{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T03:18:25Z","timestamp":1790219905255,"version":"4.1.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031089985","type":"print"},{"value":"9783031089992","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-08999-2_11","type":"book-chapter","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T11:11:46Z","timestamp":1658401906000},"page":"140-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Predicting Isocitrate Dehydrogenase Mutation Status in\u00a0Glioma Using Structural Brain Networks and\u00a0Graph Neural Networks"],"prefix":"10.1007","author":[{"given":"Yiran","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carola-Bibiane","family":"Sch\u00f6nlieb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen J.","family":"Price","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"issue":"365","key":"11_CR1","first-page":"1","volume":"2","author":"BB Avants","year":"2009","unstructured":"Avants, B.B., Tustison, N., Song, G., et al.: Advanced normalization tools (ants). Insight J. 2(365), 1\u201335 (2009)","journal-title":"Insight J."},{"issue":"1","key":"11_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 1\u201313 (2017)","journal-title":"Sci. Data"},{"key":"11_CR3","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1146\/annurev-clinpsy-040510-143934","volume":"7","author":"ET Bullmore","year":"2011","unstructured":"Bullmore, E.T., Bassett, D.S.: Brain graphs: graphical models of the human brain connectome. Annu. Rev. Clin. Psychol. 7, 113\u2013140 (2011)","journal-title":"Annu. Rev. Clin. Psychol."},{"issue":"2","key":"11_CR4","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1093\/neuonc\/noaa177","volume":"23","author":"YS Choi","year":"2021","unstructured":"Choi, Y.S., et al.: Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics. Neuro Oncol. 23(2), 304\u2013313 (2021)","journal-title":"Neuro Oncol."},{"issue":"6","key":"11_CR5","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1093\/brain\/awv075","volume":"138","author":"ED Fagerholm","year":"2015","unstructured":"Fagerholm, E.D., Hellyer, P.J., Scott, G., Leech, R., Sharp, D.J.: Disconnection of network hubs and cognitive impairment after traumatic brain injury. Brain 138(6), 1696\u20131709 (2015)","journal-title":"Brain"},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.ejrad.2019.03.003","volume":"114","author":"H Hyare","year":"2019","unstructured":"Hyare, H., et al.: Modelling MR and clinical features in grade II\/III astrocytomas to predict IDH mutation status. Eur. J. Radiol. 114, 120\u2013127 (2019)","journal-title":"Eur. J. Radiol."},{"issue":"2","key":"11_CR7","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","volume":"62","author":"M Jenkinson","year":"2012","unstructured":"Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. Neuroimage 62(2), 782\u2013790 (2012)","journal-title":"Neuroimage"},{"issue":"2","key":"11_CR8","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/S1361-8415(01)00036-6","volume":"5","author":"M Jenkinson","year":"2001","unstructured":"Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5(2), 143\u2013156 (2001)","journal-title":"Med. Image Anal."},{"issue":"9","key":"11_CR9","doi-asserted-by":"publisher","first-page":"4718","DOI":"10.1007\/s00330-018-5984-z","volume":"29","author":"C Li","year":"2019","unstructured":"Li, C., et al.: Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma. Eur. Radiol. 29(9), 4718\u20134729 (2019)","journal-title":"Eur. Radiol."},{"issue":"5","key":"11_CR10","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.3171\/2018.12.JNS182926","volume":"132","author":"C Li","year":"2019","unstructured":"Li, C., et al.: Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging. J. Neurosurg. 132(5), 1465\u20131472 (2019)","journal-title":"J. Neurosurg."},{"issue":"8","key":"11_CR11","doi-asserted-by":"publisher","first-page":"382","DOI":"10.3390\/genes9080382","volume":"9","author":"S Liang","year":"2018","unstructured":"Liang, S., et al.: Multimodal 3D densenet for IDH genotype prediction in gliomas. Genes 9(8), 382 (2018)","journal-title":"Genes"},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3389\/fnhum.2020.00023","volume":"14","author":"Y Liu","year":"2020","unstructured":"Liu, Y., et al.: Altered rich-club organization and regional topology are associated with cognitive decline in patients with frontal and temporal gliomas. Front. Hum. Neurosci. 14, 23 (2020)","journal-title":"Front. Hum. Neurosci."},{"issue":"6","key":"11_CR13","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/s00401-016-1545-1","volume":"131","author":"DN Louis","year":"2016","unstructured":"Louis, D.N., et al.: The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 131(6), 803\u2013820 (2016)","journal-title":"Acta Neuropathol."},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Morris, C., et al.: Weisfeiler and leman go neural: Higher-order graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4602\u20134609 (2019)","DOI":"10.1609\/aaai.v33i01.33014602"},{"issue":"2","key":"11_CR15","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/42.836373","volume":"19","author":"LG Ny\u00fal","year":"2000","unstructured":"Ny\u00fal, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2), 143\u2013150 (2000)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Ostrom, Q.T., et al.: CBtrus statistical report: primary brain and other central nervous system tumors diagnosed in the united states in 2009\u20132013. Neuro-oncol. 18(suppl_5), v1\u2013v75 (2016)","DOI":"10.1093\/neuonc\/now207"},{"key":"11_CR17","doi-asserted-by":"publisher","unstructured":"Pedano, N., et al.: Radiology data from the cancer genome atlas low grade glioma [TCGA-LGG] collection. Cancer Imaging Arch. (2016). https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.L4LTD3TK","DOI":"10.7937\/K9\/TCIA.2016.L4LTD3TK"},{"issue":"1","key":"11_CR18","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1148\/radiol.2016152679","volume":"283","author":"SJ Price","year":"2017","unstructured":"Price, S.J., et al.: Less invasive phenotype found in isocitrate dehydrogenase-mutated glioblastomas than in isocitrate dehydrogenase wild-type glioblastomas: a diffusion-tensor imaging study. Radiology 283(1), 215\u2013221 (2017)","journal-title":"Radiology"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Salvalaggio, A., De Filippo De Grazia, M., Zorzi, M., Thiebaut de Schotten, M., Corbetta, M.: Post-stroke deficit prediction from lesion and indirect structural and functional disconnection. Brain 143(7), 2173\u20132188 (2020)","DOI":"10.1093\/brain\/awaa156"},{"key":"11_CR20","doi-asserted-by":"publisher","unstructured":"Scarpace, L., et al.: Radiology data from the cancer genome atlas glioblastoma multiforme [TCGA-GBM] collection [data set]. Cancer Imaging Arch. (2016). https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.RNYFUYE9","DOI":"10.7937\/K9\/TCIA.2016.RNYFUYE9"},{"key":"11_CR21","doi-asserted-by":"publisher","unstructured":"Shah, N., Feng, X., Lankerovich, M., Puchalski, R.B., Keogh, B.: Data from Ivy GAP [data set]. Cancer Imaging Arch. (2016). https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.XLWAN6NL","DOI":"10.7937\/K9\/TCIA.2016.XLWAN6NL"},{"issue":"3","key":"11_CR22","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/hbm.10062","volume":"17","author":"SM Smith","year":"2002","unstructured":"Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143\u2013155 (2002)","journal-title":"Hum. Brain Mapp."},{"issue":"1","key":"11_CR23","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/A:1007963824710","volume":"23","author":"SM Smith","year":"1997","unstructured":"Smith, S.M., Brady, J.M.: Susan - a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45\u201378 (1997)","journal-title":"Int. J. Comput. Vis."},{"issue":"9","key":"11_CR24","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1093\/neuonc\/noaa044","volume":"22","author":"VM Stoecklein","year":"2020","unstructured":"Stoecklein, V.M., et al.: Resting-state FMRI detects alterations in whole brain connectivity related to tumor biology in glioma patients. Neuro Oncol. 22(9), 1388\u20131398 (2020)","journal-title":"Neuro Oncol."},{"issue":"1","key":"11_CR25","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","unstructured":"Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273\u2013289 (2002)","journal-title":"Neuroimage"},{"issue":"1","key":"11_CR26","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1038\/s41593-018-0285-z","volume":"22","author":"J Wang","year":"2019","unstructured":"Wang, J., et al.: Invasion of white matter tracts by glioma stem cells is regulated by a notch1-sox2 positive-feedback loop. Nat. Neurosci. 22(1), 91\u2013105 (2019)","journal-title":"Nat. Neurosci."},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Wei, Y., et al.: Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients. bioRxiv (2021)","DOI":"10.1101\/2021.03.09.434656"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Wei, Y., Li, C., Price, S.: Quantifying structural connectivity in brain tumor patients. medRxiv (2021)","DOI":"10.1101\/2021.03.19.21253837"},{"issue":"8","key":"11_CR29","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1056\/NEJMoa0808710","volume":"360","author":"H Yan","year":"2009","unstructured":"Yan, H., et al.: IDH1 and IDH2 mutations in gliomas. N. Engl. J. Med. 360(8), 765\u2013773 (2009)","journal-title":"N. Engl. J. Med."},{"key":"11_CR30","first-page":"9240","volume":"32","author":"R Ying","year":"2019","unstructured":"Ying, R., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: GNNExplainer: generating explanations for graph neural networks. Adv. Neural. Inf. Process. Syst. 32, 9240 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Lecture Notes in Computer Science","Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08999-2_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T11:13:17Z","timestamp":1658401997000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08999-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031089985","9783031089992"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08999-2_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"22 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BrainLes","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International MICCAI Brainlesion Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.brainlesion-workshop.org\/?msclkid=7759e32ed14111ecba82c5ba435279db","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"151","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}