{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T04:42:56Z","timestamp":1760330576358,"version":"build-2065373602"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>This study aimed to investigate the relationship between cervical vascular ultrasound\/transcranial Doppler ultrasound (TCD) parameters and anterior circulation cerebral atherosclerosis severity, and to develop a LASSO-Nomogram predictive model for clinical assessment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We retrospectively analyzed 350 patients with anterior circulation atherosclerosis, randomly divided into training (<jats:italic>n<\/jats:italic>\u202f=\u202f245) and validation (<jats:italic>n<\/jats:italic>\u202f=\u202f105) sets. Collected data included: (1) demographics and medical history; (2) lipid profiles; (3) ultrasound parameters [carotid intima-media thickness (IMT), plaque stability, internal carotid artery stenosis rate, middle cerebral artery peak systolic velocity (MCA-PSV), end-diastolic velocity (MCA-EDV), pulsatility index (PI), resistance index (RI)]. Patients were stratified by atherosclerosis severity (mild-moderate vs. severe). LASSO regression identified key predictors for nomogram prediction model construction, with model performance rigorously evaluated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Baseline characteristics were balanced between training set and validation set (<jats:italic>p<\/jats:italic>\u202f&amp;gt;\u202f0.05). Univariate analysis identified 10 significant factors (all <jats:italic>p<\/jats:italic>\u202f&amp;lt;\u202f0.05). LASSO regression selected 9 key predictors: age, high-density lipoprotein (HDL), low-density lipoprotein (LDL), carotid IMT, plaque stability, stenosis rate, and MCA hemodynamics (PSV, EDV, RI). Multivariate analysis showed HDL (OR\u202f=\u202f7.410) and stable plaques (OR\u202f=\u202f3.987) as protective factors of arteriosclerosis, while LDL (OR\u202f=\u202f0.621), carotid IMT (OR\u202f=\u202f0.038), MCA-PSV (OR\u202f=\u202f0.978), MCA-EDV (OR\u202f=\u202f0.960), and RI (OR\u202f=\u202f0.010) were risk factors of arteriosclerosis (all <jats:italic>p<\/jats:italic>\u202f&amp;lt;\u202f0.05). The model demonstrated excellent discrimination (training C-index\u202f=\u202f0.850; validation\u202f=\u202f0.796) with AUCs of 0.849 (95% CI: 0.792\u20130.907) and 0.801 (95% CI: 0.698\u20130.904), respectively. Decision curve analysis confirmed clinical utility across threshold probabilities of 10\u201380%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Cervical vascular ultrasound and TCD parameters effectively reflect anterior circulation atherosclerosis severity. Our LASSO-Nomogram model provides clinicians with a reliable, visualized tool for individualized risk assessment, potentially improving patient management.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fneur.2025.1639160","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T04:16:54Z","timestamp":1760329014000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["LASSO-Nomogram model for ultrasound-atherosclerosis correlation and diagnostic verification in anterior circulation"],"prefix":"10.3389","volume":"16","author":[{"given":"Yi","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pan","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinying","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Delin","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1148","DOI":"10.1136\/jnis-2022-019692","article-title":"Preclinical model of anterior circulation intracranial stenting","volume":"15","author":"Anagnostakou","year":"2023","journal-title":"J Neurointerv Surg"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"5685","DOI":"10.1007\/s00330-023-09660-7","article-title":"Editorial comment: cerebral hemodynamics in symptomatic anterior circulation intracranial stenosis measured by angiography-based quantitative flow ratio: association with CT perfusion","volume":"33","author":"Shinohara","year":"2023","journal-title":"Eur Radiol"},{"key":"ref3","article-title":"Subclavian artery thrombosis","volume-title":"StatPearls","author":"Kimyaghalam","year":"2025"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1295371","DOI":"10.3389\/fphys.2023.1295371","article-title":"Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population","volume":"14","author":"Weng","year":"2023","journal-title":"Front Physiol"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"3116","DOI":"10.1007\/s00330-020-07361-z","article-title":"Identification of high-risk carotid plaque with MRI-based radiomics and machine learning","volume":"31","author":"Zhang","year":"2021","journal-title":"Eur Radiol"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"741881","DOI":"10.3389\/fnagi.2021.741881","article-title":"Cerebral Hemodynamics and carotid atherosclerosis in patients with subcortical ischemic vascular dementia","volume":"13","author":"Liu","year":"2021","journal-title":"Front Aging Neurosci"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1496810","DOI":"10.3389\/fnins.2024.1496810","article-title":"A machine learning-based predictive model for predicting early neurological deterioration in lenticulostriate atheromatous disease-related infarction","volume":"18","author":"Jiang","year":"2024","journal-title":"Front Neurosci"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"2881","DOI":"10.3760\/cma.j.cn112137-20240501-01024","article-title":"Chinese guidelines for the diagnosis and treatment of vascular cognitive impairment (2024 edition)","volume":"104","year":"2024","journal-title":"Zhonghua Yi Xue Za Zhi"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"2700","DOI":"10.1111\/ene.15908","article-title":"Association of intracranial atherosclerosis with cerebral small vessel disease in a community-based population","volume":"30","author":"Wang","year":"2023","journal-title":"Eur J Neurol"},{"key":"ref10","first-page":"72","article-title":"Analysis of distribution of intracranial and extracranial atherosclerotic lesions and risk factors for recurrence in first-ever and recurrent ischemic stroke patients in Northeast China","volume":"30","author":"Sun","year":"2024","journal-title":"Altern Ther Health Med"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.ultrasmedbio.2020.10.015","article-title":"Carotid IMT and stiffness in the KiGGS 2 national survey: third-generation measurement, quality algorithms and determinants of completeness","volume":"47","author":"K\u00f6nigstein","year":"2021","journal-title":"Ultrasound Med Biol"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"110731","DOI":"10.1016\/j.ejrad.2023.110731","article-title":"A DWI-based radiomics-clinical machine learning model to preoperatively predict the futile recanalization after endovascular treatment of acute basilar artery occlusion patients","volume":"161","author":"Luo","year":"2023","journal-title":"Eur J Radiol"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"e18662","DOI":"10.7717\/peerj.18662","article-title":"A novel nomogram based on the patient's clinical data and CT signs to predict poor outcomes in AIS patients","volume":"13","author":"Yang","year":"2025","journal-title":"PeerJ"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1961","DOI":"10.1161\/STROKEAHA.120.032676","article-title":"Predictors of early infarct recurrence in patients with symptomatic intracranial atherosclerotic disease","volume":"52","author":"Prabhakaran","year":"2021","journal-title":"Stroke"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/s12944-025-02555-0","article-title":"Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and cerebral atherosclerotic stenosis: a retrospective study","volume":"24","author":"Han","year":"2025","journal-title":"Lipids Health Dis"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"8343","DOI":"10.1038\/s41598-025-93074-6","article-title":"Correlation of sdLDL-C and Apob with the degree of cerebral artery stenosis in posterior circulation stroke","volume":"15","author":"Sun","year":"2025","journal-title":"Sci Rep"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"095","DOI":"10.1055\/s-0042-1750040","article-title":"Risk factors of asymptomatic carotid artery disease in geriatric evaluated using carotid intima-media thickness (IMT) measurement, the first Indonesian population study","volume":"32","author":"Pratama","year":"2023","journal-title":"Int J Angiol"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.atherosclerosis.2020.11.024","article-title":"Carotid IMT and atherosclerosis: \u201ccalling things by name\u201d","volume":"317","author":"Sirtori","year":"2021","journal-title":"Atherosclerosis"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1590\/0100-3984.2020.0168","article-title":"Importance of computed tomography angiography in acute\/hyperacute ischemic stroke","volume":"54","author":"Dias","year":"2021","journal-title":"Radiol Bras"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"107616","DOI":"10.1016\/j.jstrokecerebrovasdis.2024.107616","article-title":"Differences in intracranial atherosclerosis plaque between posterior circulation and anterior circulation on high-resolution magnetic resonance imaging: a systematic review and meta-analysis","volume":"33","author":"Yan","year":"2024","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"107460","DOI":"10.1016\/j.jstrokecerebrovasdis.2023.107460","article-title":"Factors associated with the laterality of cardio-embolic stroke","volume":"32","author":"Abdelnaby","year":"2023","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"120226","DOI":"10.1016\/j.jns.2022.120226","article-title":"Plasma ApoB\/AI: an effective indicator for intracranial vascular positive remodeling","volume":"436","author":"Wang","year":"2022","journal-title":"J Neurol Sci"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"106897","DOI":"10.1016\/j.jstrokecerebrovasdis.2022.106897","article-title":"Borderzone infarction and recurrent stroke in intracranial atherosclerosis","volume":"32","author":"Kvernland","year":"2023","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"e026737","DOI":"10.1161\/JAHA.122.026737","article-title":"Atherosclerosis as a potential cause of deep embolic stroke of undetermined source: a 3T high-resolution magnetic resonance imaging study","volume":"11","author":"Luo","year":"2022","journal-title":"J Am Heart Assoc"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"8383","DOI":"10.21037\/qims-23-611","article-title":"Association between the fetal-type posterior cerebral artery and intracranial anterior and posterior circulating atherosclerotic plaques using multi-contrast magnetic resonance vessel wall imaging","volume":"13","author":"Liu","year":"2023","journal-title":"Quant Imaging Med Surg"}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2025.1639160\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T04:16:56Z","timestamp":1760329016000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2025.1639160\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,13]]},"references-count":25,"alternative-id":["10.3389\/fneur.2025.1639160"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2025.1639160","relation":{},"ISSN":["1664-2295"],"issn-type":[{"value":"1664-2295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,13]]},"article-number":"1639160"}}