Clinical Neuroradiology最新文献

Purpose: The purpose of this study is to measure the brain temperature (T_br) by using ¹H magnetic resonance spectroscopy (¹H MRS) thermometry and investigate its age and gender differences in healthy adults. The brain temperature was further compared with the body temperature (T_bo) to investigate the possible existence of brain-body temperature gradient (∆T).

Methods: A total of 80 subjects were included in this study. ¹H MRS data were collected on a 3.0T MR scanner using Point Resolved Selective Spectroscopy (PRESS) sequence. Voxels were positioned in the right frontal (RF) lobe and left frontal (LF) lobe, respectively. The temperature of each voxel was calculated by chemical shift difference (∆δ) between H₂O and NAA which was obtained by LCModel software. The average temperature of bilateral frontal lobe voxels was defined as T_br for each subject. The average forehead temperature was acquired before MR scanning, defined as T_bo, in this study. The difference between T_br and T_bo, denoted as the brain-body temperature gradient (∆T), was calculated. Age and gender characteristics of T_br, ∆T and T_bo were analyzed.

Results: T_br (38.51 ± 0.59℃) was higher than T_bo (36.47 ± 0.26℃) (P < 0.05). Negative correlations were observed between T_br and age (r = -0.49, P < 0.05) and between ∆T and age (r = -0.44, P < 0.05), whereas no correlation existed between T_bo and age (r = -0.03, P = 0.79).

Conclusion: Our observation demonstrated that the brain temperature, derived from ¹H MRS thermometry, is significantly higher than the body temperature, indicating the existence of a brain-body temperature gradient, and the brain temperature gradually decreases with age.

Purpose: Glioma is a complex cancer comprising various subtypes and mutations, which may have different metabolic characteristics that can potentially be investigated and identified using perfusion imaging. Therefore, the aim of this work was to use radiomics and machine learning analysis of arterial spin labeling MRI data to automatically differentiate glioma subtypes and mutations.

Methods: A total of 495 Arterial Spin Labeling (ASL) perfusion imaging datasets from the UCSF Glioma database were used in this study. These datasets were segmented to delineate the tumor volume and classified according to tumor grade, pathological diagnosis, and IDH status. Perfusion image data was obtained from a 3T MRI scanner using pseudo-continuous ASL. High level texture features were extracted for each ASL dataset using PyRadiomics after tumor volume segmentation and then analyzed using a machine learning framework consisting of ReliefF feature ranking and logistic model tree classification algorithms.

Results: The results of the evaluation revealed balanced accuracies for the three endpoints ranging from 55.76% (SD = 4.28, 95% CI: 53.90-57.65) for the tumor grade using 25.4 ± 37.21 features, 62.53% (SD = 2.86, 95% CI: 61.27-63.78) for the mutation status with 23.3 ± 29.17 picked features, and 80.97% (SD = 1.83, 95% CI: 80.17-81.78) for the pathological diagnosis which used 47.3 ± 32.72 selected features.

Conclusions: Radiomics and machine learning analysis of ASL perfusion data in glioma patients hold potential for aiding in the diagnosis and treatment of glioma, mainly for discerning glioblastoma from astrocytoma, while performance for tumor grading and mutation status appears limited.

Purpose: The modified Rankin Scale (mRS) at 90 days is the primary outcome in most acute stroke studies, but the long follow-up period has disadvantages. The National Institutes of Health Stroke Scale (NIHSS) at 24 h shows a strong, but imperfect, association with 90-day mRS. This study examines the association between 24-hour NIHSS and 90-day mRS and reasons for discrepancies.

Methods: Data are from the ESCAPE-NA1 thrombectomy patients. To address the non-normality distribution of the NIHSS and include deceased patients, a 7-point ordinal score was generated by grouping 24-hour NIHSS. The association of ordinal 24-hour NIHSS and 90-day mRS was assessed with adjusted ordinal logistic regression. Differences in baseline and treatment/post-treatment variables were compared between patients with discordant and concordant outcomes.

Results: One-thousand-seventy-six patients with available 24-hour NIHSS and 90-day mRS were included (median 24-hour NIHSS 6[IQR: 2-14], median 90-day mRS 2[IQR: 1-4]). Ordinal 24-hour NIHSS was associated with 90-day mRS (adjusted cOR 2.53 [95%CI 2.33-2.74]). Forty-eight (4.5%) patients had discordant outcomes. Of those, 19(1.8%) had 24-hour NIHSS < 6 and 90-day mRS5-6; all of which had ≥ 1 severe adverse event, most commonly pneumonia (6[31.6%]) or recurrent stroke (4[21.1%]). Twenty-nine patients (2.7%) had 24-hour NIHSS > 14 and 90-day mRS 0-2. In these patients, baseline NIHSS and ASPECTS was lower, and collateral status was worse.

Conclusion: An ordinal NIHSS score that includes death at 24 h shows a strong association with 90-day mRS, suggesting that it could be used as an alternative outcome. Patients with discrepant outcomes differed from the remaining patients regarding their baseline NIHSS, ASPECTS, collateral status, and post-stroke complications.

Purpose: Since perfusion imaging may be unavailable in smaller hospitals, alternative imaging selection methods for acute ischemic stroke can improve outcomes and optimize resources. This study assessed the safety and effectiveness of using imaging criteria other than DEFUSE 3 and DAWN for thrombectomy beyond 6 h from symptom onset in patients stroke in the anterior circulation.

Methods: This is a retrospective, single-center analysis of consecutive patients with large vessel occlusion in the anterior circulation undergoing thrombectomy. Patients were categorized into two groups based on the collateral status (moderate collaterals and good collaterals).

Results: Among 198 patients, 106 (54%) met the inclusion criteria and were analyzed. Good collateral status was observed in 78 (74%) patients. Patients with good collaterals showed significantly lower mRS scores at discharge and at 90 days compared to their counterparts with moderate collateral status (4 (3-4) vs. 4 (4-5); p = 0.001 and 2 (0-4) vs. 6 (3-6); p < 0.001, respectively). More patients with good collateral status achieved favorable outcomes at 90 days compared to those with moderate status (48 (61.5%) vs. 5 (17.9%); p < 0.001). Good collaterals were an independent predictor of good clinical outcomes at 90 days (OR = 1.31, 95% CI: 1.13-1.53, p < 0.001).

Conclusion: Selecting patients for endovascular treatment of acute ischemic stroke using non-contrast CT and CT angiography shows 90-day outcomes similar to the DAWN and DEFUSE-3 trials. Using collateral status on CT angiography can predict favorable outcomes after mechanical thrombectomy in resource-limited settings where perfusion imaging is unavailable.

Purpose: Intracranial hemorrhage (ICH) is a life-threatening condition requiring rapid diagnostic and therapeutic action. This study evaluates whether Artificial intelligence (AI) can provide high-quality ICH diagnostics and turnaround times suitable for routine radiological practice.

Methods: A convolutional neural network (CNN) was trained and validated to detect ICHs on DICOM images of cranial CT (CCT) scans, utilizing about 674,000 individually labeled slices. The CNN was then incorporated into a commercial AI engine and seamlessly integrated into three pilot centers in Germany. A real-world test-dataset was extracted and manually annotated by two experienced experts. The performance of the AI algorithm against the two raters was assessed and compared to the inter-rater agreement. The overall time ranging from data acquisition to the delivery of the AI results was analyzed.

Results: Out of 6284 CCT examinations acquired in three different centers, 947 (15%) had ICH. Breakdowns of hemorrhage types included 8% intraparenchymal, 3% intraventricular, 6% subarachnoidal, 7% subdural, < 1% epidural hematomas. Comparing the AI's performance on a subset of 255 patients with two expert raters, it achieved a sensitivity of 0.90, a specificity of 0.96, an accuracy of 0.96. The corresponding inter-rater agreement was 0.84, 0.98, and 0.96. The overall median processing times for the three centers were 9, 11, and 12 min, respectively.

Conclusion: We showed that an AI algorithm for the automatic detection of ICHs can be seamlessly integrated into clinical workflows with minimal turnaround time. The accuracy was on par with radiology experts, making the system suitable for routine clinical use.

Purpose

Polymicrogyria (PMG) is a cortical malformation frequently associated with epilepsy. Our aim was to investigate the frequency and conspicuity of enlarged perivascular spaces (EPVS) underneath dysplastic cortex as a potentially underrecognized feature of PMG in pediatric and adolescent patients undergoing clinical magnetic resonance imaging (MRI).

Methods

We analyzed data from 28 pediatric and adolescent patients with PMG and a matched control group, ranging in age from 2 days to 21 years, who underwent MRI at 1.5T or 3T. T2-weighted MR images were examined for the presence of EPVS underneath the dysplastic cortex. The quantity of EPVS was graded from 0 to 4 (0: none, 1: < 10, 2: 11–20, 3: 21–40, 4: > 40 EPVS). We then compared the presence and quantity of EPVS to the matched controls in terms of total EPVS scores, and EPVS scores underneath the dysplastsic cortex depending on the age groups, the localization of PMG, and the MRI field strength.

Results

In 23/28 (82%) PMG patients, EPVS spatially related to the dysplastic cortex were identified. EPVS scores were significantly higher in PMG patients compared to controls, independent from age or PMG location. No significant differences were observed in EPVS scores in patients examined at 1.5T compared to those examined at 3T.

Conclusion

EPVS underneath the dysplastic cortex were identified in 82% of patients. EPVS may serve as an important clue for PMG and a marker for cortical malformation.

Many functional magnetic resonance imaging (fMRI) studies and presurgical mapping applications rely on mass-univariate inference with subsequent multiple comparison correction. Statistical results are frequently visualized as thresholded statistical maps. This approach has inherent limitations including the risk of drawing overly-selective conclusions based only on selective results passing such thresholds. This article gives an overview of both established and newly emerging scientific approaches to supplement such conventional analyses by incorporating information about subthreshold effects with the aim to improve interpretation of findings or leverage a wider array of information. Topics covered include neuroimaging data visualization, p-value histogram analysis and the related Higher Criticism approach for detecting rare and weak effects. Further examples from multivariate analyses and dedicated Bayesian approaches are provided.