{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T23:59:25Z","timestamp":1776124765042,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82203204"],"award-info":[{"award-number":["82203204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82072788"],"award-info":[{"award-number":["82072788"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82203390"],"award-info":[{"award-number":["82203390"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Neurooncol"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s11060-023-04511-3","type":"journal-article","created":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T05:05:21Z","timestamp":1703567121000},"page":"59-71","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["The development of a combined clinico-radiomics model for predicting post-operative recurrence in atypical meningiomas: a multicenter study"],"prefix":"10.1007","volume":"166","author":[{"given":"Leihao","family":"Ren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaojiao","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xie","family":"Qing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haixia","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daijun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tareq A.","family":"Juratli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroaki","family":"Wakimoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingyang","family":"Hua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"4511_CR1","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1093\/neuonc\/noab200","volume":"23","author":"QT Ostrom","year":"2021","unstructured":"Ostrom QT, Cioffi G, Waite K, Kruchko C, Barnholtz-Sloan JS (2021) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2014\u20132018. Neuro Oncol 23:31\u20132105. https:\/\/doi.org\/10.1093\/neuonc\/noab200","journal-title":"Neuro Oncol"},{"key":"4511_CR2","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1093\/neuonc\/noab106","volume":"23","author":"DN Louis","year":"2021","unstructured":"Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R, von Deimling A, Ellison DW (2021) The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol 23:1231\u20131251. https:\/\/doi.org\/10.1093\/neuonc\/noab106","journal-title":"Neuro Oncol"},{"key":"4511_CR3","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.neuchi.2017.03.004","volume":"63","author":"C Champeaux","year":"2017","unstructured":"Champeaux C, Houston D, Dunn L (2017) Atypical meningioma: a study on recurrence and disease-specific survival. Neurochirurgie 63:273\u2013281. https:\/\/doi.org\/10.1016\/j.neuchi.2017.03.004","journal-title":"Neurochirurgie"},{"key":"4511_CR4","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1080\/14737175.2018.1429920","volume":"18","author":"C Apra","year":"2018","unstructured":"Apra C, Peyre M, Kalamarides M (2018) Current treatment options for meningioma. Expert Rev Neurother 18:241\u2013249. https:\/\/doi.org\/10.1080\/14737175.2018.1429920","journal-title":"Expert Rev Neurother"},{"key":"4511_CR5","doi-asserted-by":"publisher","DOI":"10.1227\/neu.0000000000002272","author":"L Ren","year":"2022","unstructured":"Ren L, Hua L, Deng J, Cheng H, Wang D, Chen J, Xie Q, Wakimoto H, Gong Y (2022) Favorable long-term outcomes of chordoid meningioma compared with the other WHO grade 2 meningioma subtypes. Neurosurgery. https:\/\/doi.org\/10.1227\/neu.0000000000002272","journal-title":"Neurosurgery"},{"key":"4511_CR6","doi-asserted-by":"publisher","DOI":"10.3171\/2022.9.JNS221530","author":"L Ren","year":"2022","unstructured":"Ren L, Cheng H, Chen J, Deng J, Wang D, Xie Q, Wakimoto H, Hua L, Gong Y (2022) Progesterone receptor expression and prediction of benefits of adjuvant radiotherapy in de novo atypical meningiomas after gross-total resection. J Neurosurg. https:\/\/doi.org\/10.3171\/2022.9.JNS221530","journal-title":"J Neurosurg"},{"key":"4511_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13061262","author":"R Meta","year":"2021","unstructured":"Meta R, Boldt HB, Kristensen BW, Sahm F, Sjursen W, Torp SH (2021) The prognostic value of methylation signatures and NF2 mutations in atypical meningiomas. Cancers (Basel). https:\/\/doi.org\/10.3390\/cancers13061262","journal-title":"Cancers (Basel)"},{"key":"4511_CR8","doi-asserted-by":"publisher","first-page":"446","DOI":"10.23736\/S0390-5616.17.04102-9","volume":"64","author":"AB Bayoumi","year":"2020","unstructured":"Bayoumi AB, Laviv Y, Karaali CN, Ertilav K, Kepoglu U, Toktas ZO, Konya D, Kasper EM (2020) Spinal meningiomas: 61 cases with predictors of early postoperative surgical outcomes. J Neurosurg Sci 64:446\u2013451. https:\/\/doi.org\/10.23736\/S0390-5616.17.04102-9","journal-title":"J Neurosurg Sci"},{"key":"4511_CR9","doi-asserted-by":"publisher","first-page":"1960","DOI":"10.1007\/s00261-019-02028-w","volume":"44","author":"Z Bodalal","year":"2019","unstructured":"Bodalal Z, Trebeschi S, Nguyen-Kim TDL, Schats W, Beets-Tan R (2019) Radiogenomics: bridging imaging and genomics. Abdom Radiol (NY) 44:1960\u20131984. https:\/\/doi.org\/10.1007\/s00261-019-02028-w","journal-title":"Abdom Radiol (NY)"},{"key":"4511_CR10","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.3171\/2014.7.JNS132359","volume":"121","author":"BJ Lin","year":"2014","unstructured":"Lin BJ, Chou KN, Kao HW, Lin C, Tsai WC, Feng SW, Lee MS, Hueng DY (2014) Correlation between magnetic resonance imaging grading and pathological grading in meningioma. J Neurosurg 121:1201\u20131208. https:\/\/doi.org\/10.3171\/2014.7.JNS132359","journal-title":"J Neurosurg"},{"key":"4511_CR11","doi-asserted-by":"publisher","first-page":"208","DOI":"10.3174\/ajnr.A5391","volume":"39","author":"M Zhou","year":"2018","unstructured":"Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, Iv M, Ou Y, Kalpathy-Cramer J, Napel S, Gillies R, Gevaert O, Gatenby R (2018) Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. AJNR Am J Neuroradiol 39:208\u2013216. https:\/\/doi.org\/10.3174\/ajnr.A5391","journal-title":"AJNR Am J Neuroradiol"},{"key":"4511_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109251","volume":"131","author":"J Hu","year":"2020","unstructured":"Hu J, Zhao Y, Li M, Liu J, Wang F, Weng Q, Wang X, Cao D (2020) Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI. Eur J Radiol 131:109251. https:\/\/doi.org\/10.1016\/j.ejrad.2020.109251","journal-title":"Eur J Radiol"},{"key":"4511_CR13","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441\u2013446. https:\/\/doi.org\/10.1016\/j.ejca.2011.11.036","journal-title":"Eur J Cancer"},{"key":"4511_CR14","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1007\/s00259-016-3325-5","volume":"43","author":"MC Desseroit","year":"2016","unstructured":"Desseroit MC, Visvikis D, Tixier F, Majdoub M, Perdrisot R, Guillevin R, Cheze Le Rest C, Hatt M (2016) Development of a nomogram combining clinical staging with (18)F-FDG PET\/CT image features in non-small-cell lung cancer stage I-III. Eur J Nucl Med Mol Imaging 43:1477\u20131485. https:\/\/doi.org\/10.1007\/s00259-016-3325-5","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"4511_CR15","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.jtho.2016.11.2226","volume":"12","author":"TP Coroller","year":"2017","unstructured":"Coroller TP, Agrawal V, Huynh E, Narayan V, Lee SW, Mak RH, Aerts H (2017) Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC. J Thorac Oncol 12:467\u2013476. https:\/\/doi.org\/10.1016\/j.jtho.2016.11.2226","journal-title":"J Thorac Oncol"},{"key":"4511_CR16","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.ebiom.2018.09.007","volume":"36","author":"Y Jiang","year":"2018","unstructured":"Jiang Y, Chen C, Xie J, Wang W, Zha X, Lv W, Chen H, Hu Y, Li T, Yu J, Zhou Z, Xu Y, Li G (2018) Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer. EBioMedicine 36:171\u2013182. https:\/\/doi.org\/10.1016\/j.ebiom.2018.09.007","journal-title":"EBioMedicine"},{"key":"4511_CR17","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1186\/s12885-018-5003-4","volume":"18","author":"Z Yao","year":"2018","unstructured":"Yao Z, Dong Y, Wu G, Zhang Q, Yang D, Yu JH, Wang WP (2018) Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images. BMC Cancer 18:1089. https:\/\/doi.org\/10.1186\/s12885-018-5003-4","journal-title":"BMC Cancer"},{"key":"4511_CR18","doi-asserted-by":"publisher","first-page":"1944","DOI":"10.1158\/1078-0432.CCR-19-0374","volume":"26","author":"IL Xi","year":"2020","unstructured":"Xi IL, Zhao Y, Wang R, Chang M, Purkayastha S, Chang K, Huang RY, Silva AC, Vallieres M, Habibollahi P, Fan Y, Zou B, Gade TP, Zhang PJ, Soulen MC, Zhang Z, Bai HX, Stavropoulos SW (2020) Deep Learning to distinguish benign from malignant renal lesions based on routine MR imaging. Clin Cancer Res 26:1944\u20131952. https:\/\/doi.org\/10.1158\/1078-0432.CCR-19-0374","journal-title":"Clin Cancer Res"},{"key":"4511_CR19","doi-asserted-by":"publisher","first-page":"e646","DOI":"10.1016\/j.wneu.2019.02.109","volume":"126","author":"L Niu","year":"2019","unstructured":"Niu L, Zhou X, Duan C, Zhao J, Sui Q, Liu X, Zhang X (2019) Differentiation researches on the meningioma subtypes by radiomics from contrast-enhanced magnetic resonance imaging: a preliminary study. World Neurosurg 126:e646\u2013e652. https:\/\/doi.org\/10.1016\/j.wneu.2019.02.109","journal-title":"World Neurosurg"},{"key":"4511_CR20","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.mri.2019.08.011","volume":"63","author":"G Hamerla","year":"2019","unstructured":"Hamerla G, Meyer HJ, Schob S, Ginat DT, Altman A, Lim T, Gihr GA, Horvath-Rizea D, Hoffmann KT, Surov A (2019) Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: a multicenter radiomics study. Magn Reson Imaging 63:244\u2013249. https:\/\/doi.org\/10.1016\/j.mri.2019.08.011","journal-title":"Magn Reson Imaging"},{"key":"4511_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2020.102933","volume":"58","author":"J Zhang","year":"2020","unstructured":"Zhang J, Yao K, Liu P, Liu Z, Han T, Zhao Z, Cao Y, Zhang G, Zhang J, Tian J, Zhou J (2020) A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: a multicentre study. EBioMedicine 58:102933. https:\/\/doi.org\/10.1016\/j.ebiom.2020.102933","journal-title":"EBioMedicine"},{"key":"4511_CR22","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1016\/j.media.2005.04.005","volume":"9","author":"I Wolf","year":"2005","unstructured":"Wolf I, Vetter M, Wegner I, Bottger T, Nolden M, Schobinger M, Hastenteufel M, Kunert T, Meinzer HP (2005) The medical imaging interaction toolkit. Med Image Anal 9:594\u2013604. https:\/\/doi.org\/10.1016\/j.media.2005.04.005","journal-title":"Med Image Anal"},{"key":"4511_CR23","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1109\/TITB.2011.2119322","volume":"15","author":"UR Acharya","year":"2011","unstructured":"Acharya UR, Dua S, Du X, Sree SV, Chua CK (2011) Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Trans Inf Technol Biomed 15:449\u2013455. https:\/\/doi.org\/10.1109\/TITB.2011.2119322","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"4511_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v039.i05","volume":"39","author":"N Simon","year":"2011","unstructured":"Simon N, Friedman J, Hastie T, Tibshirani R (2011) Regularization paths for Cox\u2019s proportional hazards model via coordinate descent. J Stat Softw 39:1\u201313. https:\/\/doi.org\/10.18637\/jss.v039.i05","journal-title":"J Stat Softw"},{"key":"4511_CR25","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4","volume":"15","author":"FE Harrell Jr","year":"1996","unstructured":"Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361\u2013387. https:\/\/doi.org\/10.1002\/(SICI)1097-0258(19960229)15:4%3c361::AID-SIM168%3e3.0.CO;2-4","journal-title":"Stat Med"},{"key":"4511_CR26","doi-asserted-by":"publisher","first-page":"W1","DOI":"10.7326\/M14-0698","volume":"162","author":"KG Moons","year":"2015","unstructured":"Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 162:W1-73. https:\/\/doi.org\/10.7326\/M14-0698","journal-title":"Ann Intern Med"},{"key":"4511_CR27","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1001\/jama.2015.37","volume":"313","author":"M Fitzgerald","year":"2015","unstructured":"Fitzgerald M, Saville BR, Lewis RJ (2015) Decision curve analysis. JAMA 313:409\u2013410. https:\/\/doi.org\/10.1001\/jama.2015.37","journal-title":"JAMA"},{"key":"4511_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13081970","author":"M Da Broi","year":"2021","unstructured":"Da Broi M, Borrelli P, Meling TR (2021) Predictors of survival in atypical meningiomas. Cancers (Basel). https:\/\/doi.org\/10.3390\/cancers13081970","journal-title":"Cancers (Basel)"},{"key":"4511_CR29","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1007\/s00066-018-1276-4","volume":"194","author":"JC Peeken","year":"2018","unstructured":"Peeken JC, Hesse J, Haller B, Kessel KA, Nusslin F, Combs SE (2018) Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients. Strahlenther Onkol 194:580\u2013590. https:\/\/doi.org\/10.1007\/s00066-018-1276-4","journal-title":"Strahlenther Onkol"},{"key":"4511_CR30","doi-asserted-by":"publisher","first-page":"e366","DOI":"10.1111\/ajco.12861","volume":"14","author":"H Kim","year":"2018","unstructured":"Kim H, Lim DH, Kim TG, Lee JI, Nam DH, Seol HJ, Kong DS, Choi JW, Suh YL, Kim ST (2018) Leptomeningeal enhancement on preoperative brain MRI in patients with glioblastoma and its clinical impact. Asia Pac J Clin Oncol 14:e366\u2013e373. https:\/\/doi.org\/10.1111\/ajco.12861","journal-title":"Asia Pac J Clin Oncol"},{"key":"4511_CR31","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.754937","volume":"11","author":"XY Chen","year":"2021","unstructured":"Chen XY, Chen JY, Huang YX, Xu JH, Sun WW, Chen Y, Ding CY, Wang SB, Wu XY, Kang DZ, You HH, Lin YX (2021) Establishment and validation of an integrated model to predict postoperative recurrence in patients with atypical meningioma. Front Oncol 11:754937. https:\/\/doi.org\/10.3389\/fonc.2021.754937","journal-title":"Front Oncol"},{"key":"4511_CR32","doi-asserted-by":"publisher","first-page":"e610","DOI":"10.1016\/j.wneu.2022.04.034","volume":"163","author":"Y Lin","year":"2022","unstructured":"Lin Y, Dai P, Lin Q, Chen J (2022) A predictive nomogram for atypical meningioma based on preoperative magnetic resonance imaging and routine blood tests. World Neurosurg 163:e610\u2013e616. https:\/\/doi.org\/10.1016\/j.wneu.2022.04.034","journal-title":"World Neurosurg"},{"key":"4511_CR33","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.567736","volume":"10","author":"H Gu","year":"2020","unstructured":"Gu H, Zhang X, di Russo P, Zhao X, Xu T (2020) The current state of radiomics for meningiomas: promises and challenges. Front Oncol 10:567736. https:\/\/doi.org\/10.3389\/fonc.2020.567736","journal-title":"Front Oncol"},{"key":"4511_CR34","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0187908","volume":"12","author":"TP Coroller","year":"2017","unstructured":"Coroller TP, Bi WL, Huynh E, Abedalthagafi M, Aizer AA, Greenwald NF, Parmar C, Narayan V, Wu WW, Miranda de Moura S, Gupta S, Beroukhim R, Wen PY, Al-Mefty O, Dunn IF, Santagata S, Alexander BM, Huang RY, Aerts H (2017) Radiographic prediction of meningioma grade by semantic and radiomic features. PLoS ONE 12:e0187908. https:\/\/doi.org\/10.1371\/journal.pone.0187908","journal-title":"PLoS ONE"},{"key":"4511_CR35","doi-asserted-by":"publisher","first-page":"4068","DOI":"10.1007\/s00330-018-5830-3","volume":"29","author":"YW Park","year":"2019","unstructured":"Park YW, Oh J, You SC, Han K, Ahn SS, Choi YS, Chang JH, Kim SH, Lee SK (2019) Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol 29:4068\u20134076. https:\/\/doi.org\/10.1007\/s00330-018-5830-3","journal-title":"Eur Radiol"},{"key":"4511_CR36","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.ejrad.2019.04.022","volume":"116","author":"Y Zhu","year":"2019","unstructured":"Zhu Y, Man C, Gong L, Dong D, Yu X, Wang S, Fang M, Wang S, Fang X, Chen X, Tian J (2019) A deep learning radiomics model for preoperative grading in meningioma. Eur J Radiol 116:128\u2013134. https:\/\/doi.org\/10.1016\/j.ejrad.2019.04.022","journal-title":"Eur J Radiol"},{"key":"4511_CR37","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1093\/noajnl\/vdz011","volume":"1","author":"O Morin","year":"2019","unstructured":"Morin O, Chen WC, Nassiri F, Susko M, Magill ST, Vasudevan HN, Wu A, Vallieres M, Gennatas ED, Valdes G, Pekmezci M, Alcaide-Leon P, Choudhury A, Interian Y, Mortezavi S, Turgutlu K, Bush NAO, Solberg TD, Braunstein SE, Sneed PK, Perry A, Zadeh G, Mcdermott MW, Villanueva-Meyer JE, Raleigh DR (2019) Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival. Neurooncol Adv 1:11. https:\/\/doi.org\/10.1093\/noajnl\/vdz011","journal-title":"Neurooncol Adv"},{"key":"4511_CR38","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1007\/s00234-019-02259-0","volume":"61","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Chen JH, Chen TY, Lim SW, Wu TC, Kuo YT, Ko CC, Su MY (2019) Radiomics approach for prediction of recurrence in skull base meningiomas. Neuroradiology 61:1355\u20131364. https:\/\/doi.org\/10.1007\/s00234-019-02259-0","journal-title":"Neuroradiology"},{"key":"4511_CR39","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.752158","volume":"11","author":"N Li","year":"2021","unstructured":"Li N, Mo Y, Huang C, Han K, He M, Wang X, Wen J, Yang S, Wu H, Dong F, Sun F, Li Y, Yu Y, Zhang M, Guan X, Xu X (2021) A clinical semantic and radiomics nomogram for predicting brain invasion in WHO grade II meningioma based on tumor and tumor-to-brain interface features. Front Oncol 11:752158. https:\/\/doi.org\/10.3389\/fonc.2021.752158","journal-title":"Front Oncol"},{"key":"4511_CR40","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/s00401-016-1545-1","volume":"131","author":"DN Louis","year":"2016","unstructured":"Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803\u2013820. https:\/\/doi.org\/10.1007\/s00401-016-1545-1","journal-title":"Acta Neuropathol"},{"key":"4511_CR41","doi-asserted-by":"publisher","DOI":"10.3390\/cancers12102942","author":"D Kalasauskas","year":"2020","unstructured":"Kalasauskas D, Kronfeld A, Renovanz M, Kurz E, Leukel P, Krenzlin H, Brockmann MA, Sommer CJ, Ringel F, Keric N (2020) Identification of high-risk atypical meningiomas according to semantic and radiomic features. Cancers (Basel). https:\/\/doi.org\/10.3390\/cancers12102942","journal-title":"Cancers (Basel)"},{"key":"4511_CR42","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ymeth.2020.06.003","volume":"188","author":"P Lohmann","year":"2021","unstructured":"Lohmann P, Galldiks N, Kocher M, Heinzel A, Filss CP, Stegmayr C, Mottaghy FM, Fink GR, Jon Shah N, Langen KJ (2021) Radiomics in neuro-oncology: basics, workflow, and applications. Methods 188:112\u2013121. https:\/\/doi.org\/10.1016\/j.ymeth.2020.06.003","journal-title":"Methods"}],"container-title":["Journal of Neuro-Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11060-023-04511-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11060-023-04511-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11060-023-04511-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T14:11:53Z","timestamp":1706537513000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11060-023-04511-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["4511"],"URL":"https:\/\/doi.org\/10.1007\/s11060-023-04511-3","relation":{},"ISSN":["0167-594X","1573-7373"],"issn-type":[{"value":"0167-594X","type":"print"},{"value":"1573-7373","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,26]]},"assertion":[{"value":"27 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2023","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 authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}},{"value":"This clinical study was approved by the Human Subjects Institutional Review Board at Huashan Hospital, Fudan University.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The consent process was omitted due to the retrospective nature of our study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors affirm that human research participants provided informed consent for publication of the images in Fig.\u00a0.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}]}}