, Zhang D, Jiang Q, et al. The value of radiographic features in predicting postoperative facial nerve function in vestibular schwannoma patients: a retrospective study and nomogram analysis. CNS Neurosci Ther. 2024;30:e14526.","journal-title":"CNS Neurosci Ther"},{"key":"4093_CR32","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1111\/cge.14200","volume":"102","author":"H Zheng","year":"2022","unstructured":"Zheng H, Yan T, Han Y, Wang Q, Zhang G, Zhang L, et al. Nomograms for prognostic risk assessment in glioblastoma multiforme: applications and limitations. Clin Genet. 2022;102:359\u201368.","journal-title":"Clin Genet"},{"key":"4093_CR33","doi-asserted-by":"publisher","first-page":"114694","DOI":"10.1016\/j.jenvman.2022.114694","volume":"309","author":"C Guo","year":"2022","unstructured":"Guo C, Cui Y. Machine learning exhibited excellent advantages in the performance simulation and prediction of free water surface constructed wetlands. J Environ Manage. 2022;309:114694.","journal-title":"J Environ Manage"},{"key":"4093_CR34","doi-asserted-by":"publisher","first-page":"111269","DOI":"10.1016\/j.asoc.2024.111269","volume":"153","author":"M Liu","year":"2024","unstructured":"Liu M, Guo C, Xu L. An interpretable automated feature engineering framework for improving logistic regression. Appl Soft Comput. 2024;153:111269.","journal-title":"Appl Soft Comput"},{"key":"4093_CR35","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1016\/j.ejor.2021.06.053","volume":"297","author":"E Dumitrescu","year":"2022","unstructured":"Dumitrescu E, Hu\u00e9 S, Hurlin C, Tokpavi S. Machine learning for credit scoring: improving logistic regression with non-linear decision-tree effects. Eur J Oper Res. 2022;297:1178\u201392.","journal-title":"Eur J Oper Res"},{"key":"4093_CR36","doi-asserted-by":"publisher","first-page":"3597051","DOI":"10.1155\/2021\/3597051","volume":"2021","author":"C Huang","year":"2021","unstructured":"Huang C. Feature selection and feature stability measurement method for high-dimensional small sample data based on big data technology. Comput Intell Neurosci. 2021;2021:3597051.","journal-title":"Comput Intell Neurosci"},{"key":"4093_CR37","doi-asserted-by":"crossref","unstructured":"Saravanan K, Prakash RB, Balakrishnan C, Kumar GVP, Siva Subramanian R, Anita M. Support vector machines: Unveiling the power and versatility of SVMs in modern machine learning. In: 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE; 2023. pp. 680\u20137.","DOI":"10.1109\/ICIMIA60377.2023.10426542"},{"key":"4093_CR38","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1093\/pan\/mpv024","volume":"24","author":"D Muchlinski","year":"2016","unstructured":"Muchlinski D, Siroky D, He J, Kocher M. Comparing random forest with logistic regression for predicting class-imbalanced civil war onset data. Polit Anal. 2016;24:87\u2013103.","journal-title":"Polit Anal"},{"key":"4093_CR39","doi-asserted-by":"crossref","unstructured":"Ogutu JO, Piepho H-P, Schulz-Streeck T. A comparison of random forests, boosting and support vector machines for genomic selection. BMC Proc. 2011;5 Suppl 3:S11.","DOI":"10.1186\/1753-6561-5-S3-S11"},{"key":"4093_CR40","doi-asserted-by":"publisher","first-page":"40","DOI":"10.3390\/app14010040","volume":"14","author":"D Huang","year":"2023","unstructured":"Huang D, Liu Z, Wu D. Research on ensemble learning-based feature selection method for time-series prediction. Appl Sci (Basel). 2023;14:40.","journal-title":"Appl Sci (Basel)"},{"key":"4093_CR41","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.ejmp.2021.03.009","volume":"83","author":"P Papadimitroulas","year":"2021","unstructured":"Papadimitroulas P, Brocki L, Christopher Chung N, Marchadour W, Vermet F, Gaubert L, et al. Artificial intelligence: deep learning in oncological radiomics and challenges of interpretability and data harmonization. Phys Med. 2021;83:108\u201321.","journal-title":"Phys Med"}],"container-title":["BMC Neurology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12883-025-04093-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12883-025-04093-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12883-025-04093-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T10:01:38Z","timestamp":1758880898000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcneurol.biomedcentral.com\/articles\/10.1186\/s12883-025-04093-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,26]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["4093"],"URL":"https:\/\/doi.org\/10.1186\/s12883-025-04093-9","relation":{},"ISSN":["1471-2377"],"issn-type":[{"value":"1471-2377","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,26]]},"assertion":[{"value":"19 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study is deemed exempt from receiving ethical approval.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"385"}}