AJNR. American journal of neuroradiology最新文献

Background and purpose: Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion classify adult-type diffuse gliomas into 3 tumor subtypes with distinct prognoses. We aimed to evaluate the performance of edited MR spectroscopy for glioma subtyping in a clinical setting, via the quantification of D-2-hydroxyglutarate (2HG) and cystathionine. The delay between this noninvasive classification and the integrated histomolecular analysis was also quantified.

Materials and methods: Subjects with presumed low-grade gliomas eligible for surgery (cohort 1) and subjects with IDH-mutant gliomas previously treated and with progressive disease (cohort 2) were prospectively examined with a single-voxel Mescher-Garwood point-resolved spectroscopy sequence at 3T. Spectra were quantified using LCModel. The Cramér-Rao lower bounds threshold was set to 20%. Integrated histomolecular analysis according to the 2021 WHO classification was considered as ground truth.

Results: Thirty-four consecutive subjects were enrolled. Due to poor spectra quality and lack of histologic specimens, data from 26 subjects were analyzed. Twenty-one belonged to cohort 1 (11 women; median age, 42 years); and 5, to cohort 2 (3 women; median age, 48 years). Edited MR spectroscopy showed 100% specificity for detection of IDH-mutation and 91% specificity for the prediction of 1p/19q-codeletion status. Sensitivities for the prediction of IDH and 1p/19q codeletion were 69% and 33%, respectively. The median Cramér-Rao lower bounds values were 16% (13%-28%) for IDH-mutant and 572% (554%-999%) for IDH wild type tumors. The time between MR spectroscopy and surgery was longer for low-grade than for high-grade gliomas (P = .03), yet the time between MR spectroscopy and WHO diagnosis did not differ between grades (P = .07), possibly reflecting molecular analyses-induced delays in high-grade gliomas.

Conclusions: Our results, acquired in a clinic setting, confirmed that edited MR spectroscopy is highly specific for both IDH-mutation and 1p/19q-codeletion predictions and can provide a faster prognosis stratification. In the upcoming IDH-inhibitor treatment era, incorporation of edited MR spectroscopy into clinical workflow is desirable.

Background and purpose: Assessing the treatment success of intracranial aneurysms treated with Woven EndoBridge (WEB) devices using MRI is important in follow-up imaging. Depicting both the device configuration as well as reperfusion is challenging due to susceptibility artifacts. We evaluated the usefulness of the contrast-enhanced 3D ultrashort TE (UTE) sequence in this setting.

Materials and methods: In this prospective study, 12 patients (9 women) with 15 treated aneurysms were included. These 12 patients underwent 18 MRI examinations. Follow-up UTE-MRI controls were performed on the same 3T scanner. We compared the visualization of device configuration, artifact-related virtual stenosis of the parent vessel, and the WEB occlusion scale in 3D isotropic UTE-MRI postcontrast with standard TOF-MRA with contrast-enhancement (CE) and without IV contrast as well as DSA. Two interventional neuroradiologists rated the images separately and in consensus.

Results: Visualization of the WEB device position and configuration was rated superior or highly superior using the UTE sequence in 17/18 MRIs compared with TOF-MRA. Artifact-related virtual stenosis of the parent vessel was significantly lower in UTE-MRI compared with TOF and CE-TOF. Reperfusion was visible in 8/18 controls on DSA. TOF was able to grade reperfusion correctly in 16 cases; CE-TOF, in 16 cases; and UTE, in 17 cases.

Conclusions: Contrast-enhanced UTE is a novel MRI sequence that shows benefit compared with the standard sequences in noninvasive and radiation-free follow-up imaging of intracranial aneurysms treated using the WEB device.

Light-chain deposition disease (LCDD) is a rare CNS disorder characterized by the extracellular accumulation of monoclonal immunoglobulin light chains in various organs. LCDD typically arises secondary to an underlying plasma cell dyscrasia, such as monoclonal gammopathy of undetermined significance or multiple myeloma. However, rare cases can occur in the absence of a demonstrable plasma cell disorder. The kidneys, liver, lungs, and heart are the most affected organs. Intracerebral LCDD, particularly without an underlying plasma cell neoplasm, represents an exceedingly uncommon entity with limited documented cases in the literature. This review article explores the pathogenesis, histopathologic features, and characteristic neuroimaging findings of intracerebral LCDD. We emphasize the diverse imaging presentations of this disease, which can closely resemble other neurologic pathologies. Recognizing these potential mimics is crucial for avoiding misdiagnosis, especially in the absence of a known underlying plasma cell disorder. This article aims to provide a comprehensive overview from a neuroradiologic perspective, facilitating the recognition and differentiation of this challenging entity.

Meningiomas, the most common primary intracranial neoplasms, account for more than one-third of primary CNS tumors. While traditionally viewed as benign, meningiomas can be associated with considerable morbidity, and specific meningioma subgroups display more aggressive behavior with higher recurrence rates. The risk stratification for recurrence has been primarily associated with the World Health Organization (WHO) histopathologic grade and extent of resection. However, a growing body of literature has highlighted the value of molecular characteristics in assessing recurrence risk. While maintaining the previous classification system, the 5th edition of the 2021 WHO Classification of Central Nervous System tumors (CNS5) book expands upon the molecular information in meningiomas to help guide management. The WHO CNS5 stratifies meningioma into 3 grades (1-3) based on histopathology criteria and molecular profile. The telomerase reverse transcriptase promoter mutations and cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletions now signify a grade 3 meningioma with increased recurrence risk. Tumor location also correlates with underlying mutations. Cerebral convexity and most spinal meningiomas carry a 22q deletion and/or NF2 mutations, while skull base meningiomas have AKT1, TRAF7, SMO, and/or PIK3CA mutations. MRI is the primary imaging technique for diagnosing and treatment-planning of meningiomas, while DOTATATE PET imaging offers supplementary information beyond anatomic imaging. Herein, we review the evolving molecular landscape of meningiomas, emphasizing imaging/genetic biomarkers and treatment strategies relevant to neuroradiologists.

Conventional MRI is currently the preferred imaging technique for detection and evaluation of malignant spinal lesions. However, this technique is limited in its ability to assess tumor viability. Unlike conventional MRI, dynamic contrast-enhanced (DCE) MRI provides insight into the physiologic and hemodynamic characteristics of malignant spinal tumors and has been utilized in different types of spinal diseases. DCE has been shown to be especially useful in the cancer setting; specifically, DCE can discriminate between malignant and benign vertebral compression fractures as well as between atypical hemangiomas and metastases. DCE has also been shown to differentiate between different types of metastases. Furthermore, DCE can be useful in the assessment of radiation therapy for spinal metastases, including the prediction of tumor recurrence. This review considers data analysis methods utilized in prior studies of DCE-MRI data acquisition and clinical implications.

Background and purpose: EPIMix is a fast brain MRI technique not previously investigated in patients with glioma grades 3 and 4. This pilot study aimed to investigate the diagnostic performance of EPIMix in the radiological treatment evaluation of adult patients with glioma grades 3 and 4 compared to routine clinical MRI (rcMRI).

Materials and methods: Patients with glioma grades 3 and 4 investigated with rcMRI and EPIMix were retrospectively included in the study. Three readers (R1-3) participated in the radiological assessment applying Response Assessment for Neuro-oncology Criteria (RANO 2.0), of which two (R1-2) independently evaluated EPIMix and later rcMRI by measuring contrast-enhancing and non-contrastenhancing tumor regions at each follow-up. For cases with discrepant evaluations, an unblinded side-by-side (EPIMix and rcMRI) reading was performed together with a third reader (R3). Comparisons between methods (EPIMix vs. rcMRI) were performed using Weighted Cohen's kappa. The sensitivity and specificity to detect tumor progression (PD) on a follow-up scan were calculated for EPIMix compared to rcMRI with receiver operating characteristic (ROC) curves to assess the area under the curve (AUC).

Results: In 35 patients (mean age 53, 31% females), a total of 93 MRIs encompassing 58 follow-up investigations showed PD at blinded reading in 33% of EPIMix (19/58, R1-2), while in 31% (18/58 exams, R1), and 34% (20/58 exams, R2) of rcMRI. An almost perfect agreement for tumor category assessment was found between EPIMix and rcMRI (EPIMixR1 vs. rcMRIR1 ϰ= 0.96; EPIMixR2 vs. rcMRIR2 ϰ= 0.89). The sensitivity for EPIMix to detect PD was 1.00 (0.81-1.00) for R1 and 0.90 (0.68-0.99) for R2, while the specificity was 0.97 (0.86-1.00) for R1-2. The AUC for PD was 0.99 for R1 (EPIMixR1 vs. rcMRIR1) and 0.94 for R2 (EPIMixR2 vs. rcMRIR2), DeLong's test AUCR1 vs. AUCR2 p=0.20 (R1-2).

Conclusions: In this pilot study, EPIMix was used as a fast MRI alternative for treatment evaluation of patients with glioma grades 3 and 4, with high, but slightly lower diagnostic performance than rcMRI.

Abbreviations: CR = complete response; EPIMix = multi-contrast echo-planar imaging-based technique; PD = progressive disease; PR = partial response; RANO = response assessment in neuro-oncology; R1 = reader 1; R2 = reader 2; R3 = reader 3; rcMRI = routine clinical MRI; SD = stable disease.

The hypoperfusion intensity ratio (HIR) is a quantitative metric used in vascular occlusion imaging to evaluate the extent of brain tissue at risk due to hypoperfusion. Defined as the ratio of tissue volume with a time-to-maximum (Tmax) of >10 seconds to that of >6 seconds, HIR assists in differentiating between the salvageable penumbra and the irreversibly injured core infarct. This review explores the role of HIR in assessing clinical outcomes and guiding treatment strategies, including mechanical thrombectomy and thrombolytic therapy, for patients with large vessel occlusions (LVOs). Evidence suggests that higher HIR values are associated with worse clinical outcomes, indicating more severe tissue damage and reduced potential for salvage through reperfusion. Additionally, HIR demonstrates predictive accuracy regarding infarct growth, collateral flow, and the risk of reperfusion hemorrhage. It has shown superiority over traditional metrics, such as core infarct volume, in predicting functional outcomes. HIR offers valuable insights for risk stratification and treatment planning in patients with LVOs and distal medium vessel occlusions (DMVOs). Incorporating HIR into clinical practice enhances patient care by improving decision-making processes, promoting timely interventions, and optimizing post-intervention management to minimize complications and improve recovery outcomes.

Background and purpose: Distal medium vessel occlusions (DMVOs) are estimated to cause acute ischemic stroke (AIS) in 25-40% of cases. Prognostic models can inform patient counseling and research by enabling outcome predictions. However, models designed specifically for DMVOs are lacking.

Materials and methods: This retrospective study developed a machine learning model to predict 90-day unfavorable outcome [defined as a modified Rankin Scale (mRS) score of 3-6] in 164 primary DMVO patients. A model developed with the TabPFN algorithm utilized selected clinical, laboratory, imaging, and treatment data with the Least Absolute Shrinkage and Selection Operator feature selection. Performance was evaluated via 5-repeat 5-fold cross-validation. Model discrimination and calibration were evaluated. SHapley Additive Explanations (SHAP) identified influential features. A web application deployed the model for individualized predictions.

Results: The model achieved an area under the receiver operating characteristic curve of 0.815 (95% CI: 0.79-0.841) for predicting unfavorable outcome, demonstrating good discrimination, and a Brier score of 0.19 (95% CI: 0.177-0.202), demonstrating good calibration. SHAP analysis ranked admission National Institutes of Health Stroke Scale (NIHSS) score, premorbid mRS, type of thrombectomy, modified thrombolysis in cerebral infarction score, and history of malignancy as top predictors. The web application enables individualized prognostication.

Conclusions: Our machine learning model demonstrated good discrimination and calibration for predicting 90-day unfavorable outcomes in primary DMVO strokes. This study demonstrates the potential for personalized prognostic counseling and research to support precision medicine in stroke care and recovery.

Abbreviations: DMVO = Distal medium vessel occlusion; AIS = acute ischemic stroke; mRS = modified Rankin Scale; SHAP = SHapley Additive Explanations; NIHSS = National Institutes of Health Stroke Scale; ST = stroke thrombectomy; TRIPOD = Transparent Reporting of Multivariable Prediction Models for Individual Prognosis or Diagnosis; CT = computed tomography; CTP = CT perfusion; MRI = magnetic resonance imaging; CTA = CT angiography; DVT = deep vein thrombosis; PE = pulmonary emboli; TIA = transient ischemic attack; BMI = body mass index; ALP = alkaline phosphatase; ALT = alanine transaminase; AST = aspartate aminotransferase; NCCT-ASPECTS = Alberta Stroke Program Early CT Score; IVT = intravenous thrombolysis; mTICI = modified thrombolysis in cerebral infarction; ER = emergency room; kNN = k-nearest neighbor; LASSO = Least Absolute Shrinkage and Selection Operator; PDPs = partial dependence plots; ROC = receiver operating characteristic; PRC = precision-recall curve; AUROC = area under the ROC curve; AUPRC = area under the PRC; CI = confidence interval.