Clinical Neuroradiology最新文献

Background: Paraclinoid aneurysms, arising from the proximal dural ring and extending to the origin of the posterior communicating artery of the internal carotid artery (ICA), represent a significant proportion of all intracranial aneurysms (IAs). Accurate prediction of the rupture risk of paraclinoid aneurysms is crucial for optimal management. The objective of this study was to identify risk factors for the rupture of paraclinoid aneurysms on the basis of computer-assisted semiautomated measurement (CASAM) and hemodynamics.

Methods: The clinical, demographic and radiological data of the 304 paraclinoid aneurysms (285 unruptured and 19 ruptured) included were extracted from the Chinese Intracranial Aneurysm Project (CIAP) database. Morphological parameters were quantified via CASAM, and hemodynamic simulations were performed via computational fluid dynamics (CFD). Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for aneurysm rupture.

Results: The mean age of the patients was 56.91 ± 11.0 years, with a female predominance (71.7%). Univariate analysis revealed that the undulation index (UI) and nonsphericity index (NSI) were significantly greater in ruptured paraclinoid aneurysms than in unruptured aneurysms. The proportion of ruptured paraclinoid aneurysms located laterally on the ICA was significantly lower than that of those located anteriorly (p = 0.002). Multivariate logistic regression analysis revealed that a greater UI (OR = 1.086, 95% CI 1.012-1.165; p = 0.022) and larger low shear area (LSA) (OR = 1.034, 95% CI 1.004-1.064; p = 0.028) were independent risk factors for rupture.

Conclusions: Our findings indicate that a greater UI and a larger LSA are independent risk factors for the rupture of paraclinoid aneurysms. Compared with aneurysms in other orientations, paraclinoid aneurysms located anteriorly to the ICA are more prone to rupture. These findings may be useful in developing more consummate predictive models to enhance the management and surveillance of paraclinoid aneurysms in the future, leading to improved clinical decision-making and better patient outcomes.

Introduction: Ventriculoperitoneal shunts (VPS) are an essential part of the treatment of hydrocephalus, with numerous valve models available with different ways of indicating pressure levels. The model types often need to be identified on X‑rays to assess pressure levels using a matching template. Artificial intelligence (AI), in particular deep learning, is ideally suited to automate repetitive tasks such as identifying different VPS valve models. The aim of this work was to investigate whether AI, in particular deep learning, allows the identification of VPS models in cranial X‑rays.

Methods: 959 cranial X‑rays of patients with a VPS were included and reviewed for image quality and complete visualization of VPS valves. The images included four VPS model types: Codman Hakim (n = 774, 81%), Codman Certas Plus (n = 117, 12%), Sophysa Sophy Mini SM8 (n = 35, 4%) and proGAV 2.0 (n = 33, 3%). A Convolutional Neural Network (CNN) was trained using stratified five-fold cross-validation to classify the four VPS model types in the dataset. A finetuned CNN pretrained on the ImageNet dataset as well as a model trained from scratch were compared. The averaged performance and uncertainty metrics were evaluated across the cross-validation splits.

Results: The fine-tuned model identified VPS valve models with a mean accuracy of 0.98 ± 0.01, macro-averaged F1 score of 0.93 ± 0.04, a recall of 0.94 ± 0.03 and a precision of 0.95 ± 0.08 across the five cross-validation splits.

Conclusion: Automatic classification of VPS valve models in skull X‑rays, using fully automatable preprocessing steps and a CNN, is feasible. This is an encouraging finding to further explore the possibility of automating VPS valve model identification and pressure level reading in skull X‑rays.

Introduction: Excessive daytime sleepiness (EDS), a prevalent non-motor symptom in Parkinson's disease (PD), significantly impacts the quality of life for PD patients and elevates the risks of injury. Our study is to investigate the altered cortical surface morphology characteristics in PD patients with EDS (PD-EDS).

Methods: Clinical data and magnetic resonance imaging were obtained from the Parkinson's Progression Marker Initiative database, comprising 36 PD-EDS and 98 PD patients without EDS (PD-nEDS). The computational anatomy toolbox was utilized to derive sulcus depth (SD) and deep grey matter (GM) nuclei volumes.

Results: PD-EDS patients exhibited significantly decreased SD values in the right caudal middle frontal gyrus, pars opercularis, and superior temporal cortex relative to PD-nEDS patients. However, no significant differences in deep GM nuclei volumes were identified. Receiver operating characteristic (ROC) curve analyses further revealed that these cortical SD values could potentially serve as a screening index for distinguishing PD-EDS from PD-nEDS. Additionally, although PD-EDS patients had a longer disease duration and poorer performance in motor function and depression compared to PD-nEDS patients, these factors were included as covariates in the neuroimaging analyses.

Conclusion: Our study findings demonstrated that decreased cortical SD values might induce sleep-wake state instability and contribute to the pathophysiological mechanisms of EDS in early-stage PD.

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: 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.

Background and importance: Flow diverters (FDs) provide curative endovascular treatment for wide-necked sidewall aneurysms. The efficacy of FDs for bifurcation or branching sidewall aneurysms is probably limited. We used anatomical flow diversion (AFD) for intractable large cerebral aneurysms. We report our experiences with AFD.

Methods: The concept of AFD is the transformation from the bifurcation or branching sidewall type to the nonbranching sidewall type. Linearization of the parent artery by stenting, intentional branch occlusion, and aneurysmal coil embolization were performed. Furthermore, bypass surgery is performed for patients intolerant to branch occlusions. We evaluated the clinical outcomes of intractable aneurysms treated with AFD.

Results: AFD was performed in seven unruptured large aneurysms. Aneurysmal locations were the top of the basilar artery (BA), BA-superior cerebellar artery (SCA), internal carotid artery (IC)-posterior communicating artery (PcomA), and IC terminal. The mean dome diameter was 17.0 ± 4.6 mm. Six patients underwent bypass surgery. The occluded branches were the PCA + SCA, PcomA, and anterior cerebral artery (ACA) A1. An FD was used in three patients and a neck bridge stent in four patients. No intraprocedural complications occurred. Two postprocedural ischemic complications occurred in one patient. Six (86%) patients demonstrated a modified Rankin Scale (mRS) 0 at the 3-month follow-up, and one with an ischemic complication showed an mRS 5. Complete occlusion of all aneurysms was maintained with a median follow-up duration of 60 months.

Conclusion: AFD is useful for intractable large cerebral aneurysms with high curability, although safety verification is required.