{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T12:28:09Z","timestamp":1759667289167,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T00:00:00Z","timestamp":1689033600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T00:00:00Z","timestamp":1689033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin City","doi-asserted-by":"publisher","award":["20JCYBJC00960"],"award-info":[{"award-number":["20JCYBJC00960"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s00371-023-02978-9","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T16:01:33Z","timestamp":1689091293000},"page":"3561-3571","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-modal deep-fusion network for meningioma presurgical grading with integrative imaging and clinical data"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5501-9575","authenticated-orcid":false,"given":"Wennan","family":"Liu","sequence":"first","affiliation":[]},{"given":"Tianling","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Tong","family":"Han","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Wan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,11]]},"reference":[{"key":"2978_CR1","doi-asserted-by":"crossref","unstructured":"Abiwinanda, N., Hanif, M., Hesaputra, S.T., Handayani, A., Mengko, T.R.: Brain tumor classification using convolutional neural network. In: World congress on medical physics and biomedical engineering 2018, pp. 183\u2013189. Springer (2019)","DOI":"10.1007\/978-981-10-9035-6_33"},{"key":"2978_CR2","unstructured":"Banerjee, S., Mitra, S., Masulli, F., Rovetta, S.: Deep radiomics for brain tumor detection and classification from multi-sequence mri. arXiv preprint arXiv:1903.09240 (2019)"},{"key":"2978_CR3","unstructured":"Banerjee, S., Mitra, S., Masulli, F., Rovetta, S.: Deep radiomics for brain tumor detection and classification from multi-sequence mri. arXiv preprint arXiv:1903.09240 (2019)"},{"issue":"3","key":"2978_CR4","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s00701-012-1611-y","volume":"155","author":"JF Cornelius","year":"2013","unstructured":"Cornelius, J.F., Slotty, P.J., Steiger, H.J., H\u00e4nggi, D., Polivka, M., George, B.: Malignant potential of skull base versus non-skull base meningiomas: clinical series of 1,663 cases. Acta Neurochirurgica 155(3), 407\u2013413 (2013)","journal-title":"Acta Neurochirurgica"},{"key":"2978_CR5","first-page":"3965","volume":"34","author":"Z Dai","year":"2021","unstructured":"Dai, Z., Liu, H., Le, Q.V., Tan, M.: Coatnet: marrying convolution and attention for all data sizes. Adv. Neural Inf. Process. Syst. 34, 3965\u20133977 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2978_CR6","doi-asserted-by":"publisher","first-page":"103345","DOI":"10.1016\/j.compbiomed.2019.103345","volume":"111","author":"S Deepak","year":"2019","unstructured":"Deepak, S., Ameer, P.M.: Brain tumor classification using deep CNN features via transfer learning. Comput. Biol. Med. 111, 103345 (2019)","journal-title":"Comput. Biol. Med."},{"key":"2978_CR7","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"11","key":"2978_CR8","doi-asserted-by":"publisher","first-page":"5526","DOI":"10.1016\/j.eswa.2014.01.021","volume":"41","author":"ESA El-Dahshan","year":"2014","unstructured":"El-Dahshan, E.S.A., Mohsen, H.M., Revett, K., Salem, A.B.M.: Computer-aided diagnosis of human brain tumor through mri: a survey and a new algorithm. Expert Syst. Appl. 41(11), 5526\u20135545 (2014)","journal-title":"Expert Syst. Appl."},{"key":"2978_CR9","doi-asserted-by":"crossref","unstructured":"Guo, Z., Li, X., Huang, H., Guo, N., Li, Q.: Medical image segmentation based on multi-modal convolutional neural network: study on image fusion schemes. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 903\u2013907. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363717"},{"key":"2978_CR10","doi-asserted-by":"publisher","first-page":"66","DOI":"10.3389\/fonc.2013.00066","volume":"3","author":"A Hawkins-Daarud","year":"2013","unstructured":"Hawkins-Daarud, A., Rockne, R.C., Anderson, A.R., Swanson, K.R.: Modeling tumor-associated edema in gliomas during anti-angiogenic therapy and its impact on imageable tumor. Front. Oncol. 3, 66 (2013)","journal-title":"Front. Oncol."},{"key":"2978_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2978_CR12","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"2978_CR13","unstructured":"Jie, H., Li, S., Gang, S., Albanie, S.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. PP(99) (2017)"},{"key":"2978_CR14","unstructured":"Kunyi, Z.: Imaging principles and clinical applications of magnetic resonance imaging (mri). J. Med. Equip. (2008)"},{"issue":"8","key":"2978_CR15","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., Oh, J., You, S.C., Han, K., Ahn, S.S., Choi, Y.S., Chang, J.H., Kim, S.H., Lee, S.K.: 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 (2019)","journal-title":"Eur. Radiol."},{"key":"2978_CR16","doi-asserted-by":"crossref","unstructured":"Perez, E., Strub, F., De\u00a0Vries, H., Dumoulin, V., Courville, A.: Film: Visual reasoning with a general conditioning layer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"2978_CR17","doi-asserted-by":"crossref","unstructured":"P\u00f6lsterl, S., Wolf, T.N., Wachinger, C.: Combining 3d image and tabular data via the dynamic affine feature map transform. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 688\u2013698. Springer (2021)","DOI":"10.1007\/978-3-030-87240-3_66"},{"issue":"3","key":"2978_CR18","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1111\/1759-7714.13309","volume":"11","author":"Y Qu","year":"2020","unstructured":"Qu, Y., Zhu, H., Cao, K., Li, X., Sun, Y.: Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (dl) method. Thorac. Cancer 11(3), 651 (2020)","journal-title":"Thorac. Cancer"},{"issue":"11","key":"2978_CR19","doi-asserted-by":"publisher","first-page":"1604","DOI":"10.1634\/theoncologist.2011-0193","volume":"16","author":"S Saraf","year":"2011","unstructured":"Saraf, S., McCarthy, B.J., Villano, J.L.: Update on meningiomas. The Oncologist 16(11), 1604\u20131613 (2011)","journal-title":"The Oncologist"},{"issue":"06","key":"2978_CR20","first-page":"742","volume":"17","author":"Z Shaowei","year":"2017","unstructured":"Shaowei, Z., Liujun, H., Shaolei, G., Xioayi, L., Zhunyi, Z., Shaochun, S., Shujia, C.: Analysis of meningioma recurrence rates following treatment. Lingnan Modern Clin. Surg. 17(06), 742 (2017)","journal-title":"Lingnan Modern Clin. Surg."},{"key":"2978_CR21","doi-asserted-by":"crossref","unstructured":"Srinivas, A., Lin, T.Y., Parmar, N., Shlens, J., Abbeel, P., Vaswani, A.: Bottleneck transformers for visual recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 16,519\u201316,529 (2021)","DOI":"10.1109\/CVPR46437.2021.01625"},{"issue":"6","key":"2978_CR22","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1016\/j.tranon.2015.11.012","volume":"8","author":"A Surov","year":"2015","unstructured":"Surov, A., Gottschling, S., Mawrin, C., Prell, J., Spielmann, R.P., Wienke, A., Fiedler, E.: Diffusion-weighted imaging in meningioma: prediction of tumor grade and association with histopathological parameters. Transl. Oncol. 8(6), 517\u2013523 (2015)","journal-title":"Transl. Oncol."},{"key":"2978_CR23","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.cogsys.2018.12.007","volume":"54","author":"M Talo","year":"2019","unstructured":"Talo, M., Baloglu, U.B., Y\u0131ld\u0131r\u0131m, \u00d6., Acharya, U.R.: Application of deep transfer learning for automated brain abnormality classification using mr images. Cogn. Syst. Res. 54, 176\u2013188 (2019)","journal-title":"Cogn. Syst. Res."},{"key":"2978_CR24","doi-asserted-by":"crossref","unstructured":"Tseng, K.L., Lin, Y.L., Hsu, W., Huang, C.Y.: Joint sequence learning and cross-modality convolution for 3d biomedical segmentation. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 6393\u20136400 (2017)","DOI":"10.1109\/CVPR.2017.398"},{"issue":"4","key":"2978_CR25","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1016\/j.tranon.2017.04.006","volume":"10","author":"PF Yan","year":"2017","unstructured":"Yan, P.F., Yan, L., Hu, T.T., Xiao, D.D., Feng, J.: The potential value of preoperative mri texture and shape analysis in grading meningiomas: a preliminary investigation. Transl. Oncol. 10(4), 570\u2013577 (2017)","journal-title":"Transl. Oncol."},{"issue":"5","key":"2978_CR26","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1136\/jnnp.2007.121582","volume":"79","author":"SY Yang","year":"2008","unstructured":"Yang, S.Y., Park, C.K., Park, S.H., Kim, D.G., Chung, Y.S., Jung, H.W.: Atypical and anaplastic meningiomas: prognostic implications of clinicopathological features. J. Neurol. Neurosurg. Psychiatry 79(5), 574\u2013580 (2008)","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"issue":"12","key":"2978_CR27","doi-asserted-by":"publisher","first-page":"4050","DOI":"10.1016\/j.ejrad.2012.06.002","volume":"81","author":"B Yin","year":"2012","unstructured":"Yin, B., Liu, L., Zhang, B.Y., Li, Y.X., Li, Y., Geng, D.Y.: Correlating apparent diffusion coefficients with histopathologic findings on meningiomas. Eur. J. Radiol. 81(12), 4050\u20134056 (2012)","journal-title":"Eur. J. Radiol."},{"key":"2978_CR28","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Tong, Z., Han, Z., Zhang, Y., Zwiggelaar, R.: The classification of meningioma subtypes based on the color segmentation and shape features. In: Frontier and future development of information technology in medicine and education, pp. 2669\u20132674. Springer (2014)","DOI":"10.1007\/978-94-007-7618-0_335"},{"key":"2978_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, H., Berg, A.C., Maire, M., Malik, J.: Svm-knn: discriminative nearest neighbor classification for visual category recognition. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), vol.\u00a02, pp. 2126\u20132136. IEEE (2006)","DOI":"10.1109\/CVPR.2006.301"},{"issue":"3","key":"2978_CR30","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/s12021-020-09492-6","volume":"19","author":"H Zhang","year":"2021","unstructured":"Zhang, H., Mo, J., Jiang, H., Li, Z., Hu, W., Zhang, C., Wang, Y., Wang, X., Liu, C., Zhao, B., et al.: Deep learning model for the automated detection and histopathological prediction of meningioma. Neuroinformatics 19(3), 393\u2013402 (2021)","journal-title":"Neuroinformatics"},{"key":"2978_CR31","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"issue":"33","key":"2978_CR32","first-page":"1","volume":"2019","author":"H Zhu","year":"2019","unstructured":"Zhu, H., Fang, Q., He, H., Hu, J., Xu, K.: Automatic prediction of meningioma grade image based on data amplification and improved convolutional neural network. Comput. Math. Methods Med. 2019(33), 1\u20139 (2019)","journal-title":"Comput. Math. Methods Med."},{"key":"2978_CR33","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Man, C., Gong, L., Dong, D., Tian, J.: A deep learning radiomics model for preoperative grading in meningioma. Eur. J. Radiol. 116, 128\u2013134 (2019)","DOI":"10.1016\/j.ejrad.2019.04.022"},{"key":"2978_CR34","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., et al.: A deep learning radiomics model for preoperative grading in meningioma. Eur. J. Radiol. 116, 128\u2013134 (2019)","journal-title":"Eur. J. Radiol."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02978-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-02978-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02978-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T23:47:41Z","timestamp":1729727261000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-02978-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,11]]},"references-count":34,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["2978"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-02978-9","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2023,7,11]]},"assertion":[{"value":"9 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2023","order":2,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest of potential conflicts of interest."}}]}}