Till Gumbel, Cindy Richter, Christian Martin, Ulf Nestler
{"title":"1000张数字减影血管造影对14条脑基底动脉血管直径的评估。","authors":"Till Gumbel, Cindy Richter, Christian Martin, Ulf Nestler","doi":"10.1038/s41597-025-05908-7","DOIUrl":null,"url":null,"abstract":"<p><p>Angiographic normative values for the size of intracranial vessels are difficult to obtain, since they vary with gender, height and weight. Cerebral angiography only is indicated in severe cerebrovascular diseases, which also can affect cerebral vessel diameters, impeding the definition of physiological values. To approximate \"normal\" values, over 1000 contemporary cerebral angiographies from a single neurovascular centre were analyzed. Diameters of 14 basal cerebral arteries, age at examination, gender and underlying disease were noted. The dataset (SPSS 29, IBM) comprises 1010 digital subtraction angiographies. For example, a significant difference (p < 0.001) in the size of the left carotid artery between male (3.23 mm, n = 361, sd = 0.49) and female (3.09 mm, n = 645, sd = 0.52) patients is found. The data can be used to compute intraindividual indices in given diseases, e.g. whether an enlarged diameter of the right media, calculated as ratio to the left media or to the ipsilateral carotid artery, is associated to cerebral aneurysms. The dataset allows for training of machine learning programs, e.g. to predict ischemic stroke or cerebral hemorrhage.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1543"},"PeriodicalIF":6.9000,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417541/pdf/","citationCount":"0","resultStr":"{\"title\":\"Vessel diameters of 14 basal cerebral arteries assessed in 1000 digital subtraction angiographies.\",\"authors\":\"Till Gumbel, Cindy Richter, Christian Martin, Ulf Nestler\",\"doi\":\"10.1038/s41597-025-05908-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Angiographic normative values for the size of intracranial vessels are difficult to obtain, since they vary with gender, height and weight. Cerebral angiography only is indicated in severe cerebrovascular diseases, which also can affect cerebral vessel diameters, impeding the definition of physiological values. To approximate \\\"normal\\\" values, over 1000 contemporary cerebral angiographies from a single neurovascular centre were analyzed. Diameters of 14 basal cerebral arteries, age at examination, gender and underlying disease were noted. The dataset (SPSS 29, IBM) comprises 1010 digital subtraction angiographies. For example, a significant difference (p < 0.001) in the size of the left carotid artery between male (3.23 mm, n = 361, sd = 0.49) and female (3.09 mm, n = 645, sd = 0.52) patients is found. The data can be used to compute intraindividual indices in given diseases, e.g. whether an enlarged diameter of the right media, calculated as ratio to the left media or to the ipsilateral carotid artery, is associated to cerebral aneurysms. The dataset allows for training of machine learning programs, e.g. to predict ischemic stroke or cerebral hemorrhage.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"1543\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417541/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-025-05908-7\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-05908-7","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Vessel diameters of 14 basal cerebral arteries assessed in 1000 digital subtraction angiographies.
Angiographic normative values for the size of intracranial vessels are difficult to obtain, since they vary with gender, height and weight. Cerebral angiography only is indicated in severe cerebrovascular diseases, which also can affect cerebral vessel diameters, impeding the definition of physiological values. To approximate "normal" values, over 1000 contemporary cerebral angiographies from a single neurovascular centre were analyzed. Diameters of 14 basal cerebral arteries, age at examination, gender and underlying disease were noted. The dataset (SPSS 29, IBM) comprises 1010 digital subtraction angiographies. For example, a significant difference (p < 0.001) in the size of the left carotid artery between male (3.23 mm, n = 361, sd = 0.49) and female (3.09 mm, n = 645, sd = 0.52) patients is found. The data can be used to compute intraindividual indices in given diseases, e.g. whether an enlarged diameter of the right media, calculated as ratio to the left media or to the ipsilateral carotid artery, is associated to cerebral aneurysms. The dataset allows for training of machine learning programs, e.g. to predict ischemic stroke or cerebral hemorrhage.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.