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Improving the Robustness of Deep-Learning Models in Predicting Hematoma Expansion from Admission Head CT. 提高深度学习模型在预测入院头颅CT血肿扩张中的鲁棒性。
Pub Date : 2025-01-10 DOI: 10.3174/ajnr.A8650
Anh T Tran, Gaby Abou Karam, Dorin Zeevi, Adnan I Qureshi, Ajay Malhotra, Shahram Majidi, Santosh B Murthy, Soojin Park, Despina Kontos, Guido J Falcone, Kevin N Sheth, Seyedmehdi Payabvash

Background and purpose: Robustness against input data perturbations is essential for deploying deep-learning models in clinical practice. Adversarial attacks involve subtle, voxel-level manipulations of scans to increase deep-learning models' prediction errors. Testing deep-learning model performance on examples of adversarial images provides a measure of robustness, and including adversarial images in the training set can improve the model's robustness. In this study, we examined adversarial training and input modifications to improve the robustness of deep-learning models in predicting hematoma expansion (HE) from admission head CTs of patients with acute intracerebral hemorrhage (ICH).

Materials and methods: We used a multicenter cohort of n=890 patients for cross-validation/training, and a cohort of n=684 consecutive ICH patients from two stroke centers for independent validation. Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) adversarial attacks were applied for training and testing. We developed and tested four different models to predict ≥3mL, ≥6mL, ≥9mL, and ≥12mL HE in independent validation cohort applying Receiver Operating Characteristics (ROC) Area Under the Curve (AUC). We examined varying mixtures of adversarial and non-perturbed (clean) scans for training as well as including additional input from the hyperparameter-free Otsu multi-threshold segmentation for model.

Results: When deep-learning models trained solely on clean scans were tested with PGD and FGSM adversarial images, the average HE prediction AUC dropped from 0.8 to 0.67 and 0.71, respectively. Overall, the best performing strategy to improve model robustness was training with 5-to-3 mix of clean and PGD adversarial scans and addition of Otsu multi-threshold segmentation to model input, increasing the average AUC to 0.77 against both PGD and FGSM adversarial attacks. Adversarial training with FGSM improved robustness against similar type attack but offered limited cross-attack robustness against PGD-type images.

Conclusions: Adversarial training and inclusion of threshold-based segmentation as an additional input can improve deep-learning model robustness in prediction of HE from admission head CTs in acute ICH.

Abbreviations: ATACH-2= Antihypertensive Treatment of Acute Cerebral Hemorrhage; AUC= Area Under the Curve; Dice=Dice coefficient; CNN= Convolutional Neural Network; FGSM= Fast Gradient Sign Method; ICH= Intracerebral hemorrhage; HD= Hausdorff distance; HE= Hematoma expansion; PGD= Projected Gradient Descent; ROC= Receiver Operating Characteristics; VS= Volume similarity.

背景和目的:对输入数据扰动的鲁棒性对于在临床实践中部署深度学习模型至关重要。对抗性攻击涉及对扫描进行微妙的体素级操作,以增加深度学习模型的预测误差。在对抗图像的示例上测试深度学习模型的性能提供了鲁棒性的度量,并且在训练集中包含对抗图像可以提高模型的鲁棒性。在这项研究中,我们研究了对抗性训练和输入修改,以提高深度学习模型在预测急性脑出血(ICH)患者入院头部ct血肿扩张(HE)方面的稳健性。材料和方法:我们使用了一个包含n=890例患者的多中心队列进行交叉验证/训练,并使用了一个来自两个卒中中心的n=684例连续脑出血患者的队列进行独立验证。采用快速梯度符号法(FGSM)和投影梯度下降法(PGD)对抗性攻击进行训练和测试。我们开发并测试了四种不同的模型来预测≥3mL,≥6mL,≥9mL和≥12mL HE在独立验证队列中应用受试者工作特征(ROC)曲线下面积(AUC)。我们检查了不同的对抗性和非扰动(干净)扫描的混合训练,以及包括来自超参数无Otsu多阈值分割模型的额外输入。结果:当深度学习模型仅在干净扫描上训练时,使用PGD和FGSM对抗图像进行测试,平均HE预测AUC分别从0.8下降到0.67和0.71。总体而言,提高模型鲁棒性的最佳策略是使用5比3的干净和PGD对抗性扫描混合训练,并在模型输入中添加Otsu多阈值分割,将针对PGD和FGSM对抗性攻击的平均AUC提高到0.77。FGSM的对抗性训练提高了对相似类型攻击的鲁棒性,但对pgd类型图像的交叉攻击鲁棒性有限。结论:对抗性训练和包含基于阈值的分割作为额外输入可以提高深度学习模型在预测急性脑出血入院头部ct中HE的稳健性。ATACH-2=急性脑出血降压治疗;AUC=曲线下面积;骰子=骰子系数;卷积神经网络;快速梯度符号法;脑出血;HD=豪斯多夫距离;HE=血肿扩张;投影梯度下降;ROC=受试者工作特征;VS=体积相似度。
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引用次数: 0
Frontal Paraventricular Cysts: Refined Definitions and Outcomes. 额室旁囊肿:精确定义和结果。
Pub Date : 2025-01-10 DOI: 10.3174/ajnr.A8653
Matthew T Whitehead, Amirreza Manteghinejad, César A P F Alves, Onur Simsek, Nahla Khalek, Erin S Schwartz

Background and purpose: Frontal paraventricular cystic changes have a varied etiology that includes connatal cysts, subependymal pseudocysts, necrosis, and enlarged perivascular spaces. These may be difficult to distinguish by neuroimaging and have a variety of associated prognoses. We aim to refine the neuroimaging definition of frontal horn cysts and correlate it with adverse clinical conditions.

Materials and methods: In this cross-sectional study, the pre-and postnatal neuroimaging database at a quaternary referral children's hospital was searched for all reports containing "frontal horn cysts", "periventricular cysts", or "connatal cysts" after IRB exemption. Frontal paraventricular abnormalities were categorized as either cysts, necroses, enlarged perivascular spaces, caudothalamic groove subependymal pseudocysts, frontal horn asymmetries, intraventricular septations, or ependymal vessels based on location and appearance. Cyst number, size, location, morphology, and signal/density/echotexture were documented, as were additional brain abnormalities. Clinical outcomes were recorded when available. Fisher's exact and Chi-squared tests were used to evaluate categorical data associations, and Kruskall-Wallis tests were employed to compare the medians among groups.

Results: 205 brain imaging exams (148 MRI; 55 US; 2 CT) from 110 distinct subjects (5 fetal: median 29.3, mean 27.5, and range 22.4 to 32.8 gestational weeks; 105 postnatal: mean 2.5 years, median 15 days, range 0 days to 19 years) were included. Seventy-one exams (35%) were initially diagnosed as connatal cysts but, instead, represented necrosis (n=23), enlarged perivascular spaces (n=20), caudothalamic groove germinolytic cysts (n=11), septations/adhesions (n=10), ventricular asymmetries (n=6), and a blood vessel (n=1). These entities differed in size, shape, location, and orientation (p<0.001). Congenital heart disease (p<0.04) and gastrointestinal (p<0.04) disorders were more common in subjects with frontal cysts and necrosis than in subjects with enlarged perivascular spaces; however, the frontal cyst and necrosis groups showed no differences in outcome (p>0.05).

Conclusions: Frontal paraventricular cystic changes represent a common interpretive dilemma. Enlarged perivascular spaces should be distinguished from other frontal cystic changes, which portend a more guarded prognosis, whether necrotic or otherwise.

Abbreviations: CMV= cytomegalovirus; CSPC= caudothalamic groove subependymal pseudocysts; FHCL= frontal horn cystic lesions; GA= gestational age; PVS= perivascular spaces.

背景和目的:额叶室旁囊性改变有多种病因,包括先天性囊肿、室管膜下假性囊肿、坏死和血管周围间隙扩大。这些可能很难通过神经影像学来区分,并且有各种相关的预后。我们的目的是完善额角囊肿的神经影像学定义,并将其与不良临床条件相关联。材料和方法:在这项横断面研究中,在一家第四转诊儿童医院的产前和产后神经影像学数据库中搜索所有在IRB豁免后包含“额角囊肿”、“室周囊肿”或“新生儿囊肿”的报告。根据位置和外观,额室旁异常可分为囊肿、坏死、血管周围间隙增大、尾丘脑沟室管膜下假性囊肿、额角不对称、室内隔或室管膜血管。记录了囊肿的数量、大小、位置、形态、信号/密度/回声结构,以及其他脑部异常。如有可能,记录临床结果。采用Fisher精确检验和卡方检验评价分类数据相关性,采用Kruskall-Wallis检验比较组间中位数。结果:205例脑影像学检查(MRI 148例;55我们;2 CT)从110个不同的受试者(5个胎儿:中位29.3,平均27.5,范围22.4至32.8胎周;105例出生后:平均2.5年,中位15天,范围0天至19年)。71例(35%)检查最初被诊断为先天性囊肿,但结果显示为坏死(n=23)、血管周围间隙扩大(n=20)、尾丘脑沟溶性囊肿(n=11)、分隔/粘连(n=10)、心室不对称(n=6)和血管(n=1)。这些实体在大小、形状、位置和方向上存在差异(p0.05)。结论:额叶室旁囊性变是一种常见的解释困境。血管周围间隙扩大应与其他额叶囊性改变区分开来,后者预示着更谨慎的预后,无论是坏死还是其他。缩写:CMV=巨细胞病毒;尾丘脑沟室管膜下假性囊肿;额角囊性病变;GA=胎龄;PVS=血管周围空间。
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引用次数: 0
Nomogram for Predicting 90-day Outcomes in Patients with Acute Vertebrobasilar Artery Occlusion Undergoing Endovascular Treatment: A Multicenter Cohort Study. 预测急性椎基底动脉闭塞接受血管内治疗患者90天预后的Nomogram:一项多中心队列研究
Pub Date : 2025-01-10 DOI: 10.3174/ajnr.A8648
Bofeng Bai, Shanshan Huang, Pan Liu, Mengxiang Wang, Cong Ning, Yannan Wang, Hong Shi, Jian Cui, Yongbin Li

Background and purpose: In this study, we aimed to develop and validate a novel nomogram model for predicting 90-day non-favorable clinical outcomes in patients with acute vertebrobasilar artery occlusion after endovascular treatment by integrating clinical and MRI features.

Materials and methods: This multicenter retrospective study analyzed data from 181 patients with vertebrobasilar artery occlusion eligible for endovascular treatment from two Chinese stroke centers. We developed a predictive model for non-favorable clinical outcomes (modified Rankin Scale score >3) using the data of 125 patients from Stroke Center A (2019-2023). The model was constructed using univariate and multivariate logistic regression analyses of clinical and MRI characteristics, with continuous variables dichotomized based on receiver operating characteristic curve analysis. Internal validation employed smooth bootstrapping, while external validation utilized 56 cases from Stroke Center B (2019-2023), ensuring model reliability and generalizability across diverse clinical settings.

Results: Age, NIHSS baseline score, recanalization, novel posterior circulation scores, and MRA-based posterior circulation collateral scores were independent predictors of 90-day prognosis, which were used to create a nomogram model. Internal validation demonstrated excellent discriminative performance of the model (mean area under the curve, 0.92 [95% CI: 0.91-0.93]), while external validation further confirmed its robust generalizability (area under the curve, 0.88). The patients were effectively stratified into the low-risk and high-risk groups using the nomogram model.

Conclusions: The posterior circulation collateral score was an independent predictor of prognosis. Our novel nomogram model, based on clinical and MRI characteristics, effectively predicts 90-day non-favorable clinical outcomes in patients with vertebrobasilar artery occlusion following endovascular treatment.

Abbreviations: AUC = area under the curve; ETO = estimated time of onset; EVT = endovascular treatment; FCO = favorable clinical outcome; mTICI = modified TICI; Novel-PCS = novel posterior circulation score; pc-ASPECTS = posterior circulation Acute Stroke Prognosis Early CT score; PC-CS = posterior circulation collateral score; ROC = receiver operating characteristic; VBAO = vertebrobasilar artery occlusion.

背景和目的:在本研究中,我们旨在通过整合临床和MRI特征,建立并验证一种新的nomogram模型,用于预测急性椎基底动脉闭塞患者在血管内治疗后90天的不良临床结果。材料和方法:这项多中心回顾性研究分析了来自中国两个卒中中心的181例符合血管内治疗条件的椎基底动脉闭塞患者的数据。我们利用a脑卒中中心(2019-2023)125例患者的数据,建立了不良临床结果的预测模型(修正Rankin量表评分>.3)。该模型采用临床和MRI特征的单因素和多因素logistic回归分析构建,并根据受试者工作特征曲线分析对连续变量进行二分类。内部验证采用平滑引导,而外部验证使用了卒中中心B(2019-2023)的56例病例,确保了模型在不同临床环境中的可靠性和泛化性。结果:年龄、NIHSS基线评分、再通化、新型后循环评分和基于mri的后循环侧支评分是90天预后的独立预测因子,并用于建立nomogram模型。内部验证表明该模型具有良好的判别性能(曲线下平均面积为0.92 [95% CI: 0.91-0.93]),外部验证进一步证实了其稳健的泛化性(曲线下面积为0.88)。采用nomogram模型将患者有效地分为低危组和高危组。结论:后循环侧支评分是一个独立的预后预测指标。我们基于临床和MRI特征的新型nomogram模型,有效预测了椎基底动脉闭塞患者在接受血管内治疗后90天的不良临床结果。缩写:AUC =曲线下面积;ETO =预计开始时间;EVT =血管内治疗;FCO =良好的临床结果;mTICI =修改后的TICI;novel - pcs =新后循环评分;pc-ASPECTS =急性脑卒中后循环预后;PC-CS =后循环侧支评分;ROC =受试者工作特征;椎基底动脉闭塞。
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引用次数: 0
Deep learning MRI models for the differential diagnosis of tumefactive demyelination versus IDH-wildtype glioblastoma. 肿瘤性脱髓鞘与idh野生型胶质母细胞瘤的深度学习MRI模型鉴别诊断。
Pub Date : 2025-01-09 DOI: 10.3174/ajnr.A8645
Gian Marco Conte, Mana Moassefi, Paul A Decker, Matthew L Kosel, Christina B McCarthy, Jessica A Sagen, Yalda Nikanpour, Mahboubeh Fereidan-Esfahani, Michael W Ruff, Fiorella S Guido, Heather K Pump, Terry C Burns, Robert B Jenkins, Bradley J Erickson, Daniel H Lachance, W Oliver Tobin, Jeanette E Eckel-Passow
<p><strong>Background and purpose: </strong>Diagnosis of tumefactive demyelination can be challenging. The diagnosis of indeterminate brain lesions on MRI often requires tissue confirmation via brain biopsy. Noninvasive methods for accurate diagnosis of tumor and non-tumor etiologies allows for tailored therapy, optimal tumor control, and a reduced risk of iatrogenic morbidity and mortality. Tumefactive demyelination has imaging features that mimic <i>isocitrate dehydrogenase</i>-wildtype glioblastoma (<i>IDH</i>wt GBM). We hypothesized that deep learning applied to postcontrast T1-weighted (T1C) and T2-weighted (T2) MRI images can discriminate tumefactive demyelination from <i>IDH</i>wt GBM.</p><p><strong>Materials and methods: </strong>Patients with tumefactive demyelination (n=144) and <i>IDH</i>wt GBM (n=455) were identified by clinical registries. A 3D DenseNet121 architecture was used to develop models to differentiate tumefactive demyelination and <i>IDH</i>wt GBM using both T1C and T2 MRI images, as well as only T1C and only T2 images. A three-stage design was used: (i) model development and internal validation via five-fold cross validation using a sex-, age-, and MRI technology-matched set of tumefactive demyelination and <i>IDH</i>wt GBM, (ii) validation of model specificity on independent <i>IDH</i>wt GBM, and (iii) prospective validation on tumefactive demyelination and <i>IDH</i>wt GBM. Stratified AUCs were used to evaluate model performance stratified by sex, age at diagnosis, MRI scanner strength, and MRI acquisition.</p><p><strong>Results: </strong>The deep learning model developed using both T1C and T2 images had a prospective validation area under the receiver operator characteristic curve (AUC) of 88% (95% CI: 0.82 - 0.95). In the prospective validation stage, a model score threshold of 0.28 resulted in 91% sensitivity of correctly classifying tumefactive demyelination and 80% specificity (correctly classifying <i>IDH</i>wt GBM). Stratified AUCs demonstrated that model performance may be improved if thresholds were chosen stratified by age and MRI acquisition.</p><p><strong>Conclusions: </strong>MRI images can provide the basis for applying deep learning models to aid in the differential diagnosis of brain lesions. Further validation is needed to evaluate how well the model generalizes across institutions, patient populations, and technology, and to evaluate optimal thresholds for classification. Next steps also should incorporate additional tumor etiologies such as CNS lymphoma and brain metastases.</p><p><strong>Abbreviations: </strong>AUC = area under the receiver operator characteristic curve; CNS = central nervous system; CNSIDD = central nervous system inflammatory demyelinating disease; FeTS = federated tumor segmentation; GBM = glioblastoma; <i>IDH</i>wt = <i>isocitrate dehydrogenase</i> wildtype; IHC = immunohistochemistry; MOGAD = myelin oligodendrocyte glycoprotein antibody associated disorder; MS = multiple scleros
背景与目的:肿瘤脱髓鞘的诊断具有挑战性。MRI诊断不确定的脑病变通常需要通过脑活检进行组织确认。准确诊断肿瘤和非肿瘤病因的非侵入性方法允许定制治疗,最佳肿瘤控制,并降低医源性发病率和死亡率的风险。肿瘤性脱髓鞘具有类似异柠檬酸脱氢酶野生型胶质母细胞瘤(IDHwt GBM)的影像学特征。我们假设将深度学习应用于对比后的t1加权(T1C)和T2加权(T2) MRI图像可以区分肿瘤性脱髓鞘和IDHwt GBM。材料和方法:通过临床登记鉴定肿瘤脱髓鞘(n=144)和IDHwt GBM (n=455)。使用3D DenseNet121架构建立模型,使用T1C和T2 MRI图像,以及仅T1C和T2图像来区分肿瘤性脱髓鞘和IDHwt GBM。采用了三个阶段的设计:(i)模型开发和内部验证,通过使用性别、年龄和MRI技术匹配的一组肿瘤脱髓鞘和IDHwt GBM的五倍交叉验证,(ii)验证模型对独立IDHwt GBM的特异性,以及(iii)对肿瘤脱髓鞘和IDHwt GBM的前瞻性验证。分层auc用于评估按性别、诊断年龄、MRI扫描仪强度和MRI采集分层的模型性能。结果:使用T1C和T2图像开发的深度学习模型在接收者操作员特征曲线(AUC)下的预期验证区域为88% (95% CI: 0.82 - 0.95)。在前瞻性验证阶段,模型评分阈值为0.28,正确分类肿瘤脱髓鞘的敏感性为91%,特异性为80%(正确分类IDHwt GBM)。分层auc表明,如果根据年龄和MRI采集分层选择阈值,则模型性能可能会得到改善。结论:MRI图像可为应用深度学习模型辅助脑病变鉴别诊断提供依据。需要进一步的验证来评估该模型在机构、患者群体和技术方面的泛化程度,并评估分类的最佳阈值。下一步还应纳入其他肿瘤病因,如中枢神经系统淋巴瘤和脑转移。缩写:AUC =接收算子特征曲线下面积;中枢神经系统;中枢神经系统炎症性脱髓鞘病;fts =联合肿瘤分割;GBM =胶质母细胞瘤;异柠檬酸脱氢酶野生型;免疫组化;髓鞘少突胶质细胞糖蛋白抗体相关疾病;MS =多发性硬化症;NMOSD =神经脊髓炎视谱障碍;Wt = wildtype。
{"title":"Deep learning MRI models for the differential diagnosis of tumefactive demyelination versus <i>IDH</i>-wildtype glioblastoma.","authors":"Gian Marco Conte, Mana Moassefi, Paul A Decker, Matthew L Kosel, Christina B McCarthy, Jessica A Sagen, Yalda Nikanpour, Mahboubeh Fereidan-Esfahani, Michael W Ruff, Fiorella S Guido, Heather K Pump, Terry C Burns, Robert B Jenkins, Bradley J Erickson, Daniel H Lachance, W Oliver Tobin, Jeanette E Eckel-Passow","doi":"10.3174/ajnr.A8645","DOIUrl":"https://doi.org/10.3174/ajnr.A8645","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background and purpose: &lt;/strong&gt;Diagnosis of tumefactive demyelination can be challenging. The diagnosis of indeterminate brain lesions on MRI often requires tissue confirmation via brain biopsy. Noninvasive methods for accurate diagnosis of tumor and non-tumor etiologies allows for tailored therapy, optimal tumor control, and a reduced risk of iatrogenic morbidity and mortality. Tumefactive demyelination has imaging features that mimic &lt;i&gt;isocitrate dehydrogenase&lt;/i&gt;-wildtype glioblastoma (&lt;i&gt;IDH&lt;/i&gt;wt GBM). We hypothesized that deep learning applied to postcontrast T1-weighted (T1C) and T2-weighted (T2) MRI images can discriminate tumefactive demyelination from &lt;i&gt;IDH&lt;/i&gt;wt GBM.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Materials and methods: &lt;/strong&gt;Patients with tumefactive demyelination (n=144) and &lt;i&gt;IDH&lt;/i&gt;wt GBM (n=455) were identified by clinical registries. A 3D DenseNet121 architecture was used to develop models to differentiate tumefactive demyelination and &lt;i&gt;IDH&lt;/i&gt;wt GBM using both T1C and T2 MRI images, as well as only T1C and only T2 images. A three-stage design was used: (i) model development and internal validation via five-fold cross validation using a sex-, age-, and MRI technology-matched set of tumefactive demyelination and &lt;i&gt;IDH&lt;/i&gt;wt GBM, (ii) validation of model specificity on independent &lt;i&gt;IDH&lt;/i&gt;wt GBM, and (iii) prospective validation on tumefactive demyelination and &lt;i&gt;IDH&lt;/i&gt;wt GBM. Stratified AUCs were used to evaluate model performance stratified by sex, age at diagnosis, MRI scanner strength, and MRI acquisition.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The deep learning model developed using both T1C and T2 images had a prospective validation area under the receiver operator characteristic curve (AUC) of 88% (95% CI: 0.82 - 0.95). In the prospective validation stage, a model score threshold of 0.28 resulted in 91% sensitivity of correctly classifying tumefactive demyelination and 80% specificity (correctly classifying &lt;i&gt;IDH&lt;/i&gt;wt GBM). Stratified AUCs demonstrated that model performance may be improved if thresholds were chosen stratified by age and MRI acquisition.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;MRI images can provide the basis for applying deep learning models to aid in the differential diagnosis of brain lesions. Further validation is needed to evaluate how well the model generalizes across institutions, patient populations, and technology, and to evaluate optimal thresholds for classification. Next steps also should incorporate additional tumor etiologies such as CNS lymphoma and brain metastases.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Abbreviations: &lt;/strong&gt;AUC = area under the receiver operator characteristic curve; CNS = central nervous system; CNSIDD = central nervous system inflammatory demyelinating disease; FeTS = federated tumor segmentation; GBM = glioblastoma; &lt;i&gt;IDH&lt;/i&gt;wt = &lt;i&gt;isocitrate dehydrogenase&lt;/i&gt; wildtype; IHC = immunohistochemistry; MOGAD = myelin oligodendrocyte glycoprotein antibody associated disorder; MS = multiple scleros","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Prevalence and characteristics of microspurs in patients with spontaneous intracranial hypotension compared to the general population. 自发性颅内低血压患者微刺的患病率和特征与一般人群的比较。
Pub Date : 2025-01-09 DOI: 10.3174/ajnr.A8644
Danial Nasiri, Levin Häni, Johannes Goldberg, Thomas Petutschnigg, Tomas Dobrocky, Ralph T Schär, Christoph Schankin, Andreas Raabe, Jürgen Beck, Eike Immo Piechowiak, Christopher Marvin Jesse

Background and purpose: In patients diagnosed with spontaneous intracranial hypotension (SIH), microspurs are considered the culprit lesion in most ventral dural leaks (type I). The imaging characteristics of discogenic spurs, and their prevalence in the general population has not been reported in the literature.

Materials and methods: This observational case-control study was conducted comparing the prevalence and characteristics of discogenic microspurs between SIH patients with a type I leak treated at a tertiary hospital between 2013 and 2023 and an age-and sex matched cohort of trauma patients.

Results: Each group consisted of 85 patients (mean age 51.6 years ± 11.9 years), 74% (58/85 patients) were female. The prevalence of discogenic microspurs in the control group and SIH group was 31.8% and 90.6%, respectively.The mean length of the culprit microspur responsible for a dural leak was larger compared to the mean length of all co-incidental microspurs from both the SIH and the control group not causing a dural leak (2.6mm versus 1.6mm, p<0.001).Our multivariate logistic regression revealed that an increasing length of a microspur (OR 1.942, CI 1.35-2.80, p<0.001) and a narrower diameter of the spinal canal (OR 0.85, CI 0.76-0.96, p=0.008) were predictive for a dural tear.

Conclusions: A discogenic microspur is a common incidental finding and may be found in almost one third of the general population. The length of the culprit microspur and the diameter of the spinal canal are distinct morphological characteristics for type I associated CSF leaks.

Abbreviations: CI = Confidence interval; CSF = Cerebrospinal fluid; CT = Computed tomography; ED = Emergency department; MRI = Magnetic resonance imaging; OR = Odds ratio; SD = Standard deviation; SIH = Spontaneous intracranial hypotension.

背景与目的:在诊断为自发性颅内低血压(SIH)的患者中,微刺被认为是大多数腹侧硬脑膜泄漏(I型)的罪魁祸首病变。椎间盘源性刺的影像学特征及其在一般人群中的发病率尚未见文献报道。材料和方法:本观察性病例对照研究比较了2013 - 2023年在某三级医院治疗的I型SIH泄漏患者与年龄和性别匹配的创伤患者的椎间盘源性微刺的患病率和特征。结果:每组85例患者(平均年龄51.6岁±11.9岁),女性占74%(58/85)。对照组和SIH组椎间盘源性微刺的发生率分别为31.8%和90.6%。导致硬脑膜泄漏的罪魁祸首微刺的平均长度比SIH组和对照组中所有同时发生的微刺的平均长度要大(2.6mm比1.6mm)。结论:椎间盘源性微刺是一种常见的偶然发现,可能在近三分之一的普通人群中发现。罪魁祸首微骨刺的长度和椎管的直径是I型相关脑脊液泄漏的明显形态学特征。缩写:CI =置信区间;脑脊液;CT =计算机断层扫描;急诊科;磁共振成像;OR =优势比;SD =标准差;自发性颅内低血压。
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引用次数: 0
Image Quality Evaluation for Brain Soft Tissue in Neuroendovascular Treatment by Dose-Reduction Mode of Dual-Axis "Butterfly" Scan. 双轴 "蝴蝶 "扫描的剂量降低模式对神经血管内治疗中脑部软组织的图像质量评估
Pub Date : 2025-01-09 DOI: 10.3174/ajnr.A8472
Hisayuki Hosoo, Yoshiro Ito, Koji Hirata, Mikito Hayakawa, Aiki Marushima, Tomohiko Masumoto, Hiroshi Yamagami, Yuji Matsumaru

Background and purpose: Flat panel conebeam CT (CBCT) is essential for detecting hemorrhagic complications during neuroendovascular treatments. Despite its superior image quality and trajectory over conventional CBCT (circular scan), the dual-axis butterfly scan has a slightly higher radiation dose relative to conventional CBCT. This study evaluates the image quality in dose-reduction mode to uncover the appropriate radiation dose for the butterfly scan.

Materials and methods: We prospectively included patients who were scheduled for neuroendovascular treatment and underwent conventional CBCT and the dose-reduction mode of the butterfly scan. Two reduced radiation dose modes were used for the butterfly scan: medium-dose butterfly scan (70% of the original dose, 45 mGy) or low-dose butterfly scan (50% of the original dose, 30 mGy). The enrolled patients were assigned alternately to undergo either the medium- or low-dose butterfly scan. We evaluated and compared artifacts, contrast, and discrimination of the corticomedullary junction between conventional CBCT and one of the dose-reduction modes of the butterfly scan, with a 5-point scale scoring system.

Results: Twenty patients were enrolled in each of the medium- and low-dose groups, totaling 40 patients. Compared with conventional CBCT, the medium-dose butterfly group exhibited reduced artifacts, enhanced contrast, and corticomedullary junction discrimination (except in the occipital lobe). While the low-dose butterfly group exhibited markedly reduced artifacts and improved contrast (except in the occipital lobe), a significant improvement in corticomedullary junction discrimination was not observed.

Conclusions: Even with dose reduction, the specialized trajectory of the butterfly scan enables artifact reduction, contrast improvement, and enhanced corticomedullary junction discrimination. However, the impact of the reduced dose was more noticeable, particularly in the occipital region where susceptibility to bone interference resulted in decreased contrast and compromised corticomedullary junction discrimination.

背景和目的:平板锥束 CT(CBCT)对于检测神经内血管治疗过程中的出血并发症至关重要。尽管双轴蝶形扫描的图像质量和轨迹优于传统 CBCT(环形扫描),但其辐射剂量略高于传统 CBCT。本研究评估了剂量降低模式下的图像质量,以确定蝶形扫描的适当辐射剂量:我们前瞻性地纳入了计划接受神经血管治疗的患者,并对其进行了常规 CBCT 和剂量减低模式的蝶形扫描。蝶形扫描采用了两种辐射剂量降低模式:中剂量蝶形扫描(原始剂量的 70%,45 mGy)或低剂量蝶形扫描(原始剂量的 50%,30 mGy)。入组患者交替接受中剂量或低剂量蝶形扫描。我们采用 5 点评分法评估并比较了传统 CBCT 和一种剂量降低模式的蝶形扫描之间的伪影、对比度和皮质髓质交界处的辨别力:中剂量组和低剂量组各有 20 名患者,共 40 名患者。与传统的 CBCT 相比,中剂量蝶形扫描组减少了伪影,增强了对比度,并能分辨皮质髓质交界处(枕叶除外)。低剂量蝶形组的伪影明显减少,对比度有所提高(枕叶除外),但皮质髓质交界处的辨别能力没有明显改善:结论:即使减少了剂量,蝶形扫描的特殊轨迹也能减少伪影、提高对比度并增强皮质髓质交界处的辨别力。然而,减少剂量的影响更为明显,尤其是在枕骨区,该区域易受骨干扰,导致对比度下降,影响了皮质髓质交界处的辨别能力:缩写:AVM=动静脉畸形,CBCT=锥形束 CT,CAS=颈动脉支架植入术,CTDI=CT 剂量指数,DAVF=硬脑膜动静脉瘘,FD=血流分流器,PTAS=经皮腔内血管成形术和支架植入术。
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引用次数: 0
Reversible Perfusion Changes during Acute Attacks in Glucose Transporter Type 1 Deficiency Syndrome: A Pediatric Case Series. 1型葡萄糖转运蛋白缺乏综合征急性发作时可逆性灌注改变:儿科病例系列。
Pub Date : 2025-01-09 DOI: 10.3174/ajnr.A8506
Francesco Pacchiano, Chiara Doneda, Filippo Arrigoni, Mario Tortora, Maria Teresa Contaldo, Germana Lomonaco, Roberto Previtali, Sara Olivotto, Pierangelo Veggiotti, Cecilia Parazzini, Andrea Righini

Glucose transporter type 1 deficiency syndrome (GLUT1-DS) is an uncommon condition represented by an infantile-onset disorder, frequently arising from heterozygous mutations in the SLC2A1 gene. Individuals with GLUT1-DS may present with early-onset seizures (typically manifesting before 4 years of age), developmental delay, and complex movement disorders. In fewer cases, stroke-like events or hemiplegic migraine-like symptoms are also reported, defined by unilateral paresis affecting 1 side of the body and/or one-half of the face, occasionally accompanied by speech impairment. Currently, the pathomechanism underlying these acute transient clinical manifestations is poorly understood. MR imaging studies performed in the absence of acute manifestations frequently reveal nonspecific imaging signs associated with this syndrome. We present findings obtained using the arterial spin-labeling technique for perfusion imaging and MRA during the acute onset of stroke-like episodes in a series of 4 pediatric patients with GLUT1-DS. We observed reversible hypoperfusion in the left hemisphere and associated reversible attenuation of distal MCA branches on MRA. A notable association between unilateral cerebral hypoperfusion and transient crossed cerebellar diaschisis was evident on perfusion maps as well.

葡萄糖转运蛋白1型缺乏综合征(GLUT1-DS)是一种罕见的疾病,以婴儿发病的疾病为代表,通常由SLC2A1基因的杂合突变引起。患有GLUT1-DS的个体可能表现为早发性癫痫发作(通常在4岁之前出现)、发育迟缓和复杂的运动障碍。在少数病例中,也有中风样事件或偏瘫样偏头痛症状的报道,其定义为单侧麻痹,影响身体的一侧和/或半张脸,偶尔伴有语言障碍。目前,这些急性短暂临床表现的病理机制尚不清楚。在没有急性表现的情况下进行的磁共振成像研究经常显示与该综合征相关的非特异性影像学征象。我们报告了在4例GLUT1-DS患儿急性发作的卒中样发作期间,使用动脉自旋标记技术进行灌注成像和MRA的发现。我们在MRA上观察到左半球可逆灌注不足和相关的MCA远端分支可逆衰减。在灌注图上,单侧脑灌注不足和短暂性交叉小脑缺血之间也有明显的联系。
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引用次数: 0
The "Outline Sign": Thin Hyperenhancing Perimeter as an MR Imaging Feature of Meningioma. A Useful Tool in the Temporal Bone Region for Differentiating Meningiomas from Schwannomas and Paragangliomas. “轮廓征象”:脑膜瘤的MR成像特征为薄的超增强周长。颞骨区脑膜瘤与神经鞘瘤及副神经节瘤鉴别的有用工具。
Pub Date : 2025-01-09 DOI: 10.3174/ajnr.A8452
Anil K Vasireddi, Katherine L Reinshagen, Donghoon Shin, Laura V Romo, Amy F Juliano

Background and purpose: This study investigates the practicality and utility of the "outline sign," which refers to the thin curvilinear hyperenhancing line that may be seen along the margin of a meningioma on a spin-echo postcontrast T1-weighted image. For cases in which the differential diagnosis may include other tumors, visualization of the outline sign may help to increase the diagnostic confidence for a meningioma. Therefore, in the temporal bone region such as the cerebellopontine angle or jugular foramen, where differential considerations may include a schwannoma or paraganglioma, we additionally investigated whether the outline sign may be observed in these nonmeningioma lesions.

Materials and methods: A total of 39 clinical MRIs of meningiomas, schwannomas, and paragangliomas with confirmed histopathologic data were studied retrospectively. Two experienced head and neck radiologists independently assessed for the presence or absence of an outline sign and subsequently formed a consensus opinion while blinded to patient information and histopathologic data. Interreader reliability was assessed by Cohen κ statistics. Simple bivariate comparisons were performed on the consensus opinions to assess for statistical differences in presence of the sign in meningiomas versus schwannomas and paragangliomas. Sensitivity, specificity, and accuracy of the sign with respect to identifying an underlying meningioma were calculated.

Results: Both readers displayed identical opinions in assessment of the outline sign in 34 of the 39 cases (87%), including 13 of the 14 meningiomas (93%), with substantial agreement (Cohen κ of 0.74). The outline sign was present in 12 of 14 meningiomas (86%), which was significantly greater in frequency compared with schwannomas (3 of 22, 14%) and paragangliomas (1 of 3, 33%). The outline sign demonstrated high sensitivity (86%), specificity (84%), and accuracy (85%) in identifying an underlying meningioma.

Conclusions: The outline sign can serve as a useful tool for diagnosing meningiomas. It may help distinguish meningiomas from other enhancing tumors, for example schwannomas and paragangliomas in the temporal bone region.

背景和目的:本研究探讨了“轮廓征象”的实用性和实用性,“轮廓征象”是指在旋转回声对比后t1加权图像上沿脑膜瘤边缘可见的细曲线超增强线。对于鉴别诊断可能包括其他肿瘤的病例,轮廓征象的可视化可能有助于增加脑膜瘤的诊断信心。因此,在颞骨区域,如桥小脑角或颈静脉孔,鉴别考虑可能包括神经鞘瘤或副神经节瘤,我们进一步研究了在这些非脑膜瘤病变中是否可以观察到轮廓征象。材料与方法:回顾性分析39例经病理证实的脑膜瘤、神经鞘瘤、副神经节瘤的临床mri。两名经验丰富的头颈部放射科医生在不了解患者信息和组织病理学数据的情况下,独立评估是否存在轮廓征,并随后形成共识意见。采用Cohen κ统计量评估解读器信度。简单的双变量比较在共识意见上进行,以评估脑膜瘤与神经鞘瘤和副神经节瘤的体征存在的统计学差异。计算该征象在鉴别潜在脑膜瘤方面的敏感性、特异性和准确性。结果:两位读者对39例中34例(87%),包括14例脑膜瘤中的13例(93%)的轮廓征象的评价意见一致(Cohen κ为0.74)。14例脑膜瘤中有12例(86%)出现大纲征象,与神经鞘瘤(22例中有3例,14%)和副神经节瘤(3例中有1例,33%)相比,其频率明显更高。轮廓征象在识别潜在脑膜瘤方面表现出高灵敏度(86%)、特异性(84%)和准确性(85%)。结论:轮廓征象可作为诊断脑膜瘤的有效工具。它可能有助于区分脑膜瘤与其他增强肿瘤,如颞骨区神经鞘瘤和副神经节瘤。
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引用次数: 0
Circle of Willis Variants and Their Association with Outcome after Successful Revascularization of Anterior Large Vessel Occlusion. 威利斯圈变异及其与前大血管闭塞成功血运重建后预后的关系。
Pub Date : 2025-01-09 DOI: 10.3174/ajnr.A8643
Aakanksha Sriwastwa, Michael K Oswald, Achala S Vagal, Stacie L Demel, Bin Zhang, Sriharsha Voleti, Arafat Ali, Daniel Morgan, Trevor Thompson, Johnathan Vidovich, Yasmin N Aziz, Lily Li-Li Wang

Background and purpose: Leptomeningeal collaterals have been associated with better outcomes in large-vessel stroke, but little is known about how the Circle of Willis (CoW) collaterals affect stroke outcomes. We aimed to determine the relationship between three anatomically distinct CoW subtypes and 90-day outcomes in acute ischemic stroke patients after successful revascularization via endovascular thrombectomy (EVT).

Materials and methods: We performed a retrospective analysis of patients treated with successful EVT for large-vessel occlusion at a comprehensive stroke center between May 2016 and November 2023. The CoW anatomy was trichotomized using baseline computed tomography angiography as follows: (I) complete CoW (C-CoW), (II) non-isolating incomplete CoW (NI-CoW), and (III) isolating incomplete CoW (I-CoW). Chi-squared and logistic regression analyses were utilized to determine the association of the CoW subtype with two co-primary outcomes: the 90-day modified Rankin Scale and 90-day mortality.

Results: A total of 465 patients were included in the analysis. Multivariable logistic regression analysis demonstrated a significant association between I-CoW and 90-day mRS compared to NI-CoW [OR (95% CI), 1.83 (1.08-3.09); p=0.02]. Additionally, I-CoW anatomy was associated with a higher 90-day mortality than C-CoW [OR (95%CI), 2.58 (1.01-6.60); p=0.04] and NI-CoW [OR (95% CI), 1.89 (1.13-3.18); p=0.01].

Conclusions: CoW variants are associated with functional and mortality outcomes in patients treated with endovascular thrombectomy for anterior circulation large vessel occlusion. Further research is needed to determine how CoW vessel anatomy may impact clinical assessment, triage, and treatment in acute ischemic stroke.

Abbreviations: CoW = Circle of Willis; EVT = endovascular thrombectomy; C-CoW = complete Circle of Willis; NI-CoW = non-isolating incomplete Circle of Willis; I-CoW = isolating Circle of Willis; AIS = acute ischemic stroke; LVO = large vessel occlusion; ACom = anterior communicating artery; PCom = posterior communicating artery; Tan CS = Tan collateral scores; ACA = anterior cerebral artery; PCA = posterior cerebral artery.

背景和目的:在大血管卒中中,轻脑膜侧支与更好的预后相关,但对于威利斯圈(CoW)侧支如何影响卒中预后知之甚少。我们的目的是确定三种解剖学上不同的CoW亚型与急性缺血性卒中患者通过血管内取栓(EVT)成功血运重建后90天预后的关系。材料和方法:我们回顾性分析了2016年5月至2023年11月在综合卒中中心成功接受EVT治疗的大血管闭塞患者。使用基线计算机断层血管造影对CoW解剖进行三分术:(I)完全CoW (C-CoW), (II)非分离性不完全CoW (NI-CoW), (III)分离性不完全CoW (I-CoW)。使用卡方和逻辑回归分析来确定CoW亚型与两个共同主要结局的关联:90天修正Rankin量表和90天死亡率。结果:共纳入465例患者。多变量logistic回归分析显示,与NI-CoW相比,I-CoW与90天mRS之间存在显著相关性[OR (95% CI), 1.83 (1.08-3.09);p = 0.02)。此外,与C-CoW相比,I-CoW解剖结构与更高的90天死亡率相关[OR (95%CI), 2.58 (1.01-6.60);p = 0.04)和NI-CoW(或(95% CI), 1.89 (1.13 - -3.18);p = 0.01)。结论:CoW变异与前循环大血管闭塞接受血管内取栓治疗的患者的功能和死亡率相关。需要进一步的研究来确定CoW血管解剖如何影响急性缺血性卒中的临床评估、分诊和治疗。缩写:CoW =威利斯圈;EVT =血管内血栓切除术;C-CoW =完全威利斯圈;NI-CoW =非隔离不完全Willis环;I-CoW = Willis隔离圈;AIS =急性缺血性卒中;LVO =大血管闭塞;前交通动脉;PCom =后交通动脉;Tan CS = Tan collateral scores;ACA =大脑前动脉;PCA =大脑后动脉。
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引用次数: 0
Introduction to Digital Subtraction Myelography for CSF-Venous Fistula Detection. 数字减影脊髓造影在csf -静脉瘘检测中的应用。
Pub Date : 2025-01-08 DOI: 10.3174/ajnr.A8587
Ian T Mark, Ajay A Madhavan, John C Benson, Jared Verdoorn, Waleed Brinjikji

The purpose of this video is to introduce digital subtraction myelography for CSF-venous fistula (CVF) detectection.1-3 CVF is the most recently identified and likely the most prevalent type of spinal CSF leak that leads to spontaneous intracranial hypotension4,5 CVFs are occult on conventional MRI and CT, necessitating the use of myelography for the diagnosis. This video highlights one such technique, which is important because an increasing number of centers are starting to diagnose CVF.

本视频的目的是介绍数字减影脊髓造影检测csf -静脉瘘(CVF)。1-3 CVF是最近发现的,可能是最常见的导致自发性颅内低血压的脊髓脊液泄漏类型4,5 CVF在常规MRI和CT上是隐匿的,需要使用脊髓造影进行诊断。本视频重点介绍了一种这样的技术,它很重要,因为越来越多的中心开始诊断CVF。
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引用次数: 0
期刊
AJNR. American journal of neuroradiology
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