{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:13:14Z","timestamp":1785337994858,"version":"3.55.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Capital' s Funds for Health Improvement and Research","award":["2022-2-1074"],"award-info":[{"award-number":["2022-2-1074"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82371939"],"award-info":[{"award-number":["82371939"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-01722-0","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T16:36:35Z","timestamp":1748018195000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Development of a non-contrast CT-based radiomics nomogram for early prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage"],"prefix":"10.1186","volume":"25","author":[{"given":"Lingxu","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sihui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuening","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengyuan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengjun","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"issue":"5","key":"1722_CR1","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1001\/jamaneurol.2019.0006","volume":"76","author":"N Etminan","year":"2019","unstructured":"Etminan N, Chang HS, Hackenberg K, et al. Worldwide incidence of aneurysmal subarachnoid hemorrhage according to region, time period, blood pressure, and smoking prevalence in the population: A systematic review and Meta-analysis. JAMA Neurol. 2019;76(5):588\u201397.","journal-title":"JAMA Neurol"},{"issue":"10355","key":"1722_CR2","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1016\/S0140-6736(22)00938-2","volume":"400","author":"J Claassen","year":"2022","unstructured":"Claassen J, Park S. Spontaneous subarachnoid haemorrhage. Lancet. 2022;400(10355):846\u201362.","journal-title":"Lancet"},{"issue":"3","key":"1722_CR3","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1007\/s12975-020-00867-0","volume":"12","author":"SN Neifert","year":"2021","unstructured":"Neifert SN, Chapman EK, Martini ML, et al. Aneurysmal subarachnoid hemorrhage: the last decade. Transl Stroke Res. 2021;12(3):428\u201346.","journal-title":"Transl Stroke Res"},{"issue":"10","key":"1722_CR4","doi-asserted-by":"publisher","first-page":"2391","DOI":"10.1161\/STROKEAHA.110.589275","volume":"41","author":"MD Vergouwen","year":"2010","unstructured":"Vergouwen MD, Vermeulen M, van Gijn J, et al. Definition of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage as an outcome event in clinical trials and observational studies: proposal of a multidisciplinary research group. Stroke. 2010;41(10):2391\u20135.","journal-title":"Stroke"},{"issue":"11","key":"1722_CR5","doi-asserted-by":"publisher","first-page":"2958","DOI":"10.1161\/STROKEAHA.117.017777","volume":"48","author":"JP Galea","year":"2017","unstructured":"Galea JP, Dulhanty L, Patel HC. Predictors of outcome in aneurysmal subarachnoid hemorrhage patients: observations from a multicenter data set. Stroke. 2017;48(11):2958\u201363.","journal-title":"Stroke"},{"issue":"3","key":"1722_CR6","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1161\/STROKEAHA.115.011386","volume":"47","author":"NM Dubosh","year":"2016","unstructured":"Dubosh NM, Bellolio MF, Rabinstein AA, et al. Sensitivity of early brain computed tomography to exclude aneurysmal subarachnoid hemorrhage: A systematic review and Meta-Analysis. Stroke. 2016;47(3):750\u20135.","journal-title":"Stroke"},{"issue":"2","key":"1722_CR7","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1007\/s12028-009-9247-3","volume":"11","author":"SA Dupont","year":"2009","unstructured":"Dupont SA, Wijdicks EF, Manno EM, et al. Prediction of angiographic vasospasm after aneurysmal subarachnoid hemorrhage: value of the Hijdra sum scoring system. Neurocrit Care. 2009;11(2):172\u20136.","journal-title":"Neurocrit Care"},{"key":"1722_CR8","doi-asserted-by":"crossref","unstructured":"Couret D, Boussen S, Cardoso D, et al. Comparison of scales for the evaluation of aneurysmal subarachnoid haemorrhage: a retrospective cohort study. Eur Radiol. 2024.","DOI":"10.1007\/s00330-024-10814-4"},{"issue":"1","key":"1722_CR9","first-page":"21","volume":"59","author":"JA Frontera","year":"2006","unstructured":"Frontera JA, Claassen J, Schmidt JM, et al. Prediction of symptomatic vasospasm after subarachnoid hemorrhage: the modified fisher scale. Neurosurgery. 2006;59(1):21\u20137. discussion 21\u2013\u20097.","journal-title":"Neurosurgery"},{"issue":"2","key":"1722_CR10","doi-asserted-by":"publisher","first-page":"458","DOI":"10.3171\/2017.3.JNS162808","volume":"129","author":"MR Germans","year":"2018","unstructured":"Germans MR, Jaja B, de Oliviera Manoel AL, et al. Sex differences in delayed cerebral ischemia after subarachnoid hemorrhage. J Neurosurg. 2018;129(2):458\u201364.","journal-title":"J Neurosurg"},{"issue":"1","key":"1722_CR11","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1161\/STROKEAHA.112.674291","volume":"44","author":"NK de Rooij","year":"2013","unstructured":"de Rooij NK, Rinkel GJ, Dankbaar JW, et al. Delayed cerebral ischemia after subarachnoid hemorrhage: a systematic review of clinical, laboratory, and radiological predictors. Stroke. 2013;44(1):43\u201354.","journal-title":"Stroke"},{"key":"1722_CR12","doi-asserted-by":"publisher","first-page":"103242","DOI":"10.1016\/j.nicl.2022.103242","volume":"36","author":"X Huang","year":"2022","unstructured":"Huang X, Wang D, Zhang Q, et al. Radiomics for prediction of intracerebral hemorrhage outcomes: A retrospective multicenter study. Neuroimage Clin. 2022;36:103242.","journal-title":"Neuroimage Clin"},{"issue":"5","key":"1722_CR13","doi-asserted-by":"publisher","first-page":"2058","DOI":"10.1007\/s00330-017-5146-8","volume":"28","author":"L Yang","year":"2018","unstructured":"Yang L, Dong D, Fang M, et al. Can CT-based radiomics signature predict KRAS\/NRAS\/BRAF mutations in colorectal cancer. Eur Radiol. 2018;28(5):2058\u201367.","journal-title":"Eur Radiol"},{"key":"1722_CR14","doi-asserted-by":"crossref","unstructured":"Yu F, Yang M, He C et al. CT radiomics combined with clinical and radiological factors predict hematoma expansion in hypertensive intracerebral hemorrhage. Eur Radiol 2024.","DOI":"10.1007\/s00330-024-10921-2"},{"issue":"1","key":"1722_CR15","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1186\/s13244-022-01324-2","volume":"13","author":"S Zhang","year":"2022","unstructured":"Zhang S, Gao L, Kang B, et al. Radiomics assessment of carotid intraplaque hemorrhage: detecting the vulnerable patients. Insights Imaging. 2022;13(1):200.","journal-title":"Insights Imaging"},{"issue":"8","key":"1722_CR16","first-page":"967","volume":"10","author":"D Shan","year":"2023","unstructured":"Shan D, Wang J, Qi P, et al. Non-Contrasted CT radiomics for SAH prognosis prediction. Bioeng (Basel). 2023;10(8):967.","journal-title":"Bioeng (Basel)"},{"issue":"9","key":"1722_CR17","doi-asserted-by":"publisher","first-page":"13195","DOI":"10.18632\/aging.203001","volume":"13","author":"X Tong","year":"2021","unstructured":"Tong X, Feng X, Peng F, et al. Morphology-based radiomics signature: a novel determinant to identify multiple intracranial aneurysms rupture. Aging. 2021;13(9):13195\u2013210.","journal-title":"Aging"},{"issue":"4","key":"1722_CR18","doi-asserted-by":"publisher","first-page":"754","DOI":"10.2214\/AJR.16.17224","volume":"208","author":"M Kohli","year":"2017","unstructured":"Kohli M, Prevedello LM, Filice RW, et al. Implementing machine learning in radiology practice and research. AJR Am J Roentgenol. 2017;208(4):754\u201360.","journal-title":"AJR Am J Roentgenol"},{"issue":"2","key":"1722_CR19","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1148\/radiol.2018171820","volume":"288","author":"G Choy","year":"2018","unstructured":"Choy G, Khalilzadeh O, Michalski M, et al. Current applications and future impact of machine learning in radiology. Radiology. 2018;288(2):318\u201328.","journal-title":"Radiology"},{"issue":"9","key":"1722_CR20","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1097\/RLI.0000000000000673","volume":"55","author":"JL Wichmann","year":"2020","unstructured":"Wichmann JL, Willemink MJ, De Cecco CN. Artificial intelligence and machine learning in radiology: current state and considerations for routine clinical implementation. Invest Radiol. 2020;55(9):619\u201327.","journal-title":"Invest Radiol"},{"issue":"9 Pt B","key":"1722_CR21","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1016\/j.jacr.2019.05.047","volume":"16","author":"T Mart\u00edn Noguerol","year":"2019","unstructured":"Mart\u00edn Noguerol T, Paulano-Godino F, Mart\u00edn-Valdivia MT, et al. Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology. J Am Coll Radiol. 2019;16(9 Pt B):1239\u201347.","journal-title":"J Am Coll Radiol"},{"issue":"1","key":"1722_CR22","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s13045-022-01225-3","volume":"15","author":"R Wang","year":"2022","unstructured":"Wang R, Dai W, Gong J, et al. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J Hematol Oncol. 2022;15(1):11.","journal-title":"J Hematol Oncol"},{"issue":"3","key":"1722_CR23","doi-asserted-by":"publisher","first-page":"1983","DOI":"10.1007\/s00330-021-08268-z","volume":"32","author":"L Huang","year":"2022","unstructured":"Huang L, Lin W, Xie D, et al. Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study. Eur Radiol. 2022;32(3):1983\u201396.","journal-title":"Eur Radiol"},{"key":"1722_CR24","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","volume":"110","author":"E Christodoulou","year":"2019","unstructured":"Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12\u201322.","journal-title":"J Clin Epidemiol"},{"issue":"1","key":"1722_CR25","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1186\/s12911-023-02377-z","volume":"23","author":"D Zuo","year":"2023","unstructured":"Zuo D, Yang L, Jin Y, Qi H, Liu Y, Ren L. Machine learning-based models for the prediction of breast cancer recurrence risk. BMC Med Inf Decis Mak. 2023;23(1):276.","journal-title":"BMC Med Inf Decis Mak"},{"issue":"11","key":"1722_CR26","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/s11906-022-01212-6","volume":"24","author":"GFS Silva","year":"2022","unstructured":"Silva GFS, Fagundes TP, Teixeira BC, Chiavegatto Filho ADP. Machine learning for hypertension prediction: a systematic review. Curr Hypertens Rep. 2022;24(11):523\u201333.","journal-title":"Curr Hypertens Rep"},{"issue":"1","key":"1722_CR27","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1186\/s40001-023-01361-7","volume":"28","author":"Y Jin","year":"2023","unstructured":"Jin Y, Lan A, Dai Y, Jiang L, Liu S. Development and testing of a random forest-based machine learning model for predicting events among breast cancer patients with a poor response to neoadjuvant chemotherapy. Eur J Med Res. 2023;28(1):394.","journal-title":"Eur J Med Res"},{"issue":"1","key":"1722_CR28","doi-asserted-by":"publisher","first-page":"9858","DOI":"10.1038\/s41598-022-14143-8","volume":"12","author":"J Hao","year":"2022","unstructured":"Hao J, Luo S, Pan L. Rule extraction from biased random forest and fuzzy support vector machine for early diagnosis of diabetes. Sci Rep. 2022;12(1):9858.","journal-title":"Sci Rep"},{"key":"1722_CR29","doi-asserted-by":"crossref","unstructured":"Connolly ES Jr, Rabinstein AA, Carhuapoma JR, et al. Guidelines for the management of aneurysmal subarachnoid hemorrhage: a guideline for healthcare professionals from the American Heart Association\/american Stroke Association. Stroke 2012;43(6):1711-37.","DOI":"10.1161\/STR.0b013e3182587839"},{"key":"1722_CR30","doi-asserted-by":"crossref","unstructured":"Neidert MC, Maldaner N, Stienen MN, et al. The Barrow neurological Institute grading scale as a predictor for delayed cerebral ischemia and outcome after aneurysmal subarachnoid hemorrhage: data from a nationwide patient registry (Swiss SOS). Neurosurgery. 2018;83(6):1286\u201393.","DOI":"10.1093\/neuros\/nyx609"},{"key":"1722_CR31","doi-asserted-by":"crossref","unstructured":"Zijlstra IA, Gathier CS, Boers AM, et al. Association of automatically quantified total blood volume after aneurysmal subarachnoid hemorrhage with delayed cerebral ischemia. AJNR Am J Neuroradiol. 2016;37(9):1588\u201393.","DOI":"10.3174\/ajnr.A4771"},{"issue":"6","key":"1722_CR32","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.3174\/ajnr.A5626","volume":"39","author":"WE van der Steen","year":"2018","unstructured":"van der Steen WE, Zijlstra IA, Verbaan D, et al. Association of quantified Location-Specific blood volumes with delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage. AJNR Am J Neuroradiol. 2018;39(6):1059\u201364.","journal-title":"AJNR Am J Neuroradiol"},{"key":"1722_CR33","doi-asserted-by":"publisher","first-page":"e214","DOI":"10.1016\/j.wneu.2022.10.105","volume":"170","author":"JY Yuan","year":"2023","unstructured":"Yuan JY, Chen Y, Jayaraman K, et al. Automated quantification of compartmental blood volumes enables prediction of delayed cerebral ischemia and outcomes after aneurysmal subarachnoid hemorrhage. World Neurosurg. 2023;170:e214\u2013214222.","journal-title":"World Neurosurg"},{"issue":"2","key":"1722_CR34","doi-asserted-by":"publisher","first-page":"e232459","DOI":"10.1148\/radiol.232459","volume":"310","author":"M Huisman","year":"2024","unstructured":"Huisman M, Akinci D, Antonoli T. What a radiologist needs to know about radiomics, standardization, and reproducibility. Radiology. 2024;310(2):e232459.","journal-title":"Radiology"},{"issue":"5","key":"1722_CR35","doi-asserted-by":"publisher","first-page":"2905","DOI":"10.1007\/s00330-024-10586-x","volume":"34","author":"N Le","year":"2024","unstructured":"Le N. Hematoma expansion prediction: still navigating the intersection of deep learning and radiomics. Eur Radiol. 2024;34(5):2905\u20137.","journal-title":"Eur Radiol"},{"key":"1722_CR36","doi-asserted-by":"crossref","unstructured":"Mukherjee S, Patra A, Khasawneh H, et al. Radiomics-based machine-learning models can detect pancreatic cancer on prediagnostic computed tomography scans at a substantial lead time before clinical diagnosis. Gastroenterology. 2022;163(5):1435-46.e3.","DOI":"10.1053\/j.gastro.2022.06.066"},{"issue":"5","key":"1722_CR37","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1136\/neurintsurg-2018-014258","volume":"11","author":"LA Ramos","year":"2019","unstructured":"Ramos LA, van der Steen WE, Sales Barros R, et al. Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage. J Neurointerv Surg. 2019;11(5):497\u2013502.","journal-title":"J Neurointerv Surg"},{"issue":"4","key":"1722_CR38","doi-asserted-by":"publisher","first-page":"e553","DOI":"10.1212\/WNL.0000000000011211","volume":"96","author":"J Savarraj","year":"2021","unstructured":"Savarraj J, Hergenroeder GW, Zhu L, et al. Machine learning to predict delayed cerebral ischemia and outcomes in subarachnoid hemorrhage. Neurology. 2021;96(4):e553\u2013553562.","journal-title":"Neurology"},{"issue":"5","key":"1722_CR39","doi-asserted-by":"publisher","first-page":"E427","DOI":"10.1093\/neuros\/nyaa581","volume":"88","author":"G de Jong","year":"2021","unstructured":"de Jong G, Aquarius R, Sanaan B, et al. Prediction models in aneurysmal subarachnoid hemorrhage: forecasting clinical outcome with artificial intelligence. Neurosurgery. 2021;88(5):E427\u2013427434.","journal-title":"Neurosurgery"},{"key":"1722_CR40","unstructured":"Xiao ZK, Wang B, Liu J et al. Risk factors for the development of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage: A systematic review and meta-analysis. World Neurosurg 2024:S1878-8750(24)01653-01653X [pii]."},{"issue":"9","key":"1722_CR41","doi-asserted-by":"publisher","first-page":"105005","DOI":"10.1016\/j.jstrokecerebrovasdis.2020.105005","volume":"29","author":"H Liu","year":"2020","unstructured":"Liu H, Xu Q, Li A. Nomogram for predicting delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage in the Chinese population. J Stroke Cerebrovasc Dis. 2020;29(9):105005.","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"1722_CR42","doi-asserted-by":"publisher","first-page":"846066","DOI":"10.3389\/fneur.2022.846066","volume":"13","author":"J G\u00f6ttsche","year":"2022","unstructured":"G\u00f6ttsche J, Piffko A, Pantel TF, et al. Aneurysm location affects clinical course and mortality in patients with subarachnoid hemorrhage. Front Neurol. 2022;13:846066.","journal-title":"Front Neurol"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01722-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01722-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01722-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T16:36:38Z","timestamp":1748018198000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01722-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,23]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1722"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01722-0","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,23]]},"assertion":[{"value":"18 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 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":"This study was conducted in accordance with the Declaration of Helsinki, and was approved by the Institutional Review Board (IRB) of Beijing Tiantan Hospital, Capital Medical University (KY2022-058-02). The IRB waived the need for informed consent forms.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All the participants consented to the use of participant data for the study.","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":"182"}}