Clinical Neuroradiology最新文献

{"title":"Mismatch Vs No Mismatch in Large Core-A Matter of Definition.","authors":"Vivek Yedavalli, Hamza Adel Salim, Dhairya A Lakhani, Janet Mei, Aneri Balar, Basel Musmar, Nimer Adeeb, Meisam Hoseinyazdi, Licia Luna, Francis Deng, Nathan Z Hyson, Adam A Dmytriw, Adrien Guenego, Hanzhang Lu, Victor C Urrutia, Kambiz Nael, Elisabeth B Marsh, Raf Llinas, Argye E Hillis, Max Wintermark, Tobias D Faizy, Jeremy J Heit, Gregory W Albers","doi":"10.1007/s00062-024-01470-8","DOIUrl":"https://doi.org/10.1007/s00062-024-01470-8","url":null,"abstract":"<p><strong>Background: </strong>Endovascular thrombectomy (EVT) has shown promise in randomized controlled trials (RCTs) for large ischemic core stroke patients, yet variability in core definition and onset-to-imaging time creates heterogeneity in outcomes. This study aims to clarify the prevalence and implications of core-perfusion mismatch (MM) versus no mismatch (No MM) in such patients, utilizing established imaging criteria.</p><p><strong>Methods: </strong>A retrospective cohort study was conducted including patients from 7/29/2019 to 1/29/2023, with data extracted from a continuously maintained database. Patients were eligible if they met criteria including multimodal CT imaging performed within 24 h from last known well (LKW), AIS-LVO diagnosis, and ischemic core size defined by specific rCBF thresholds. Mismatch was assessed based on different operational definitions from the EXTEND and DEFUSE 3 trials.</p><p><strong>Results: </strong>Fifty-two patients were included, with various time windows from LKW. Using EXTEND criteria, a significant portion of early window patients exhibited MM; however, fewer patients met MM criteria in the late window. Defining MM using DEFUSE 3 criteria yielded similar patterns, but with overall lower MM prevalence in the late window. When employing rCBF <38% as a surrogate for ischemic core, a higher percentage of patients were classified as MM across both time windows compared to rCBF <30%.</p><p><strong>Conclusion: </strong>The prevalence of MM in large ischemic core patients varies significantly depending on the imaging criteria and time from LKW. Notably, MM was more prevalent in the early time window across all criteria used. Additional RCTs are needed to determine if this definition of MM identifies patients who will benefit most from EVT.</p>","PeriodicalId":49298,"journal":{"name":"Clinical Neuroradiology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Algorithms to Predict the Risk of Rupture of Intracranial Aneurysms: a Systematic Review.","authors":"Karan Daga, Siddharth Agarwal, Zaeem Moti, Matthew B K Lee, Munaib Din, David Wood, Marc Modat, Thomas C Booth","doi":"10.1007/s00062-024-01474-4","DOIUrl":"https://doi.org/10.1007/s00062-024-01474-4","url":null,"abstract":"<p><strong>Purpose: </strong>Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk, hence identifying rupture-prone aneurysms is of substantial clinical importance. This systematic review aims to evaluate the performance of machine learning algorithms for predicting intracranial aneurysm rupture risk.</p><p><strong>Methods: </strong>MEDLINE, Embase, Cochrane Library and Web of Science were searched until December 2023. Studies incorporating any machine learning algorithm to predict the risk of rupture of an intracranial aneurysm were included. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). PROSPERO registration: CRD42023452509.</p><p><strong>Results: </strong>Out of 10,307 records screened, 20 studies met the eligibility criteria for this review incorporating a total of 20,286 aneurysm cases. The machine learning models gave a 0.66-0.90 range for performance accuracy. The models were compared to current clinical standards in six studies and gave mixed results. Most studies posed high or unclear risks of bias and concerns for applicability, limiting the inferences that can be drawn from them. There was insufficient homogenous data for a meta-analysis.</p><p><strong>Conclusions: </strong>Machine learning can be applied to predict the risk of rupture for intracranial aneurysms. However, the evidence does not comprehensively demonstrate superiority to existing practice, limiting its role as a clinical adjunct. Further prospective multicentre studies of recent machine learning tools are needed to prove clinical validation before they are implemented in the clinic.</p>","PeriodicalId":49298,"journal":{"name":"Clinical Neuroradiology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}