{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:06:43Z","timestamp":1780607203405,"version":"3.54.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"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 Med Imaging"],"DOI":"10.1186\/s12880-025-01787-x","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T08:52:59Z","timestamp":1751359979000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multimodal deep learning-based radiomics for meningioma consistency prediction: integrating T1 and T2 MRI in a multi-center study"],"prefix":"10.1186","volume":"25","author":[{"given":"Huanjie","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubiao","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingbing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weifeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinrong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"1787_CR1","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1002\/cncr.29015","volume":"121","author":"HR Gittleman","year":"2015","unstructured":"Gittleman HR, Ostrom QT, Rouse CD, et al. Trends in central nervous system tumor incidence relative to other common cancers in adults, adolescents, and children in the united states, 2000 to 2010. Cancer. 2015;121:102\u201312. https:\/\/doi.org\/10.1002\/cncr.29015.","journal-title":"Cancer"},{"key":"1787_CR2","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, et al. The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol (Berl). 2016;131:803\u201320. https:\/\/doi.org\/10.1007\/s00401-016-1545-1.","journal-title":"Acta Neuropathol (Berl)"},{"key":"1787_CR3","doi-asserted-by":"publisher","first-page":"579599","DOI":"10.3389\/fonc.2020.579599","volume":"10","author":"K Huntoon","year":"2020","unstructured":"Huntoon K, Toland AMS, Dahiya S. Meningioma: A review of clinicopathological and molecular aspects. Front Oncol. 2020;10:579599. https:\/\/doi.org\/10.3389\/fonc.2020.579599.","journal-title":"Front Oncol"},{"key":"1787_CR4","doi-asserted-by":"publisher","first-page":"319","DOI":"10.3390\/biomedicines9030319","volume":"9","author":"C Ogasawara","year":"2021","unstructured":"Ogasawara C, Philbrick BD, Adamson DC. Meningioma: A review of epidemiology, pathology, diagnosis, treatment, and future directions. Biomedicines. 2021;9:319. https:\/\/doi.org\/10.3390\/biomedicines9030319.","journal-title":"Biomedicines"},{"key":"1787_CR5","doi-asserted-by":"publisher","first-page":"v1","DOI":"10.1093\/neuonc\/noz150","volume":"21","author":"QT Ostrom","year":"2019","unstructured":"Ostrom QT, Cioffi G, Gittleman H, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the united States in 2012\u20132016. Neuro-Oncol. 2019;21:v1\u2013100. https:\/\/doi.org\/10.1093\/neuonc\/noz150.","journal-title":"Neuro-Oncol"},{"key":"1787_CR6","doi-asserted-by":"publisher","first-page":"E1","DOI":"10.3171\/2013.8.FOCUS13274","volume":"35","author":"G Zada","year":"2013","unstructured":"Zada G, Yashar P, Robison A, et al. A proposed grading system for standardizing tumor consistency of intracranial meningiomas. Neurosurg Focus. 2013;35:E1. https:\/\/doi.org\/10.3171\/2013.8.FOCUS13274.","journal-title":"Neurosurg Focus"},{"key":"1787_CR7","doi-asserted-by":"publisher","first-page":"286","DOI":"10.3171\/2010.8.JNS10520","volume":"114","author":"G Zada","year":"2011","unstructured":"Zada G, Du R, Laws ER. Defining the edge of the envelope: patient selection in treating complex sellar-based neoplasms via transsphenoidal versus open craniotomy. J Neurosurg. 2011;114:286\u2013300. https:\/\/doi.org\/10.3171\/2010.8.JNS10520.","journal-title":"J Neurosurg"},{"key":"1787_CR8","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1177\/0284185115578323","volume":"57","author":"K Watanabe","year":"2016","unstructured":"Watanabe K, Kakeda S, Yamamoto J, et al. Prediction of hard meningiomas: quantitative evaluation based on the magnetic resonance signal intensity. Acta Radiol Stockh Swed 1987. 2016;57:333\u201340. https:\/\/doi.org\/10.1177\/0284185115578323.","journal-title":"Acta Radiol Stockh Swed 1987"},{"key":"1787_CR9","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1016\/j.wneu.2015.07.018","volume":"84","author":"LA Ortega-Porcayo","year":"2015","unstructured":"Ortega-Porcayo LA, Ballesteros-Zebad\u00faa P, Marrufo-Mel\u00e9ndez OR, et al. Prediction of mechanical properties and subjective consistency of meningiomas using T1-T2 assessment versus fractional anisotropy. World Neurosurg. 2015;84:1691\u20138. https:\/\/doi.org\/10.1016\/j.wneu.2015.07.018.","journal-title":"World Neurosurg"},{"key":"1787_CR10","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1007\/s10143-016-0801-0","volume":"41","author":"A Yao","year":"2018","unstructured":"Yao A, Pain M, Balchandani P, Shrivastava RK. Can MRI predict meningioma consistency?? A correlation with tumor pathology and systematic review. Neurosurg Rev. 2018;41:745\u201353. https:\/\/doi.org\/10.1007\/s10143-016-0801-0.","journal-title":"Neurosurg Rev"},{"key":"1787_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10143-017-0862-8","volume":"42","author":"AG Chartrain","year":"2019","unstructured":"Chartrain AG, Kurt M, Yao A, et al. Utility of preoperative meningioma consistency measurement with magnetic resonance elastography (MRE): a review. Neurosurg Rev. 2019;42:1\u20137. https:\/\/doi.org\/10.1007\/s10143-017-0862-8.","journal-title":"Neurosurg Rev"},{"key":"1787_CR12","unstructured":"Magnetic resonance imaging of meningiomas. a pictorial review| Insights into Imaging. https:\/\/link.springer.com\/article\/10.1007\/s13244-013-0302-4. Accessed 8 Aug 2024."},{"key":"1787_CR13","doi-asserted-by":"publisher","first-page":"142","DOI":"10.4103\/2152-7806.85983","volume":"2","author":"JM Hoover","year":"2011","unstructured":"Hoover JM, Morris JM, Meyer FB. Use of preoperative magnetic resonance imaging T1 and T2 sequences to determine intraoperative meningioma consistency. Surg Neurol Int. 2011;2:142. https:\/\/doi.org\/10.4103\/2152-7806.85983.","journal-title":"Surg Neurol Int"},{"issue":"3 Pt 1","key":"1787_CR14","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1148\/radiology.170.3.2916043","volume":"170","author":"AD Elster","year":"1989","unstructured":"Elster AD, et al. Meningiomas: MR and histopathologic features. Radiology. 1989;170(3 Pt 1):857\u201362. https:\/\/doi.org\/10.1148\/radiology.170.3.2916043. [DOI] [PubMed] [Google Scholar].","journal-title":"Radiology"},{"key":"1787_CR15","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1227\/00006123-199212000-00005","volume":"31","author":"TC Chen","year":"1992","unstructured":"Chen TC, Zee CS, Miller CA, et al. Magnetic resonance imaging and pathological correlates of meningiomas. Neurosurgery. 1992;31:1015\u201321. https:\/\/doi.org\/10.1227\/00006123-199212000-00005. discussion 1021\u20131022.","journal-title":"Neurosurgery"},{"key":"1787_CR16","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/s0720-048x(98)00083-7","volume":"31","author":"F Maiuri","year":"1999","unstructured":"Maiuri F, Iaconetta G, de Divitiis O, et al. Intracranial meningiomas: correlations between MR imaging and histology. Eur J Radiol. 1999;31:69\u201375. https:\/\/doi.org\/10.1016\/s0720-048x(98)00083-7.","journal-title":"Eur J Radiol"},{"key":"1787_CR17","doi-asserted-by":"publisher","first-page":"643","DOI":"10.3171\/2012.9.JNS12519","volume":"118","author":"MC Murphy","year":"2013","unstructured":"Murphy MC, Huston J, Glaser KJ, et al. Preoperative assessment of meningioma stiffness using magnetic resonance elastography. J Neurosurg. 2013;118:643\u20138. https:\/\/doi.org\/10.3171\/2012.9.JNS12519.","journal-title":"J Neurosurg"},{"key":"1787_CR18","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/BF00345721","volume":"18","author":"B Kendall","year":"1979","unstructured":"Kendall B, Pullicino P. Comparison of consistency of meningiomas and CT appearances. Neuroradiology. 1979;18:173\u20136. https:\/\/doi.org\/10.1007\/BF00345721.","journal-title":"Neuroradiology"},{"key":"1787_CR19","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.clineuro.2010.11.008","volume":"113","author":"MF Chernov","year":"2011","unstructured":"Chernov MF, Kasuya H, Nakaya K, et al. ) 1H-MRS of intracranial meningiomas: what it can add to known clinical and MRI predictors of the histopathological and biological characteristics of the tumor? Clin Neurol Neurosurg. 2011;113:202\u201312. https:\/\/doi.org\/10.1016\/j.clineuro.2010.11.008.","journal-title":"Clin Neurol Neurosurg"},{"key":"1787_CR20","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.oooo.2024.01.016","volume":"138","author":"D Wang","year":"2024","unstructured":"Wang D, He X, Huang C, et al. Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongue. Oral Surg Oral Med Oral Pathol Oral Radiol. 2024;138:214\u201324. https:\/\/doi.org\/10.1016\/j.oooo.2024.01.016.","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"key":"1787_CR21","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, et al. Radiomics and machine learning May accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol. 2019;29:4068\u201376. https:\/\/doi.org\/10.1007\/s00330-018-5830-3.","journal-title":"Eur Radiol"},{"key":"1787_CR22","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1007\/s10278-024-01024-x","volume":"37","author":"Y Gui","year":"2024","unstructured":"Gui Y, Chen F, Ren J, et al. MRI- and DWI-Based radiomics features for preoperatively predicting meningioma sinus invasion. J Imaging Inf Med. 2024;37:1054\u201366. https:\/\/doi.org\/10.1007\/s10278-024-01024-x.","journal-title":"J Imaging Inf Med"},{"key":"1787_CR23","unstructured":"Preoperative Prediction of Meningioma Consistency via Machine Learning-Based. Radiomics - PubMed. https:\/\/pubmed.ncbi.nlm.nih.gov\/34123812\/. Accessed 9 Aug 2024."},{"key":"1787_CR24","doi-asserted-by":"publisher","first-page":"e1147","DOI":"10.1016\/j.wneu.2020.11.113","volume":"146","author":"S Cepeda","year":"2021","unstructured":"Cepeda S, Arrese I, Garc\u00eda-Garc\u00eda S, et al. Meningioma consistency can be defined by combining the radiomic features of magnetic resonance imaging and ultrasound elastography. A pilot study using machine learning classifiers. World Neurosurg. 2021;146:e1147\u201359. https:\/\/doi.org\/10.1016\/j.wneu.2020.11.113.","journal-title":"World Neurosurg"},{"key":"1787_CR25","doi-asserted-by":"publisher","first-page":"111250","DOI":"10.1016\/j.ejrad.2023.111250","volume":"170","author":"J Zhang","year":"2024","unstructured":"Zhang J, Zhao Y, Lu Y, et al. Meningioma consistency assessment based on the fusion of deep learning features and radiomics features. Eur J Radiol. 2024;170:111250. https:\/\/doi.org\/10.1016\/j.ejrad.2023.111250.","journal-title":"Eur J Radiol"},{"key":"1787_CR26","doi-asserted-by":"publisher","first-page":"111136","DOI":"10.1016\/j.ejrad.2023.111136","volume":"168","author":"H Zhou","year":"2023","unstructured":"Zhou H, Bai HX, Jiao Z, et al. Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study. Eur J Radiol. 2023;168:111136. https:\/\/doi.org\/10.1016\/j.ejrad.2023.111136.","journal-title":"Eur J Radiol"},{"key":"1787_CR27","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1002\/cncr.34540","volume":"129","author":"J Gu","year":"2023","unstructured":"Gu J, Tong T, Xu D, et al. Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study. Cancer. 2023;129:356\u201366. https:\/\/doi.org\/10.1002\/cncr.34540.","journal-title":"Cancer"},{"key":"1787_CR28","doi-asserted-by":"publisher","first-page":"20220841","DOI":"10.1259\/bjr.20220841","volume":"96","author":"G Tulum","year":"2023","unstructured":"Tulum G. Novel radiomic features versus deep learning: differentiating brain metastases from pathological lung cancer types in small datasets. Br J Radiol. 2023;96:20220841. https:\/\/doi.org\/10.1259\/bjr.20220841.","journal-title":"Br J Radiol"},{"key":"1787_CR29","doi-asserted-by":"publisher","first-page":"2997","DOI":"10.1007\/s00330-023-10258-2","volume":"34","author":"J Chen","year":"2024","unstructured":"Chen J, Xue Y, Ren L, et al. Predicting meningioma grades and pathologic marker expression via deep learning. Eur Radiol. 2024;34:2997\u20133008. https:\/\/doi.org\/10.1007\/s00330-023-10258-2.","journal-title":"Eur Radiol"},{"key":"1787_CR30","doi-asserted-by":"publisher","first-page":"8858","DOI":"10.1007\/s00330-023-09869-6","volume":"33","author":"P Nie","year":"2023","unstructured":"Nie P, Yang G, Wang Y, et al. A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study. Eur Radiol. 2023;33:8858\u201368. https:\/\/doi.org\/10.1007\/s00330-023-09869-6.","journal-title":"Eur Radiol"},{"key":"1787_CR31","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1055\/s-0034-1543965","volume":"76","author":"KA Smith","year":"2015","unstructured":"Smith KA, Leever JD, Chamoun RB. Predicting consistency of meningioma by magnetic resonance imaging. J Neurol Surg Part B Skull Base. 2015;76:225\u20139. https:\/\/doi.org\/10.1055\/s-0034-1543965.","journal-title":"J Neurol Surg Part B Skull Base"},{"key":"1787_CR32","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1186\/s40644-023-00633-z","volume":"23","author":"L Zheng","year":"2023","unstructured":"Zheng L, Jiang P, Lin D, et al. Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas. Cancer Imaging Off Publ Int Cancer Imaging Soc. 2023;23:117. https:\/\/doi.org\/10.1186\/s40644-023-00633-z.","journal-title":"Cancer Imaging Off Publ Int Cancer Imaging Soc"},{"key":"1787_CR33","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.3171\/2018.7.JNS1838","volume":"131","author":"K Itamura","year":"2019","unstructured":"Itamura K, Chang K-E, Lucas J, et al. Prospective clinical validation of a meningioma consistency grading scheme: association with surgical outcomes and extent of tumor resection. J Neurosurg. 2019;131:1356\u201360. https:\/\/doi.org\/10.3171\/2018.7.JNS1838.","journal-title":"J Neurosurg"},{"key":"1787_CR34","doi-asserted-by":"publisher","first-page":"107634","DOI":"10.1016\/j.isci.2023.107634","volume":"26","author":"X Chu","year":"2023","unstructured":"Chu X, Niu L, Yang X, et al. Radiomics and deep learning models to differentiate lung adenosquamous carcinoma: A multicenter trial. iScience. 2023;26:107634. https:\/\/doi.org\/10.1016\/j.isci.2023.107634.","journal-title":"iScience"},{"key":"1787_CR35","unstructured":"A radiomics model. enables prediction venous sinus invasion in meningioma - PubMed. https:\/\/pubmed.ncbi.nlm.nih.gov\/37408500\/. Accessed 9 Aug 2024."},{"key":"1787_CR36","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 J-H, Chen T-Y, et al. Radiomics approach for prediction of recurrence in skull base meningiomas. Neuroradiology. 2019;61:1355\u201364. https:\/\/doi.org\/10.1007\/s00234-019-02259-0.","journal-title":"Neuroradiology"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01787-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01787-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01787-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T08:53:02Z","timestamp":1751359982000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01787-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1787"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01787-x","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"assertion":[{"value":"16 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 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":"This study was approved by the Clinical Research and Application Institutional Review Board of The Second Affiliated Hospital of Guangzhou Medical University (Approval No. 2023-hg-ks-24, Approval Date: 2023-08-28). All procedures conducted in this study complied with the ICH GCP guidelines, government regulations, laws, and the Declaration of Helsinki. Written informed consent was obtained from all participants.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors provided their consent for the publication of this manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"216"}}