{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T09:24:48Z","timestamp":1788341088191,"version":"build-2803163510"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T00:00:00Z","timestamp":1747094400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T00:00:00Z","timestamp":1747094400000},"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":["Sci Rep"],"DOI":"10.1038\/s41598-025-01665-0","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T12:38:47Z","timestamp":1747139927000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An MRI-based deep transfer learning radiomics nomogram for predicting meningioma grade"],"prefix":"10.1038","volume":"15","author":[{"given":"Nan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuejun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaona","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xushun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongfeng","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,13]]},"reference":[{"key":"1665_CR1","doi-asserted-by":"publisher","first-page":"101562","DOI":"10.1016\/j.canep.2019.07.001","volume":"62","author":"B Holleczek","year":"2019","unstructured":"Holleczek, B. et al. Incidence, mortality and outcome of meningiomas: A population-based study from Germany. Cancer Epidemiol. 62, 101562. https:\/\/doi.org\/10.1016\/j.canep.2019.07.001 (2019).","journal-title":"Cancer Epidemiol."},{"issue":"3","key":"1665_CR2","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/s11060-010-0386-3","volume":"99","author":"J Wiemels","year":"2010","unstructured":"Wiemels, J., Wrensch, M. & Claus, E. B. Epidemiology and etiology of meningioma. J. Neurooncol. 99 (3), 307\u2013314. https:\/\/doi.org\/10.1007\/s11060-010-0386-3 (2010).","journal-title":"J. Neurooncol"},{"key":"1665_CR3","unstructured":"WHO classification of tumours editorial board. World Health Organization Classification of Tumours of the Central Nervous System 5th edn (International Agency for Research on Cancer, 2021)."},{"issue":"1","key":"1665_CR4","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 (1), 4\u201323. https:\/\/doi.org\/10.3171\/2014.7.JNS131644 (2015).","journal-title":"J. Neurosurg."},{"issue":"11","key":"1665_CR5","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 (11), 1821\u20131834. https:\/\/doi.org\/10.1093\/neuonc\/noab150 (2021).","journal-title":"Neuro Oncol."},{"issue":"9420","key":"1665_CR6","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1016\/S0140-6736(04)16153-9","volume":"363","author":"IR Whittle","year":"2004","unstructured":"Whittle, I. R., Smith, C., Navoo, P., Collie, D. & Meningiomas Lancet ;363(9420):1535\u20131543. doi:https:\/\/doi.org\/10.1016\/S0140-6736(04)16153-9 (2004).","journal-title":"Lancet"},{"issue":"1","key":"1665_CR7","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s11060-019-03278-w","volume":"145","author":"D Li","year":"2019","unstructured":"Li, D. et al. Survival impacts of extent of resection and adjuvant radiotherapy for the modern management of high-grade meningiomas. J. Neurooncol. 145 (1), 125\u2013134. https:\/\/doi.org\/10.1007\/s11060-019-03278-w (2019).","journal-title":"J. Neurooncol"},{"issue":"6","key":"1665_CR8","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s007010170060","volume":"143","author":"PM Black","year":"2001","unstructured":"Black, P. M., 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 (6), 555\u2013562. https:\/\/doi.org\/10.1007\/s007010170060 (2001).","journal-title":"Acta Neurochir. (Wien)"},{"issue":"2","key":"1665_CR9","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: images are more than pictures, they are data. Radiology 278 (2), 563\u2013577. https:\/\/doi.org\/10.1148\/radiol.2015151169 (2016).","journal-title":"Radiology"},{"issue":"9","key":"1665_CR10","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1016\/j.mri.2012.06.010","volume":"30","author":"V Kumar","year":"2012","unstructured":"Kumar, V. et al. Radiomics: the process and the challenges. Magn. Reson. Imaging. 30 (9), 1234\u20131248. https:\/\/doi.org\/10.1016\/j.mri.2012.06.010 (2012).","journal-title":"Magn. Reson. Imaging"},{"issue":"4","key":"1665_CR11","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. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer. 48 (4), 441\u2013446. https:\/\/doi.org\/10.1016\/j.ejca.2011.11.036 (2012).","journal-title":"Eur. J. Cancer"},{"issue":"2","key":"1665_CR12","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1007\/s00330-021-08221-0","volume":"32","author":"S Liu","year":"2022","unstructured":"Liu, S. et al. Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study. Eur. Radiol. 32 (2), 793\u2013805 (2022).","journal-title":"Eur. Radiol."},{"issue":"22","key":"1665_CR13","doi-asserted-by":"publisher","first-page":"00612","DOI":"10.1016\/j.acra.2022.11.007","volume":"S1076-6332","author":"YM Zheng","year":"2022","unstructured":"Zheng, Y. M. et al. A CT-Based deep learning radiomics nomogram to predict histological grades of head and neck squamous cell carcinoma [published online ahead of print, 2022 Nov 30]. Acad. Radiol. S1076-6332 (22), 00612\u201300612. https:\/\/doi.org\/10.1016\/j.acra.2022.11.007 (2022).","journal-title":"Acad. Radiol."},{"issue":"1137","key":"1665_CR14","doi-asserted-by":"publisher","first-page":"20220141","DOI":"10.1259\/bjr.20220141","volume":"95","author":"C Duan","year":"2022","unstructured":"Duan, C. et al. A radiomics nomogram for predicting the meningioma grade based on enhanced T1WI images. Br. J. Radiol. 95 (1137), 20220141. https:\/\/doi.org\/10.1259\/bjr.20220141 (2022).","journal-title":"Br. J. Radiol."},{"issue":"4","key":"1665_CR15","doi-asserted-by":"publisher","first-page":"e302","DOI":"10.1016\/j.crad.2022.01.039","volume":"77","author":"CF Duan","year":"2022","unstructured":"Duan, C. F. et al. Comparison of different radiomic models based on enhanced T1-weighted images to predict the meningioma grade. Clin. Radiol. 77 (4), e302\u2013e307. https:\/\/doi.org\/10.1016\/j.crad.2022.01.039 (2022).","journal-title":"Clin. Radiol."},{"issue":"8","key":"1665_CR16","doi-asserted-by":"publisher","first-page":"4068","DOI":"10.1007\/s00330-018-5830-3","volume":"29","author":"YW Park","year":"2019","unstructured":"Park, Y. W. et al. Radiomics and machine learning May accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur. Radiol. 29 (8), 4068\u20134076. https:\/\/doi.org\/10.1007\/s00330-018-5830-3 (2019).","journal-title":"Eur. Radiol."},{"issue":"3","key":"1665_CR17","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1007\/s00330-018-5632-7","volume":"29","author":"Y Lu","year":"2019","unstructured":"Lu, Y. et al. The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest. Eur. Radiol. 29 (3), 1318\u20131328. https:\/\/doi.org\/10.1007\/s00330-018-5632-7 (2019).","journal-title":"Eur. Radiol."},{"issue":"6","key":"1665_CR18","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 (6), 1810\u20131820. https:\/\/doi.org\/10.1002\/jmri.26976 (2020).","journal-title":"J. Magn. Reson. Imaging"},{"key":"1665_CR19","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. 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":"1665_CR20","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. et al. 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 (2019).","journal-title":"Eur. J. Radiol."},{"issue":"7","key":"1665_CR21","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1007\/s00234-022-02894-0","volume":"64","author":"L Yang","year":"2022","unstructured":"Yang, L. et al. A deep learning radiomics model May help to improve the prediction performance of preoperative grading in meningioma. Neuroradiology 64 (7), 1373\u20131382. https:\/\/doi.org\/10.1007\/s00234-022-02894-0 (2022).","journal-title":"Neuroradiology"},{"key":"1665_CR22","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N. J. et al. N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging. 29, 1310\u20131320 (2010).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1665_CR23","doi-asserted-by":"publisher","first-page":"1157379","DOI":"10.3389\/fonc.2023.1157379","volume":"13","author":"C Duan","year":"2023","unstructured":"Duan, C. et al. Performance comparison of 2D and 3D MRI radiomics features in meningioma grade prediction: A preliminary study. Front. Oncol. 13, 1157379. https:\/\/doi.org\/10.3389\/fonc.2023.1157379 (2023). Published 2023 Mar 23.","journal-title":"Front. Oncol."},{"key":"1665_CR24","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.media.2013.10.005","volume":"18","author":"A Depeursinge","year":"2014","unstructured":"Depeursinge, A., Foncubierta-Rodriguez, A., Van De Ville, D. & Muller, H. Three-dimensional solid texture analysis in biomedical imaging: review and opportunities. Med. Image Anal. 18, 176\u2013196 (2014).","journal-title":"Med. Image Anal."},{"issue":"5","key":"1665_CR25","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 (5), 687\u2013693. https:\/\/doi.org\/10.1016\/j.acra.2020.03.034 (2021).","journal-title":"Acad. Radiol."},{"key":"1665_CR26","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.3389\/fonc.2019.01338","volume":"9","author":"C Chen","year":"2019","unstructured":"Chen, C. et al. The diagnostic value of Radiomics-Based machine learning in predicting the grade of meningiomas using conventional magnetic resonance imaging: A preliminary study. Front. Oncol. 9, 1338. https:\/\/doi.org\/10.3389\/fonc.2019.01338 (2019). Published 2019 Dec 6.","journal-title":"Front. Oncol."},{"issue":"12","key":"1665_CR27","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin, P. et al. Radiomics: the Bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14 (12), 749\u2013762. https:\/\/doi.org\/10.1038\/nrclinonc.2017.141 (2017).","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"1665_CR28","doi-asserted-by":"publisher","unstructured":"Aerts, H. J. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach [published correction appears in  Nat Commun 5 4644. Cavalho, Sara [corrected to Carvalho, Sara]].  Nat Commun. 2014 5 4006. (Published, 2014). https:\/\/doi.org\/10.1038\/ncomms5006","DOI":"10.1038\/ncomms5006"},{"key":"1665_CR29","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005 (2017).","journal-title":"Med. Image Anal."},{"issue":"12","key":"1665_CR30","doi-asserted-by":"publisher","first-page":"6334","DOI":"10.1002\/mp.14538","volume":"47","author":"D Arefan","year":"2020","unstructured":"Arefan, D., Chai, R., Sun, M., Zuley, M. L. & Wu, S. Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic features. Med. Phys. 47 (12), 6334\u20136342. https:\/\/doi.org\/10.1002\/mp.14538 (2020).","journal-title":"Med. Phys."},{"issue":"3","key":"1665_CR31","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1109\/JBHI.2020.3002805","volume":"25","author":"L Meng","year":"2021","unstructured":"Meng, L. et al. 2D and 3D CT radiomic features performance comparison in characterization of gastric cancer: A multi-center study. IEEE J. Biomed. Health Inf. 25 (3), 755\u2013763. https:\/\/doi.org\/10.1109\/JBHI.2020.3002805 (2021).","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"Suppl 2","key":"1665_CR32","doi-asserted-by":"publisher","first-page":"S161","DOI":"10.1016\/j.acra.2022.12.038","volume":"30","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y. et al. Deep Learning-based automatic diagnosis of breast Cancer on MRI using mask R-CNN for detection followed by ResNet50 for classification. Acad. Radiol. 30 (Suppl 2), S161\u2013S171. https:\/\/doi.org\/10.1016\/j.acra.2022.12.038 (2023).","journal-title":"Acad. Radiol."},{"issue":"1","key":"1665_CR33","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1186\/s12891-024-07355-8","volume":"25","author":"H Wang","year":"2024","unstructured":"Wang, H., Ying, J., Liu, J., Yu, T. & Huang, D. Harnessing ResNet50 and senet for enhanced ankle fracture identification. BMC Musculoskelet. Disord. 25 (1), 250. https:\/\/doi.org\/10.1186\/s12891-024-07355-8 (2024). Published 2024 Apr 1.","journal-title":"BMC Musculoskelet. Disord"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01665-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01665-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01665-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T12:38:48Z","timestamp":1747139928000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01665-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,13]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1665"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-01665-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,13]]},"assertion":[{"value":"29 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 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 authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"16614"}}