{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T22:07:49Z","timestamp":1793743669612,"version":"4.6.1"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003792","name":"Hj\u00e4rnfonden","doi-asserted-by":"publisher","award":["2019-0006"],"award-info":[{"award-number":["2019-0006"]}],"id":[{"id":"10.13039\/501100003792","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["World Neurosurgery"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1016\/j.wneu.2023.08.023","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T23:37:45Z","timestamp":1691797065000},"page":"e119-e134","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":18,"special_numbering":"C","title":["Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis"],"prefix":"10.1016","volume":"179","author":[{"given":"Krish M.","family":"Maniar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0833-1093","authenticated-orcid":false,"given":"Philipp","family":"Lassar\u00e9n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aakanksha","family":"Rana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7623-9593","authenticated-orcid":false,"given":"Ishaan A.","family":"Tewarie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob V.E.","family":"Gerstl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camila M.","family":"Recio Blanco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3402-8429","authenticated-orcid":false,"given":"Liam H.","family":"Power","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4217-9742","authenticated-orcid":false,"given":"Marco","family":"Mammi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1263-2537","authenticated-orcid":false,"given":"Heather","family":"Mattie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy R.","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rania A.","family":"Mekary","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.wneu.2023.08.023_bib1","doi-asserted-by":"crossref","first-page":"iii1","DOI":"10.1093\/neuonc\/noab200","article-title":"CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2014-2018","volume":"23","author":"Ostrom","year":"2021","journal-title":"Neuro Oncol"},{"key":"10.1016\/j.wneu.2023.08.023_bib2","doi-asserted-by":"crossref","first-page":"i4","DOI":"10.1093\/neuonc\/noy178","article-title":"Molecular and translational advances in meningiomas","volume":"21","author":"Suppiah","year":"2019","journal-title":"Neuro Oncol"},{"key":"10.1016\/j.wneu.2023.08.023_bib3","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1002\/jmri.27378","article-title":"Machine learning in meningioma MRI: Past to present. A narrative review","volume":"55","author":"Neromyliotis","year":"2022","journal-title":"J\u00a0Magn Reson Imaging"},{"key":"10.1016\/j.wneu.2023.08.023_bib4","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1093\/neuonc\/noab150","article-title":"EANO guideline on the diagnosis and management of meningiomas","volume":"23","author":"Goldbrunner","year":"2021","journal-title":"Neuro Oncol"},{"key":"10.1016\/j.wneu.2023.08.023_bib5","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"10.1016\/j.wneu.2023.08.023_bib6","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1016\/j.cell.2018.05.015","article-title":"Next-Generation machine learning for Biological networks","volume":"173","author":"Camacho","year":"2018","journal-title":"Cell"},{"key":"10.1016\/j.wneu.2023.08.023_bib7","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine learning for medical imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"Radiographics"},{"key":"10.1016\/j.wneu.2023.08.023_bib8","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu Rev Biomed Eng"},{"key":"10.1016\/j.wneu.2023.08.023_bib9","doi-asserted-by":"crossref","first-page":"777","DOI":"10.7326\/M14-2385","article-title":"The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations","volume":"162","author":"Hutton","year":"2015","journal-title":"Ann Intern Med"},{"key":"10.1016\/j.wneu.2023.08.023_bib10","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann Intern Med"},{"key":"10.1016\/j.wneu.2023.08.023_bib11","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1002\/sim.2889","article-title":"Performance of the trim and fill method in the presence of publication bias and between-study heterogeneity","volume":"26","author":"Peters","year":"2007","journal-title":"Stat Med"},{"key":"10.1016\/j.wneu.2023.08.023_bib12","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","article-title":"Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput Sci"},{"key":"10.1016\/j.wneu.2023.08.023_bib13","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/0197-2456(86)90046-2","article-title":"Meta-analysis in clinical trials","volume":"7","author":"DerSimonian","year":"1986","journal-title":"Control Clin Trials"},{"key":"10.1016\/j.wneu.2023.08.023_bib14","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1258\/096914103321610806","article-title":"A\u00a0likelihood ratio approach to meta-analysis of diagnostic studies","volume":"10","author":"Stengel","year":"2003","journal-title":"J\u00a0Med Screen"},{"key":"10.1016\/j.wneu.2023.08.023_bib15","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s12880-015-0068-x","article-title":"Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool","volume":"15","author":"Taha","year":"2015","journal-title":"BMC Med Imaging"},{"key":"10.1016\/j.wneu.2023.08.023_bib16","doi-asserted-by":"crossref","first-page":"153","DOI":"10.4328\/ACAM.20226","article-title":"The efficiency of deep learning for the diagnosis of psammomatous meningioma","volume":"12","author":"Bukhari","year":"2021","journal-title":"J Clin Anal Med"},{"key":"10.1016\/j.wneu.2023.08.023_bib17","doi-asserted-by":"crossref","first-page":"939","DOI":"10.13005\/bpj\/1720","article-title":"Parasagittal meningioma brain tumor classification system based on Mri images and multi Phase level set formulation","volume":"12","author":"David","year":"2019","journal-title":"Biomed Pharmacol J"},{"key":"10.1016\/j.wneu.2023.08.023_bib18","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1186\/1471-2105-7-539","article-title":"A\u00a0minimally invasive multiple marker approach allows highly efficient detection of meningioma tumors","volume":"7","author":"Keller","year":"2006","journal-title":"BMC Bioinf"},{"key":"10.1016\/j.wneu.2023.08.023_bib44","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1007\/s12021-020-09492-6","article-title":"Deep learning model for the automated detection and histopathological prediction of meningioma","volume":"19","author":"Zhang","year":"2021","journal-title":"Neuroinformatics"},{"key":"10.1016\/j.wneu.2023.08.023_bib46","doi-asserted-by":"crossref","DOI":"10.1016\/j.clineuro.2020.105947","article-title":"Prediction value of preoperative findings on meningioma grading using artificial neural network","volume":"196","author":"Khayat Kashani","year":"2020","journal-title":"Clin Neurol Neurosurg"},{"key":"10.1016\/j.wneu.2023.08.023_bib45","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.ejrad.2019.04.022","article-title":"A\u00a0deep learning radiomics model for preoperative grading in meningioma","volume":"116","author":"Zhu","year":"2019","journal-title":"Eur J Radiol"},{"key":"10.1016\/j.wneu.2023.08.023_bib21","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1002\/jmri.26723","article-title":"Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: a preliminary study","volume":"50","author":"Banzato","year":"2019","journal-title":"J\u00a0Magn Reson Imaging"},{"key":"10.1016\/j.wneu.2023.08.023_bib27","doi-asserted-by":"crossref","first-page":"E4","DOI":"10.3171\/2018.8.FOCUS18191","article-title":"Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging","volume":"45","author":"Hale","year":"2018","journal-title":"Neurosurg Focus"},{"key":"10.1016\/j.wneu.2023.08.023_bib32","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.jbi.2007.06.007","article-title":"A\u00a0method for linking computed image features to histological semantics in neuropathology","volume":"40","author":"Lessmann","year":"2007","journal-title":"J\u00a0Biomed Inform"},{"key":"10.1016\/j.wneu.2023.08.023_bib29","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.mri.2020.11.009","article-title":"Meningiomas: preoperative predictive histopathological grading based on radiomics of MRI","volume":"77","author":"Han","year":"2021","journal-title":"Magn Reson Imaging"},{"key":"10.1016\/j.wneu.2023.08.023_bib24","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.acra.2020.03.034","article-title":"Value of MRI radiomics based on enhanced T1WI images in prediction of meningiomas grade","volume":"28","author":"Chu","year":"2021","journal-title":"Acad Radiol"},{"key":"10.1016\/j.wneu.2023.08.023_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2020.109251","article-title":"Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI","volume":"131","author":"Hu","year":"2020","journal-title":"Eur J Radiol"},{"key":"10.1016\/j.wneu.2023.08.023_bib36","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1038\/s41746-020-0219-5","article-title":"Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database","volume":"3","author":"Moreau","year":"2020","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.wneu.2023.08.023_bib39","doi-asserted-by":"crossref","first-page":"4615","DOI":"10.1007\/s00330-020-06788-8","article-title":"Magnetic resonance imaging-based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade","volume":"30","author":"Park","year":"2020","journal-title":"Eur Radiol"},{"key":"10.1016\/j.wneu.2023.08.023_bib23","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.3389\/fonc.2019.01338","article-title":"The diagnostic value of radiomics-based machine learning in predicting the grade of meningiomas using conventional magnetic resonance imaging: a preliminary study","volume":"9","author":"Chen","year":"2019","journal-title":"Front Oncol"},{"key":"10.1016\/j.wneu.2023.08.023_bib38","doi-asserted-by":"crossref","first-page":"e646","DOI":"10.1016\/j.wneu.2019.02.109","article-title":"Differentiation researches on the meningioma subtypes by radiomics from contrast-enhanced magnetic resonance imaging: a preliminary study","volume":"126","author":"Niu","year":"2019","journal-title":"World Neurosurg"},{"key":"10.1016\/j.wneu.2023.08.023_bib37","first-page":"vdz011","article-title":"Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival","volume":"1","author":"Morin","year":"2019","journal-title":"Neurooncol Adv"},{"key":"10.1016\/j.wneu.2023.08.023_bib35","doi-asserted-by":"crossref","first-page":"7024","DOI":"10.1039\/C9AN01551E","article-title":"Determination of meningioma brain tumour grades using Raman microspectroscopy imaging","volume":"144","author":"Morais","year":"2019","journal-title":"Analyst"},{"key":"10.1016\/j.wneu.2023.08.023_bib31","doi-asserted-by":"crossref","first-page":"e366","DOI":"10.1016\/j.wneu.2019.08.148","article-title":"Accuracy of radiomics-based feature analysis on multiparametric magnetic resonance images for noninvasive meningioma grading","volume":"132","author":"Laukamp","year":"2019","journal-title":"World Neurosurg"},{"key":"10.1016\/j.wneu.2023.08.023_bib34","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1007\/s00330-018-5632-7","article-title":"The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest","volume":"29","author":"Lu","year":"2019","journal-title":"Eur Radiol"},{"key":"10.1016\/j.wneu.2023.08.023_bib28","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.mri.2019.08.011","article-title":"Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: a multicenter radiomics study","volume":"63","author":"Hamerla","year":"2019","journal-title":"Magn Reson Imaging"},{"key":"10.1016\/j.wneu.2023.08.023_bib33","doi-asserted-by":"crossref","first-page":"e677","DOI":"10.1016\/j.wneu.2019.03.241","article-title":"Can systemic inflammatory Markers Be used to predict the Pathological grade of meningioma before surgery?","volume":"127","author":"Lin","year":"2019","journal-title":"World Neurosurg"},{"key":"10.1016\/j.wneu.2023.08.023_bib40","doi-asserted-by":"crossref","first-page":"4068","DOI":"10.1007\/s00330-018-5830-3","article-title":"Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging","volume":"29","author":"Park","year":"2019","journal-title":"Eur Radiol"},{"key":"10.1016\/j.wneu.2023.08.023_bib25","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0187908","article-title":"Radiographic prediction of meningioma grade by semantic and radiomic features","volume":"12","author":"Coroller","year":"2017","journal-title":"PLoS One"},{"key":"10.1016\/j.wneu.2023.08.023_bib26","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1002\/jemt.22874","article-title":"Nuclear spatial and spectral features based evolutionary method for meningioma subtypes classification in histopathology","volume":"80","author":"Fatima","year":"2017","journal-title":"Microsc Res Tech"},{"key":"10.1016\/j.wneu.2023.08.023_bib43","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1016\/j.tranon.2017.04.006","article-title":"The potential value of preoperative MRI texture and shape analysis in grading meningiomas: a preliminary investigation","volume":"10","author":"Yan","year":"2017","journal-title":"Transl Oncol"},{"key":"10.1016\/j.wneu.2023.08.023_bib20","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.compmedimag.2014.05.013","article-title":"A\u00a0multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours","volume":"41","author":"Al-Kadi","year":"2015","journal-title":"Comput Med Imaging Graph"},{"key":"10.1016\/j.wneu.2023.08.023_bib42","article-title":"Meningioma subtype classification using morphology features and random forests","volume":"8676","author":"Strange","year":"2013","journal-title":"SPIE"},{"key":"10.1016\/j.wneu.2023.08.023_bib19","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1016\/j.patcog.2010.01.005","article-title":"Texture measures combination for improved meningioma classification of histopathological images","volume":"43","author":"Al-Kadi","year":"2010","journal-title":"Pattern Recogn"},{"key":"10.1016\/j.wneu.2023.08.023_bib41","first-page":"196","article-title":"Adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classification","volume":"11","author":"Qureshi","year":"2008","journal-title":"Med Image Comput Comput Assist Interv"},{"key":"10.1016\/j.wneu.2023.08.023_bib22","doi-asserted-by":"crossref","DOI":"10.1117\/1.2141624","article-title":"Diagnosis of meningioma by time-resolved fluorescence spectroscopy","volume":"10","author":"Butte","year":"2005","journal-title":"J\u00a0Biomed Opt"},{"key":"10.1016\/j.wneu.2023.08.023_bib50","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1093\/neuros\/nyaa585","article-title":"Predicting discharge disposition following meningioma resection using a multi-institutional natural language processing model","volume":"88","author":"Muhlestein","year":"2021","journal-title":"Neurosurgery"},{"key":"10.1016\/j.wneu.2023.08.023_bib47","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2021.636235","article-title":"Pre-operative MRI radiomics for the prediction of Progression and recurrence in meningiomas","volume":"12","author":"Ko","year":"2021","journal-title":"Front Neurol"},{"key":"10.1016\/j.wneu.2023.08.023_bib48","article-title":"MPscore: a novel predictive and prognostic scoring for Progressive meningioma","volume":"13","author":"Liu","year":"2021","journal-title":"Cancers"},{"key":"10.1016\/j.wneu.2023.08.023_bib51","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1007\/s00234-019-02259-0","article-title":"Radiomics approach for prediction of recurrence in skull base meningiomas","volume":"61","author":"Zhang","year":"2019","journal-title":"Neuroradiology"},{"key":"10.1016\/j.wneu.2023.08.023_bib49","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1055\/s-0037-1604393","article-title":"Using a guided machine learning ensemble model to predict discharge disposition following meningioma resection","volume":"79","author":"Muhlestein","year":"2018","journal-title":"J\u00a0Neurol Surg B Skull Base"},{"key":"10.1016\/j.wneu.2023.08.023_bib57","article-title":"Fast meningioma segmentation in T1-weighted magnetic resonance imaging volumes using a lightweight 3D deep learning architecture","volume":"8","author":"Bouget","year":"2021","journal-title":"J\u00a0Med Imaging"},{"key":"10.1016\/j.wneu.2023.08.023_bib55","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s00062-020-00884-4","article-title":"Automated meningioma segmentation in multiparametric MRI : comparable effectiveness of a deep learning model and manual segmentation","volume":"31","author":"Laukamp","year":"2021","journal-title":"Clin Neuroradiol"},{"key":"10.1016\/j.wneu.2023.08.023_bib52","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s10278-019-00220-4","article-title":"Eye tracking for deep learning segmentation using convolutional neural networks","volume":"32","author":"Stember","year":"2019","journal-title":"J\u00a0Digit Imaging"},{"key":"10.1016\/j.wneu.2023.08.023_bib54","series-title":"Robust tumor localization with pyramid grad-cam","author":"Lee","year":"2018"},{"key":"10.1016\/j.wneu.2023.08.023_bib56","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1002\/jmri.26976","article-title":"Differentiation between benign and nonbenign meningiomas by using texture analysis from multiparametric MRI","volume":"51","author":"Ke","year":"2020","journal-title":"J\u00a0Magn Reson Imaging"},{"key":"10.1016\/j.wneu.2023.08.023_bib53","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1002\/ima.22288","article-title":"Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier","volume":"28","author":"Selvapandian","year":"2018","journal-title":"Int J Imag Syst Technol"},{"key":"10.1016\/j.wneu.2023.08.023_bib58","first-page":"58","article-title":"Significance of machine learning in healthcare: features, pillars and applications","volume":"3","author":"Javaid","year":"2022","journal-title":"Int J Intell Syst"},{"key":"10.1016\/j.wneu.2023.08.023_bib59","first-page":"4","article-title":"Meningiomas: knowledge base, treatment outcomes, and uncertainties. A RANO review","volume":"122","author":"Rogers","year":"2015","journal-title":"J\u00a0Neurosurg"},{"key":"10.1016\/j.wneu.2023.08.023_bib60","doi-asserted-by":"crossref","DOI":"10.3389\/fradi.2021.711514","article-title":"Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms","volume":"1","author":"Bouget","year":"2021","journal-title":"Front Radiol"},{"key":"10.1016\/j.wneu.2023.08.023_bib61","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-022-19356-5","article-title":"Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice","volume":"12","author":"Boaro","year":"2022","journal-title":"Sci Rep"},{"key":"10.1016\/j.wneu.2023.08.023_bib62","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000033","article-title":"A\u00a0systematic review of federated learning applications for biomedical data","volume":"1","author":"Crowson","year":"2022","journal-title":"PLOS Digit Health"},{"key":"10.1016\/j.wneu.2023.08.023_bib63","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102251","article-title":"High resolution histopathology image generation and segmentation through adversarial training","volume":"75","author":"Li","year":"2022","journal-title":"Med Image Anal"},{"key":"10.1016\/j.wneu.2023.08.023_bib64","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1038\/s41746-020-00341-z","article-title":"Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines","volume":"3","author":"Huang","year":"2020","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.wneu.2023.08.023_bib65","series-title":"Artificial Intelligence in Precision Health","first-page":"203","article-title":"Chapter 9 - artificial intelligence for management of patients with intracranial neoplasms","author":"Mammi","year":"2020"},{"key":"10.1016\/j.wneu.2023.08.023_bib66","series-title":"Youmans and Winn Neurological Surgery","first-page":"478","article-title":"Artificial intelligence and big data in neurosurgery","author":"Boaro","year":"2022"},{"key":"10.1016\/j.wneu.2023.08.023_bib68","author":"Initiative"},{"key":"10.1016\/j.wneu.2023.08.023_bib70","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1007\/s00234-021-02668-0","article-title":"Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis","volume":"63","author":"Ugga","year":"2021","journal-title":"Neuroradiology"}],"container-title":["World Neurosurgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1878875023011269?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1878875023011269?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T09:22:50Z","timestamp":1761816170000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1878875023011269"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11]]},"references-count":68,"alternative-id":["S1878875023011269"],"URL":"https:\/\/doi.org\/10.1016\/j.wneu.2023.08.023","relation":{},"ISSN":["1878-8750"],"issn-type":[{"value":"1878-8750","type":"print"}],"subject":[],"published":{"date-parts":[[2023,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis","name":"articletitle","label":"Article Title"},{"value":"World Neurosurgery","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.wneu.2023.08.023","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}