{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:09:13Z","timestamp":1792782553951,"version":"4.5.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T00:00:00Z","timestamp":1792195200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T00:00:00Z","timestamp":1792195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Frontier Technologies R&D Program of Jiangsu","award":["BF2025627"],"award-info":[{"award-number":["BF2025627"]}]},{"name":"Nanjing Outstanding Youth Talent Program","award":["JQX25013"],"award-info":[{"award-number":["JQX25013"]}]},{"name":"National Natural Science Foundation of China","award":["No. U24A20256, No. 62573434"],"award-info":[{"award-number":["No. U24A20256, No. 62573434"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Hunan Province","award":["No. 2025JJ50374"],"award-info":[{"award-number":["No. 2025JJ50374"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroradiology"],"DOI":"10.1007\/s00234-026-04219-x","type":"journal-article","created":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T08:06:11Z","timestamp":1792224371000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multi-stage multimodal learning model for functional outcomes prediction in acute ischemic stroke"],"prefix":"10.1007","author":[{"given":"Bin","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongping","family":"Ai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hulin","family":"Kuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,17]]},"reference":[{"key":"4219_CR1","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/s1474-4422(21)00252-0","volume":"20","author":"Global","year":"2021","unstructured":"Global (2021) regional, and national burden of stroke and its risk factors, 1990\u20132019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol 20:795\u2013820. https:\/\/doi.org\/10.1016\/s1474-4422(21)00252-0","journal-title":"Lancet Neurol"},{"key":"4219_CR2","doi-asserted-by":"publisher","first-page":"1480","DOI":"10.1161\/01.STR.0000170706.13595.4f","volume":"36","author":"DS Nichols-Larsen","year":"2005","unstructured":"Nichols-Larsen DS, Clark PC, Zeringue A, Greenspan A, Blanton S (2005) Factors influencing stroke survivors\u2019 quality of life during subacute recovery. Stroke 36:1480\u20131484. https:\/\/doi.org\/10.1161\/01.STR.0000170706.13595.4f","journal-title":"Stroke"},{"key":"4219_CR3","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1056\/NEJMoa1411587","volume":"372","author":"OA Berkhemer","year":"2015","unstructured":"Berkhemer OA, Fransen PS, Beumer D, van den Berg LA, Lingsma HF, Yoo AJ, Schonewille WJ, Vos JA, Nederkoorn PJ, Wermer MJ et al (2015) A randomized trial of intraarterial treatment for acute ischemic stroke. N Engl J Med 372:11\u201320. https:\/\/doi.org\/10.1056\/NEJMoa1411587","journal-title":"N Engl J Med"},{"key":"4219_CR4","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1056\/NEJMoa1713973","volume":"378","author":"GW Albers","year":"2018","unstructured":"Albers GW, Marks MP, Kemp S, Christensen S, Tsai JP, Ortega-Gutierrez S, McTaggart RA, Torbey MT, Kim-Tenser M, Leslie-Mazwi T et al (2018) Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging. N Engl J Med 378:708\u2013718. https:\/\/doi.org\/10.1056\/NEJMoa1713973","journal-title":"N Engl J Med"},{"key":"4219_CR5","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1016\/s0140-6736(23)02032-9","volume":"402","author":"M Bendszus","year":"2023","unstructured":"Bendszus M, Fiehler J, Subtil F, Bonekamp S, Aamodt AH, Fuentes B, Gizewski ER, Hill MD, Krajina A, Pierot L et al (2023) Endovascular thrombectomy for acute ischaemic stroke with established large infarct: multicentre, open-label, randomised trial. Lancet 402:1753\u20131763. https:\/\/doi.org\/10.1016\/s0140-6736(23)02032-9","journal-title":"Lancet"},{"key":"4219_CR6","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1056\/NEJMoa1414792","volume":"372","author":"BC Campbell","year":"2015","unstructured":"Campbell BC, Mitchell PJ, Kleinig TJ, Dewey HM, Churilov L, Yassi N, Yan B, Dowling RJ, Parsons MW, Oxley TJ et al (2015) Endovascular therapy for ischemic stroke with perfusion-imaging selection. N Engl J Med 372:1009\u20131018. https:\/\/doi.org\/10.1056\/NEJMoa1414792","journal-title":"N Engl J Med"},{"key":"4219_CR7","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1177\/0271678x17700666","volume":"38","author":"MR Etherton","year":"2018","unstructured":"Etherton MR, Rost NS, Wu O (2018) Infarct topography and functional outcomes. J Cereb Blood Flow Metab 38:1517\u20131532. https:\/\/doi.org\/10.1177\/0271678x17700666","journal-title":"J Cereb Blood Flow Metab"},{"key":"4219_CR8","doi-asserted-by":"publisher","first-page":"e220080","DOI":"10.1148\/radiol.220080","volume":"306","author":"H Fukutomi","year":"2023","unstructured":"Fukutomi H, Yamamoto T, Sibon I, Christensen S, Raposo N, Marnat G, Albucher JF, Olindo S, Calvi\u00e8re L, Sagnier S et al (2023) Location-weighted versus Volume-weighted Mismatch at MRI for Response to Mechanical Thrombectomy in Acute Stroke. Radiology 306:e220080. https:\/\/doi.org\/10.1148\/radiol.220080","journal-title":"Radiology"},{"key":"4219_CR9","doi-asserted-by":"publisher","first-page":"44","DOI":"10.2214\/ajr.18.20260","volume":"212","author":"Y Xie","year":"2019","unstructured":"Xie Y, Jiang B, Gong E, Li Y, Zhu G, Michel P, Wintermark M, Zaharchuk G (2019) JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information. AJR Am J Roentgenol 212:44\u201351. https:\/\/doi.org\/10.2214\/ajr.18.20260","journal-title":"AJR Am J Roentgenol"},{"key":"4219_CR10","doi-asserted-by":"publisher","first-page":"1723","DOI":"10.1016\/s0140-6736(16)00163-x","volume":"387","author":"M Goyal","year":"2016","unstructured":"Goyal M, Menon BK, van Zwam WH, Dippel DW, Mitchell PJ, Demchuk AM, D\u00e1valos A, Majoie CB, van der Lugt A, de Miquel MA et al (2016) Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet 387:1723\u20131731. https:\/\/doi.org\/10.1016\/s0140-6736(16)00163-x","journal-title":"Lancet"},{"key":"4219_CR11","doi-asserted-by":"publisher","unstructured":"Tolhuisen ML, Hoving JW, Koopman MS, Kappelhof M, van Voorst H, Bruggeman AE, Demchuck AM, Dippel DWJ, Emmer BJ, Bracard S et al (2022) Outcome Prediction Based on Automatically Extracted Infarct Core Image Features in Patients with Acute Ischemic Stroke. Diagnostics (Basel) 12. https:\/\/doi.org\/10.3390\/diagnostics12081786","DOI":"10.3390\/diagnostics12081786"},{"key":"4219_CR12","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"4219_CR13","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","volume":"29","author":"AS Lundervold","year":"2019","unstructured":"Lundervold AS, Lundervold A (2019) An overview of deep learning in medical imaging focusing on MRI. Z Med Phys 29:102\u2013127. https:\/\/doi.org\/10.1016\/j.zemedi.2018.11.002","journal-title":"Z Med Phys"},{"key":"4219_CR14","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1161\/strokeaha.118.024293","volume":"50","author":"J Heo","year":"2019","unstructured":"Heo J, Yoon JG, Park H, Kim YD, Nam HS, Heo JH (2019) Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke. Stroke 50:1263\u20131265. https:\/\/doi.org\/10.1161\/strokeaha.118.024293","journal-title":"Stroke"},{"key":"4219_CR15","doi-asserted-by":"publisher","first-page":"2316","DOI":"10.1161\/strokeaha.123.044072","volume":"54","author":"Y Liu","year":"2023","unstructured":"Liu Y, Yu Y, Ouyang J, Jiang B, Yang G, Ostmeier S, Wintermark M, Michel P, Liebeskind DS, Lansberg MG et al (2023) Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model. Stroke 54:2316\u20132327. https:\/\/doi.org\/10.1161\/strokeaha.123.044072","journal-title":"Stroke"},{"key":"4219_CR16","doi-asserted-by":"publisher","first-page":"3020","DOI":"10.1161\/str.0000000000000074","volume":"46","author":"WJ Powers","year":"2015","unstructured":"Powers WJ, Derdeyn CP, Biller J, Coffey CS, Hoh BL, Jauch EC, Johnston KC, Johnston SC, Khalessi AA, Kidwell CS et al (2015) 2015 American Heart Association\/American Stroke Association Focused Update of the 2013 Guidelines for the Early Management of Patients With Acute Ischemic Stroke Regarding Endovascular Treatment: A Guideline for Healthcare Professionals From the American Heart Association\/American Stroke Association. Stroke 46:3020\u20133035. https:\/\/doi.org\/10.1161\/str.0000000000000074","journal-title":"Stroke"},{"key":"4219_CR17","doi-asserted-by":"publisher","first-page":"e478","DOI":"10.1161\/circulationaha.107.181486","volume":"115","author":"HP del Adams Jr.","year":"2007","unstructured":"Adams HP Jr., del Zoppo G, Alberts MJ, Bhatt DL, Brass L, Furlan A, Grubb RL, Higashida RT, Jauch EC, Kidwell C et al (2007) Clinical Cardiology Council, Cardiovascular Radiology and Intervention Council, and the Atherosclerotic Peripheral Vascular Disease and Quality of Care Outcomes in Research Interdisciplinary Working Groups: The American Academy of Neurology affirms the value of this guideline as an educational tool for neurologists. Circulation 115:e478\u2013534. https:\/\/doi.org\/10.1161\/circulationaha.107.181486","journal-title":"Circulation"},{"key":"4219_CR18","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1161\/strokeaha.117.020138","volume":"49","author":"A Morotti","year":"2018","unstructured":"Morotti A, Dowlatshahi D, Boulouis G, Al-Ajlan F, Demchuk AM, Aviv RI, Yu L, Schwab K, Romero JM, Gurol ME et al (2018) Predicting Intracerebral Hemorrhage Expansion With Noncontrast Computed Tomography: The BAT Score. Stroke 49:1163\u20131169. https:\/\/doi.org\/10.1161\/strokeaha.117.020138","journal-title":"Stroke"},{"key":"4219_CR19","doi-asserted-by":"publisher","unstructured":"Eliasziw M, Paddock-Eliasziw L (2005) Comparison of MRI and CT for detection of acute intracerebral hemorrhage. Jama. ;293:550; author reply 550\u2013551. https:\/\/doi.org\/10.1001\/jama.293.5.550-a","DOI":"10.1001\/jama.293.5.550-a"},{"key":"4219_CR20","unstructured":"Bai F, Du Y, Huang T et al (2024) M3d: Advancing 3d medical image analysis with multi-modal large language models[J]. arXiv preprint arXiv:2404.00578"},{"issue":"2","key":"4219_CR21","first-page":"3","volume":"1","author":"EJ Hu","year":"2022","unstructured":"Hu EJ, Shen Y, Wallis P et al (2022) Lora: Low-rank adaptation of large language models[J]. Iclr 1(2):3","journal-title":"Iclr"},{"issue":"2","key":"4219_CR22","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1016\/j.neuroimage.2012.01.024","volume":"62","author":"AC Evans","year":"2012","unstructured":"Evans AC, Janke AL, Collins DL et al (2012) Brain templates and atlases[J]. NeuroImage 62(2):911\u2013922","journal-title":"NeuroImage"},{"key":"4219_CR23","doi-asserted-by":"crossref","unstructured":"Najm M, Kuang H, Federico A et al (2019) Automated brain extraction from head CT and CTA images using convex optimization with shape propagation[J], vol 176. Computer Methods and Programs in Biomedicine, pp 1\u20138","DOI":"10.1016\/j.cmpb.2019.04.030"},{"key":"4219_CR24","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1161\/01.Str.0000251792.76080.45","volume":"38","author":"NM Menezes","year":"2007","unstructured":"Menezes NM, Ay H, Wang Zhu M, Lopez CJ, Singhal AB, Karonen JO, Aronen HJ, Liu Y, Nuutinen J, Koroshetz WJ et al (2007) The real estate factor: quantifying the impact of infarct location on stroke severity. Stroke 38:194\u2013197. https:\/\/doi.org\/10.1161\/01.Str.0000251792.76080.45","journal-title":"Stroke"},{"key":"4219_CR25","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1161\/strokeaha.119.025373","volume":"50","author":"O Wu","year":"2019","unstructured":"Wu O, Winzeck S, Giese AK, Hancock BL, Etherton MR, Bouts M, Donahue K, Schirmer MD, Irie RE, Mocking SJT et al (2019) Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data. Stroke 50:1734\u20131741. https:\/\/doi.org\/10.1161\/strokeaha.119.025373","journal-title":"Stroke"},{"key":"4219_CR26","doi-asserted-by":"publisher","first-page":"3541","DOI":"10.1161\/strokeaha.120.030287","volume":"51","author":"G Brugnara","year":"2020","unstructured":"Brugnara G, Neuberger U, Mahmutoglu MA, Foltyn M, Herweh C, Nagel S, Sch\u00f6nenberger S, Heiland S, Ulfert C, Ringleb PA et al (2020) Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning. Stroke 51:3541\u20133551. https:\/\/doi.org\/10.1161\/strokeaha.120.030287","journal-title":"Stroke"},{"key":"4219_CR27","doi-asserted-by":"publisher","unstructured":"Sch\u00f6n F, Wahl H, Grey A, Krukowski P, M\u00fcller A, Puetz V, Linn J, Kaiser DPO (2023) Improved Visualization and Quantification of Net Water Uptake in Recent Small Subcortical Infarcts in the Thalamus Using Computed Tomography. Diagnostics (Basel) 13. https:\/\/doi.org\/10.3390\/diagnostics13223416","DOI":"10.3390\/diagnostics13223416"},{"key":"4219_CR28","doi-asserted-by":"publisher","first-page":"3393","DOI":"10.1161\/strokeaha.109.557256","volume":"40","author":"TJ Quinn","year":"2009","unstructured":"Quinn TJ, Dawson J, Walters MR, Lees KR (2009) Reliability of the modified Rankin Scale: a systematic review. Stroke 40:3393\u20133395. https:\/\/doi.org\/10.1161\/strokeaha.109.557256","journal-title":"Stroke"},{"key":"4219_CR29","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1161\/strokeaha.108.522516","volume":"40","author":"TJ Quinn","year":"2009","unstructured":"Quinn TJ, Dawson J, Walters MR, Lees KR (2009) Exploring the reliability of the modified rankin scale. Stroke 40:762\u2013766. https:\/\/doi.org\/10.1161\/strokeaha.108.522516","journal-title":"Stroke"},{"key":"4219_CR30","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.1161\/strokeaha.117.017866","volume":"48","author":"JP Broderick","year":"2017","unstructured":"Broderick JP, Adeoye O, Elm J (2017) Evolution of the Modified Rankin Scale and Its Use in Future Stroke Trials. Stroke 48:2007\u20132012. https:\/\/doi.org\/10.1161\/strokeaha.117.017866","journal-title":"Stroke"}],"container-title":["Neuroradiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00234-026-04219-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00234-026-04219-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00234-026-04219-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T17:03:01Z","timestamp":1792256581000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00234-026-04219-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,17]]},"references-count":30,"alternative-id":["4219"],"URL":"https:\/\/doi.org\/10.1007\/s00234-026-04219-x","relation":{},"ISSN":["0028-3940","1432-1920"],"issn-type":[{"value":"0028-3940","type":"print"},{"value":"1432-1920","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,17]]},"assertion":[{"value":"9 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}