{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T16:27:50Z","timestamp":1793809670785,"version":"4.6.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This study aims to develop and evaluate radiomics-based machine learning (ML) models for predicting meningioma grades using multiparametric magnetic resonance imaging (MRI). The study utilized the BraTS-MEN dataset\u2019s training split, including 698 patients (524 with grade 1 and 174 with grade 2\u20133 meningiomas). We extracted 4872 radiomic features from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. LASSO regression reduced features to 176. The data was split into training (60%), validation (20%), and test (20%) sets. Five ML algorithms (TabPFN, XGBoost, LightGBM, CatBoost, and Random Forest) were employed to build models differentiating low-grade (grade 1) from high-grade (grade 2\u20133) meningiomas. Hyperparameter tuning was performed using Optuna, optimizing model-specific parameters and feature selection. The CatBoost model demonstrated the best performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.838 [95% confidence interval (CI): 0.689\u20130.935], precision of 0.492 (95% CI: 0.371\u20130.623), recall of 0.838 (95% CI: 0.689\u20130.935), F1 score of 0.620 (95% CI: 0.495\u20130.722), accuracy of 0.729 (95% CI: 0.650\u20130.800), an area under the precision-recall curve (AUPRC) of 0.620 (95% CI: 0.433\u20130.753), and Brier score of 0.156 (95% CI: 0.122-0.200). Other models showed comparable performance, with mean AUROCs ranging from 0.752 to 0.784. The radiomics-based ML approach presented in this study showcases the potential for non-invasive and pre-operative grading of meningiomas using multiparametric MRI. Further validation on larger and independent datasets is necessary to establish the robustness and generalizability of these findings.<\/jats:p>","DOI":"10.1038\/s41598-024-78311-8","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T02:02:18Z","timestamp":1730340138000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A large scale multi institutional study for radiomics driven machine learning for meningioma grading"],"prefix":"10.1038","volume":"14","author":[{"given":"Mert","family":"Karabacak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiv","family":"Patil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raj K.","family":"Shrivastava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantinos","family":"Margetis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"78311_CR1","doi-asserted-by":"publisher","first-page":"v1","DOI":"10.1093\/neuonc\/noac202","volume":"24","author":"QT Ostrom","year":"2022","unstructured":"Ostrom, Q. T. et al. CBTRUS Statistical Report: primary brain and other Central Nervous System tumors diagnosed in the United States in 2015\u20132019. Neuro-Oncol. 24, v1\u2013v95. https:\/\/doi.org\/10.1093\/neuonc\/noac202 (2022).","journal-title":"Neuro-Oncol"},{"key":"78311_CR2","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1093\/neuonc\/noab106","volume":"23","author":"DN Louis","year":"2021","unstructured":"Louis, D. N. et al. 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 (2021).","journal-title":"Neuro-Oncol"},{"key":"78311_CR3","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3171\/2014.7.JNS131644","volume":"122","author":"L Rogers","year":"2015","unstructured":"Rogers, L. et al. Meningiomas: knowledge base, treatment outcomes, and uncertainties. A RANO review. J. Neurosurg. 122, 4\u201323. https:\/\/doi.org\/10.3171\/2014.7.JNS131644 (2015).","journal-title":"J. Neurosurg."},{"key":"78311_CR4","doi-asserted-by":"publisher","first-page":"1137849","DOI":"10.3389\/fonc.2023.1137849","volume":"13","author":"V Yarabarla","year":"2023","unstructured":"Yarabarla, V. et al. Intracranial meningiomas: an update of the 2021 World Health Organization classifications and review of management with a focus on radiation therapy. Front. Oncol. 13, 1137849. https:\/\/doi.org\/10.3389\/fonc.2023.1137849 (2023).","journal-title":"Front. Oncol."},{"key":"78311_CR5","doi-asserted-by":"publisher","first-page":"551","DOI":"10.3171\/2015.9.JNS15754","volume":"125","author":"K Gousias","year":"2016","unstructured":"Gousias, K., Schramm, J. & Simon, M. The Simpson grading revisited: aggressive surgery and its place in modern meningioma management. J. Neurosurg. 125, 551\u2013560. https:\/\/doi.org\/10.3171\/2015.9.JNS15754 (2016).","journal-title":"J. Neurosurg."},{"key":"78311_CR6","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s007010170060","volume":"143","author":"PML Black","year":"2001","unstructured":"Black, P. M. L., Villavicencio, A. T., Rhouddou, C. & Loeffler, J. S. Aggressive surgery and focal radiation in the management of meningiomas of the skull base: preservation of function with maintenance of local control. Acta Neurochir. (Wien). 143, 555\u2013562. https:\/\/doi.org\/10.1007\/s007010170060 (2001).","journal-title":"Acta Neurochir. (Wien)"},{"key":"78311_CR7","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1093\/neuonc\/noab150","volume":"23","author":"R Goldbrunner","year":"2021","unstructured":"Goldbrunner, R. et al. EANO guideline on the diagnosis and management of meningiomas. Neuro-Oncol. 23, 1821\u20131834. https:\/\/doi.org\/10.1093\/neuonc\/noab150 (2021).","journal-title":"Neuro-Oncol"},{"key":"78311_CR8","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/j.acra.2020.03.034","volume":"28","author":"H Chu","year":"2021","unstructured":"Chu, H. et al. Value of MRI radiomics based on enhanced T1WI images in prediction of meningiomas grade. Acad. Radiol. 28, 687\u2013693. https:\/\/doi.org\/10.1016\/j.acra.2020.03.034 (2021).","journal-title":"Acad. Radiol."},{"key":"78311_CR9","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.ejrad.2018.11.016","volume":"110","author":"X Li","year":"2019","unstructured":"Li, X. et al. Meningioma grading using conventional MRI histogram analysis based on 3D tumor measurement. Eur. J. Radiol. 110, 45\u201353. https:\/\/doi.org\/10.1016\/j.ejrad.2018.11.016 (2019).","journal-title":"Eur. J. Radiol."},{"key":"78311_CR10","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.mri.2020.11.009","volume":"77","author":"Y Han","year":"2021","unstructured":"Han, Y. et al. Meningiomas: preoperative predictive histopathological grading based on radiomics of MRI. Magn. Reson. Imaging. 77, 36\u201343. https:\/\/doi.org\/10.1016\/j.mri.2020.11.009 (2021).","journal-title":"Magn. Reson. Imaging"},{"key":"78311_CR11","doi-asserted-by":"publisher","unstructured":"Gui, Y. & Zhang, J. Research progress of artificial intelligence in the grading and classification of meningiomas. Acad. Radiol. S1076633224000734. (2024). https:\/\/doi.org\/10.1016\/j.acra.2024.02.003","DOI":"10.1016\/j.acra.2024.02.003"},{"key":"78311_CR12","doi-asserted-by":"publisher","first-page":"i44","DOI":"10.1093\/neuonc\/noy143","volume":"21","author":"RY Huang","year":"2019","unstructured":"Huang, R. Y. et al. Imaging and diagnostic advances for intracranial meningiomas. Neuro-Oncol. 21, i44\u2013i61. https:\/\/doi.org\/10.1093\/neuonc\/noy143 (2019).","journal-title":"Neuro-Oncol"},{"key":"78311_CR13","doi-asserted-by":"publisher","first-page":"9866","DOI":"10.1038\/s41598-022-13379-8","volume":"12","author":"H Naseri","year":"2022","unstructured":"Naseri, H. et al. Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest. Sci. Rep. 12, 9866. https:\/\/doi.org\/10.1038\/s41598-022-13379-8 (2022).","journal-title":"Sci. Rep."},{"key":"78311_CR14","doi-asserted-by":"publisher","first-page":"109251","DOI":"10.1016\/j.ejrad.2020.109251","volume":"131","author":"J Hu","year":"2020","unstructured":"Hu, J. et al. 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 (2020).","journal-title":"Eur. J. Radiol."},{"key":"78311_CR15","doi-asserted-by":"publisher","first-page":"1810","DOI":"10.1002\/jmri.26976","volume":"51","author":"C Ke","year":"2020","unstructured":"Ke, C. et al. Differentiation between benign and nonbenign meningiomas by using texture analysis from Multiparametric MRI. J. Magn. Reson. Imaging. 51, 1810\u20131820. https:\/\/doi.org\/10.1002\/jmri.26976 (2020).","journal-title":"J. Magn. Reson. Imaging"},{"key":"78311_CR16","doi-asserted-by":"publisher","first-page":"e366","DOI":"10.1016\/j.wneu.2019.08.148","volume":"132","author":"KR Laukamp","year":"2019","unstructured":"Laukamp, K. R. et al. Accuracy of radiomics-based feature analysis on multiparametric magnetic resonance images for noninvasive meningioma grading. World Neurosurg. 132, e366\u2013e390. https:\/\/doi.org\/10.1016\/j.wneu.2019.08.148 (2019).","journal-title":"World Neurosurg."},{"key":"78311_CR17","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1038\/s41597-024-03350-9","volume":"11","author":"D LaBella","year":"2024","unstructured":"LaBella, D. et al. A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation. Sci. Data. 11, 496. https:\/\/doi.org\/10.1038\/s41597-024-03350-9 (2024).","journal-title":"Sci. Data"},{"key":"78311_CR18","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen, J. J. M. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77, e104\u2013e107. https:\/\/doi.org\/10.1158\/0008-5472.CAN-17-0339 (2017).","journal-title":"Cancer Res."},{"key":"78311_CR19","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357. https:\/\/doi.org\/10.1613\/jair.953 (2002).","journal-title":"J. Artif. Intell. Res."},{"key":"78311_CR20","doi-asserted-by":"crossref","unstructured":"Akiba, T. et al. Optuna: A Next-generation Hyperparameter Optimization Framework (2019).","DOI":"10.1145\/3292500.3330701"},{"key":"78311_CR21","first-page":"61","volume":"10","author":"J Platt","year":"1999","unstructured":"Platt, J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10, 61\u201374 (1999).","journal-title":"Adv. Large Margin Classif."},{"key":"78311_CR22","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","volume":"3","author":"WJ Youden","year":"1950","unstructured":"Youden, W. J. Index for rating diagnostic tests. Cancer. 3, 32\u201335.  (1950).","journal-title":"Cancer"},{"key":"78311_CR23","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1002\/bimj.200410135","volume":"47","author":"R Fluss","year":"2005","unstructured":"Fluss, R., Faraggi, D. & Reiser, B. Estimation of the Youden Index and its associated cutoff point. Biom J. 47, 458\u2013472. https:\/\/doi.org\/10.1002\/bimj.200410135 (2005).","journal-title":"Biom J."},{"key":"78311_CR24","unstructured":"Lundberg, S. M. & Lee, S-I. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (eds Guyon, I.  et al.). (Curran Associates, Inc., 2017)."},{"key":"78311_CR25","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley, J. A. & McNeil, B. J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 143, 29\u201336. https:\/\/doi.org\/10.1148\/radiology.143.1.7063747 (1982).","journal-title":"Radiology"},{"key":"78311_CR26","doi-asserted-by":"publisher","first-page":"3268","DOI":"10.3390\/biomedicines11123268","volume":"11","author":"JH Park","year":"2023","unstructured":"Park, J. H. et al. Predicting histologic grade of meningiomas using a combined model of radiomic and clinical imaging features from preoperative MRI. Biomedicines. 11, 3268. https:\/\/doi.org\/10.3390\/biomedicines11123268 (2023).","journal-title":"Biomedicines"},{"key":"78311_CR27","doi-asserted-by":"publisher","first-page":"488","DOI":"10.2967\/jnumed.118.222893","volume":"61","author":"ME Mayerhoefer","year":"2020","unstructured":"Mayerhoefer, M. E. et al. Introduction to radiomics. J. Nucl. Med. 61, 488\u2013495. https:\/\/doi.org\/10.2967\/jnumed.118.222893 (2020).","journal-title":"J. Nucl. Med."},{"key":"78311_CR28","doi-asserted-by":"publisher","unstructured":"Prokhorenkova, L. et al. CatBoost: unbiased boosting with categorical features. (2017). https:\/\/doi.org\/10.48550\/ARXIV.1706.09516","DOI":"10.48550\/ARXIV.1706.09516"},{"key":"78311_CR29","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. et al. 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 (2019).","journal-title":"Magn. Reson. Imaging"},{"key":"78311_CR30","doi-asserted-by":"publisher","unstructured":"On behalf of Topic Group \u2018Evaluating diagnostic tests and prediction models\u2019 of the STRATOS initiative, Van Calster, B., McLernon, D. J. et al. Calibration: the Achilles heel of predictive analytics. BMC Med. 17, 230 https:\/\/doi.org\/10.1186\/s12916-019-1466-7 (2019).","DOI":"10.1186\/s12916-019-1466-7"},{"key":"78311_CR31","doi-asserted-by":"publisher","first-page":"e0187908","DOI":"10.1371\/journal.pone.0187908","volume":"12","author":"TP Coroller","year":"2017","unstructured":"Coroller, T. P. et al. Radiographic prediction of meningioma grade by semantic and radiomic features. PLOS ONE. 12, e0187908. https:\/\/doi.org\/10.1371\/journal.pone.0187908 (2017).","journal-title":"PLOS ONE"},{"key":"78311_CR32","doi-asserted-by":"publisher","first-page":"vdz011","DOI":"10.1093\/noajnl\/vdz011","volume":"1","author":"O Morin","year":"2019","unstructured":"Morin, O. et al. Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival. Neuro-Oncol Adv. 1, vdz011. https:\/\/doi.org\/10.1093\/noajnl\/vdz011 (2019).","journal-title":"Neuro-Oncol Adv."},{"key":"78311_CR33","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.2214\/AJR.10.4531","volume":"195","author":"DT Ginat","year":"2010","unstructured":"Ginat, D. T., Mangla, R., Yeaney, G. & Wang, H. Z. Correlation of diffusion and perfusion MRI with Ki-67 in high-grade meningiomas. Am. J. Roentgenol. 195, 1391\u20131395. https:\/\/doi.org\/10.2214\/AJR.10.4531 (2010).","journal-title":"Am. J. Roentgenol."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-78311-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-78311-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-78311-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T02:02:45Z","timestamp":1730340165000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-78311-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["78311"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-78311-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,31]]},"assertion":[{"value":"14 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was deemed exempt from approval by the Icahn School of Medicine at Mount Sinai institutional review board because it involved analysis of deidentified patient data.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Institutional review board statement"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"26191"}}