Transition of intracranial aneurysmal wall enhancement from high to low wall shear stress mediation with size increase: A hemodynamic study based on 7T magnetic resonance imaging.

Background: Wall shear stress (WSS) has been proved to be related to the formation, development and rupture of intracranial aneurysms. Aneurysm wall enhancement (AWE) on magnetic resonance imaging (MRI) can be caused by inflammation and have confirmed its relationship with low WSS. High WSS can also result in inflammation but the research of its correlation with AWE is lack because of the focus on large aneurysms limited by 3T MRI in most previous studies.This study aimed to assess the potential association between high or low WSS and AWE in different aneuryms. Especially the relationship between high WSS and AWE in small aneurysm. Methods: Forty-three unruptured intracranial aneurysms in 42 patients were prospectively included for analysis. 7.0 T MRI was used for imaging. Aneurysm size was measured on three-dimensional time-of-flight (TOF) images. Aneurysm-to-pituitary stalk contrast ratio (CRstalk) was calculated on post-contrast black-blood T1-weighted fast spin echo sequence images. Hemodynamics were assessed by four-dimensional flow MRI. Results: The small aneurysms group had more positive WSS-CRstalk correlation coefficient distribution (dome: 78.6 %, p = 0.009; body: 50.0 %, p = 0.025), and large group had more negative coefficient distribution (dome: 44.8 %, p = 0.001; body: 69.0 %, p = 0.002). Aneurysm size was positively correlated with the significant OSI-CRstalk correlation coefficient at the dome (p = 0.012) and body (p = 0.010) but negatively correlated with the significant WSS-CRstalk correlation coefficient at the dome (p < 0.001) and body (p = 0.017). Conclusion: AWE can be mediated by both high and low WSS, and translate from high WSS- to low WSS-mediated pathways as size increase. Additionally, AWE may serve as an indicator of the stage of aneurysm development via different correlations with hemodynamic factors.

Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation.

BACKGROUND: Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task. OBJECTIVE: To evaluates a novel deep-learning algorithm, named vessel attention (VA)-Unet, for the efficient detection and segmentation of IAs. METHODS: This retrospective study was conducted using head CT angiography (CTA) examinations depicting IAs from two hospitals in China between 2010 and 2021. Training included cases with subarachnoid hemorrhage (SAH) and arterial stenosis, common accompanying vascular abnormalities. Testing was performed in cohorts with reference-standard digital subtraction angiography (cohort 1), with SAH (cohort 2), acquired outside the time interval of training data (cohort 3), and an external dataset (cohort 4). The algorithm's performance was evaluated using sensitivity, recall, false positives per case (FPs/case), and Dice coefficient, with manual segmentation as the reference standard. RESULTS: The study included 3190 CTA scans with 4124 IAs. Sensitivity, recall, and FPs/case for detection of IAs were, respectively, 98.58%, 96.17%, and 2.08 in cohort 1; 95.00%, 88.8%, and 3.62 in cohort 2; 96.00%, 93.77%, and 2.60 in cohort 3; and, 96.17%, 94.05%, and 3.60 in external cohort 4. The segmentation accuracy, as measured by the Dice coefficient, was 0.78, 0.71, 0.71, and 0.66 for cohorts 1-4, respectively. VA-Unet detection recall and FPs/case and segmentation accuracy were affected by several clinical factors, including aneurysm size, bifurcation aneurysms, and the presence of arterial stenosis and SAH. CONCLUSIONS: VA-Unet accurately detected and segmented IAs in head CTA comparably to expert interpretation. The proposed algorithm has significant potential to assist radiologists in efficiently detecting and segmenting IAs from CTA images.

Hybrid surgery can improve neurocognitive function in patients with internal carotid artery occlusion.

Internal carotid artery occlusion (ICAO) is a relatively uncommon but important cause of transient ischaemic attack and cerebral infarction. Hybrid surgery (HS) improves cerebral perfusion, but its impact on neurocognitive function has been controversial. Patients with symptomatic chronic ICAO treated by hybrid surgery or medical treatment from 2016 to 2019 were included. We recorded and analysed the clinical characteristics, angiographic data, outcomes and cognitive status. Functional assessments, including the National Institutes of Health Stroke Scale (NIHSS), the Barthel Index, and a battery of neuropsychological tests, including the Mini-Mental State Examination (MMSE), Alzheimer Disease Assessment Scale-Cognitive Subtest (ADAS-cog), verbal fluency, and Colour Trail test Parts 1 and 2, were administered. Significant improvements in the ADAS (before, 7.5 ± 6.2 versus after, 5.2 ± 5.7; P = 0.022), MMSE (before, 25.5 ± 2.8 versus after, 28.1 ± 2.3; P = 0.013), and Colour Trail test Part 1 (before, 118.3 ± 26.5 versus after, 96.2 ± 23.1; P = 0.016) were observed six months after HS. Moreover, the abovementioned postprocedure scales were ameliorated in the HS group. This study found that in patients with multiple symptomatic ICAO and objective ipsilateral ischaemia, successful HS leads to improvement in the scores of three cognitive tests.

Efficacy and safety of a neurointerventional operation robotic assistance system in cerebral angiography.

BACKGROUND: At present, neurointerventional surgery requires angiographers to perform operations in the digital subtraction angiography (DSA) room. Ionising radiation and chronic joint damage are still unavoidable for angiographers. Therefore, we researched and developed a neurointerventional robot-assisted system, which is operated by angiographers in an operating room outside the DSA room. We have conducted a prospective, multicentre, randomised controlled trial to evaluate the safety and efficacy of a robot-assisted system in human cerebral angiography. In the future, this research will provide a platform for the research and development of an intelligent surgical system and bring revolutionary progress in neurointerventional surgery. METHODS: From December 2020 to December 2021, 260 patients were enrolled from three medical centres, who were randomly and equally divided into a robot-assisted system group and a clinical routine cerebral angiography group. The success rate of angiography, the rate of the catheter reaching the target vessel, the operation time, X-ray radiation exposure and the incidence of related adverse events were compared between the two groups. RESULTS: A total of 257 patients completed this trial; baseline characteristics of the two groups did not differ significantly. The success rate of angiography in both the control group and the experimental group was 100%. The rate of the catheter reaching the target vessel was 99.23% and 100.00% in the control and experimental groups, respectively. For the control versus experimental groups, the angiographic operation time was 48.59±25.60 min versus 47.94±27.49 min, respectively; the X-ray radiation dose was 735.01±554.77 mGy versus 821.65±705.45 mGy, respectively; and the incidence of adverse events was 23.44% versus 22.48%, respectively. No statistical differences were present between the two groups. CONCLUSION: The robot-assisted surgical system is more convenient for cerebral angiography and is as safe and effective as the traditional cerebral angiography.

Long-term outcomes and dynamic changes of in-stent stenosis after Pipeline embolization device treatment of intracranial aneurysms.

BACKGROUND: Flow diverters have revolutionized the treatment of intracranial aneurysms. However, the delayed complications associated with flow diverter use are unknown. OBJECTIVE: To evaluate the incidence, severity, clinical outcomes, risk factors, and dynamic changes associated with in-stent stenosis (ISS) after treatment with a Pipeline embolization device (PED). METHODS: Patients who underwent PED treatment between 2015 and 2020 were enrolled. The angiographic, clinical, and follow-up data of 459 patients were independently reviewed by four neuroradiologists to identify ISS. Binary logistic regression was conducted to determine ISS risk factors, and an ISS-time curve was established to demonstrate dynamic changes in ISS after PED implantation. RESULTS: Of the 459 treated patients, 69 (15.0%) developed ISS. At follow-up, nine patients (2.0%) with ISS demonstrated reversal, while 18 (3.9%) developed parental artery occlusion. A total of 380 patients (82.8%) achieved complete aneurysm occlusion (O'Kelly-Marotta grade D). Patients with posterior-circulation aneurysm (OR=2.895, 95% CI (1.732 to 4.838; P<0.001) or balloon angioplasty (OR=1.992, 95% CI 1.162 to 3.414; P=0.037) were more likely to develop ISS. Patients aged >54 years (OR=0.464, 95% CI 0.274 to 0.785; P=0.006) or with a body mass index of >28 kg/m2 (OR=0.427, 95% CI 0.184 to 0.991; P=0.026) had a lower ISS risk. Intimal hyperplasia initiated by PED placement peaked within 1 year after the procedure, rarely progressed after 12 months, and tended to reverse within 24 months. CONCLUSIONS: ISS is a common, benign, and self-limiting complication of PED implantation in the Chinese population.

Automated anatomical labeling of the intracranial arteries via deep learning in computed tomography angiography.

Background and purpose: Anatomical labeling of the cerebral vasculature is a crucial topic in determining the morphological nature and characterizing the vital variations of vessels, yet precise labeling of the intracranial arteries is time-consuming and challenging, given anatomical structural variability and surging imaging data. We present a U-Net-based deep learning (DL) model to automatically label detailed anatomical segments in computed tomography angiography (CTA) for the first time. The trained DL algorithm was further tested on a clinically relevant set for the localization of intracranial aneurysms (IAs). Methods: 457 examinations with varying degrees of arterial stenosis were used to train, validate, and test the model, aiming to automatically label 42 segments of the intracranial arteries [e.g., 7 segments of the internal carotid artery (ICA)]. Evaluation metrics included Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD). Additionally, 96 examinations containing at least one IA were enrolled to assess the model's potential in enhancing clinicians' precision in IA localization. A total of 5 clinicians with different experience levels participated as readers in the clinical experiment and identified the precise location of IA without and with algorithm assistance, where there was a washout period of 14 days between two interpretations. The diagnostic accuracy, time, and mean interrater agreement (Fleiss' Kappa) were calculated to assess the differences in clinical performance of clinicians. Results: The proposed model exhibited notable labeling performance on 42 segments that included 7 anatomical segments of ICA, with the mean DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm. Furthermore, the model demonstrated superior labeling performance in healthy subjects compared to patients with stenosis (DSC: 0.91 vs. 0.89, p < 0.05; HD: 4.75 vs. 6.19, p < 0.05). Concurrently, clinicians with model predictions achieved significant improvements when interpreting the precise location of IA. The clinicians' mean accuracy increased by 0.04 (p = 0.003), mean time to diagnosis reduced by 9.76 s (p < 0.001), and mean interrater agreement (Fleiss' Kappa) increased by 0.07 (p = 0.029). Conclusion: Our model stands proficient for labeling intracranial arteries using the largest CTA dataset. Crucially, it demonstrates clinical utility, helping prioritize the patients with high risks and ease clinical workload.

Machine learning to predict in-stent stenosis after Pipeline embolization device placement.

Background: The Pipeline embolization device (PED) is a flow diverter used to treat intracranial aneurysms. In-stent stenosis (ISS) is a common complication of PED placement that can affect long-term outcome. This study aimed to establish a feasible, effective, and reliable model to predict ISS using machine learning methodology. Methods: We retrospectively examined clinical, laboratory, and imaging data obtained from 435 patients with intracranial aneurysms who underwent PED placement in our center. Aneurysm morphological measurements were manually measured on pre- and posttreatment imaging studies by three experienced neurointerventionalists. ISS was defined as stenosis rate >50% within the PED. We compared the performance of five machine learning algorithms (elastic net (ENT), support vector machine, Xgboost, Gaussian Naïve Bayes, and random forest) in predicting ISS. Shapley additive explanation was applied to provide an explanation for the predictions. Results: A total of 69 ISS cases (15.2%) were identified. Six predictors of ISS (age, obesity, balloon angioplasty, internal carotid artery location, neck ratio, and coefficient of variation of red cell volume distribution width) were identified. The ENT model had the best predictive performance with a mean area under the receiver operating characteristic curve of 0.709 (95% confidence interval [CI], 0.697-0.721), mean sensitivity of 77.9% (95% CI, 75.1-80.6%), and mean specificity of 63.4% (95% CI, 60.8-65.9%) in Monte Carlo cross-validation. Shapley additive explanation analysis showed that internal carotid artery location was the most important predictor of ISS. Conclusion: Our machine learning model can predict ISS after PED placement for treatment of intracranial aneurysms and has the potential to improve patient outcomes.

Protocol and Preliminary Results of the Establishment of Intracranial Aneurysm Database for Artificial Intelligence Application Based on CTA Images.

Background and Purpose: Unruptured intracranial aneurysms (UIAs) are increasingly being detected in clinical practice. Artificial intelligence (AI) has been increasingly used to assist diagnostic techniques and shows encouraging prospects. In this study, we reported the protocol and preliminary results of the establishment of an intracranial aneurysm database for AI application based on computed tomography angiography (CTA) images. Methods: Through a review of picture archiving and communication systems, we collected CTA images of patients with aneurysms between January 2010 and March 2021. The radiologists performed manual segmentation of all diagnosed aneurysms on subtraction CTA as the basis for automatic aneurysm segmentation. Then, AI will be applied to two stages of aneurysm treatment, namely, automatic aneurysm detection and segmentation model based on the CTA image and the aneurysm risk prediction model. Results: Three medical centers have been included in this study so far. A total of 3,190 cases of CTA examinations with 4,124 aneurysms were included in the database. All identified aneurysms from CTA images that enrolled in this study were manually segmented on subtraction CTA by six readers. We developed a structure of 3D-Unet for aneurysm detection and segmentation in CTA images. The algorithm was developed and tested using a total of 2,272 head CTAs with 2,938 intracranial aneurysms. The recall and false positives per case (FP/case) of this model for detecting aneurysms were 0.964 and 2.01, and the Dice values for aneurysm segmentation were 0.783. Conclusion: This study introduces the protocol and preliminary results of the establishment of the intracranial aneurysm database for AI applications based on CTA images. The establishment of a multicenter database based on CTA images of intracranial aneurysms is the basis for the application of AI in the diagnosis and treatment of aneurysms. In addition to segmentation, AI should have great potential for aneurysm treatment and management in the future.

Bifurcated Aneurysm Location Predicts In-Stent Stenosis After Neuroform-EZ Stent-Assisted Coiling for Intracranial Aneurysm.

Background and Purpose: The Neuroform EZ stent system (Boston Scientific Corporation, Fremont, CA, United States) is a fourth-generation intracranial aneurysm stent designed specifically for the cerebrovasculature to support aneurysm treatment. In this study, we analyzed our consecutive series of patients with aneurysm treated with the Neuroform EZ stent, with special attention to the occurrence of in-stent stenosis (ISS). Methods: A retrospective review of our center's electronic database was conducted to identify all patients with intracranial aneurysms who underwent aneurysm treatment with the Neuroform EZ stent between January 2016 and October 2018. Patients with at least one digital subtraction angiography (DSA) follow-up in our hospital were enrolled in this study. In-stent stenosis (ISS) was graded as mild (<2-5%), moderate (25-50%), or severe (>50%). Results: The study included 114 patients (78 women, 68.4%; median age 57.2 ± 9 years) with a total of 116 aneurysms. Of the 116 lesions, 20 were identified with ISS (17.2%) at a mean follow-up of 6.9 ± 1.7 months, and ISS was mild in 30% (6/20), moderate in 50% (10/20), and severe in 20% (4/20). No patients were symptomatic or required further intervention. Patients who developed ISS were younger than those without ISS (52.6 ± 7.8 vs. 57.9 ± 9; p = 0.016). The proportion of aneurysms located at the artery bifurcation was significantly higher in patients with stenosis than located at the sidewall artery (37.9 vs. 10.3%; p = 0.002). In the multivariable analysis, the patients' age (OR = 0.94; 95% CI 0.88-0.998; p = 0.02) and bifurcated aneurysm location (OR = 4.59; 95% CI 1.54-13.67; p = 0.006) were independent predictors of ISS. Conclusions: In this retrospective study, the incidence of ISS after Neuroform EZ stent placement was 17.2%, and all the ISS cases were asymptomatic. Patients with younger age and bifurcated aneurysm location are more likely to develop ISS. Although Neuroform EZ stent is particularly suitable for bifurcated aneurysms, the ISS for this location should be focused upon.

Quantitative analysis of unruptured intracranial aneurysm wall thickness and enhancement using 7T high resolution, black blood magnetic resonance imaging.

BACKGROUND: This study was performed to quantify intracranial aneurysm wall thickness (AWT) and enhancement using 7T MRI, and their relationship with aneurysm size and type. METHODS: 27 patients with 29 intracranial aneurysms were included. Three-dimensional T1 weighted pre- and post-contrast fast spin echo with 0.4 mm isotropic resolution was used. AWT was defined as the full width at half maximum on profiles of signal intensity across the aneurysm wall on pre-contrast images. Enhancement ratio (ER) was defined as the signal intensity of the aneurysm wall over that of the brain parenchyma. The relationships between AWT, ER, and aneurysm size and type were investigated. RESULTS: 7T MRI revealed large variations in AWT (range 0.11-1.24 mm). Large aneurysms (>7 mm) had thicker walls than small aneurysms (≤7 mm) (0.49±0.05 vs 0.41±0.05 mm, p<0.001). AWT was similar between saccular and fusiform aneurysms (p=0.546). Within each aneurysm, a thicker aneurysm wall was associated with increased enhancement in 28 of 29 aneurysms (average r=0.65, p<0.05). Thicker walls were observed in enhanced segments (ER >1) than in non-enhanced segments (0.53±0.09 vs 0.38±0.07 mm, p<0.001). CONCLUSION: Improved image quality at 7T allowed quantification of intracranial AWT and enhancement. A thicker aneurysm wall was observed in larger aneurysms and was associated with stronger enhancement.

Comparison of 7 T and 3 T vessel wall MRI for the evaluation of intracranial aneurysm wall.

OBJECTIVES: To compare the visibility of intracranial aneurysm wall and thickness quantification between 7 and 3 T vessel wall imaging and evaluate the association between aneurysm size and wall thickness. METHODS: Twenty-nine patients with 29 unruptured intracranial aneurysms were prospectively recruited for 3D T1-weighted vessel wall MRI at both 3 T and 7 T with 0.53 mm (3 T) and 0.4 mm (7 T) isotropic resolution, respectively. Two neuroradiologists independently evaluated wall visibility (0-5 Likert scale), quantified the apparent wall thickness (AWT) using a semi-automated full-width-half-maximum method, calculated wall sharpness, and measured the wall-to-lumen contrast ratio (CRwall/lumen). RESULTS: Twenty-four patients with 24 aneurysms were included in this study. 7 T achieved significantly better aneurysm wall visibility than 3 T (3.6 ± 1.1 vs 2.7 ± 0.8, p = 0.003). AWT measured on 3 T and 7 T had a good correlation (averaged r = 0.63 ± 0.19). However, AWT on 3 T was 15% thicker than that on 7 T (0.52 ± 0.07 mm vs 0.45 ± 0.05 mm, p < 0.001). Wall sharpness on 7 T was 57% higher than that on 3 T (1.95 ± 0.32 mm-1 vs 1.24 ± 0.15 mm-1, p < 0.001). CRwall/lumen on 3 T and 7 T was comparable (p = 0.424). AWT on 7 T was positively correlated with aneurysm size (saccular: r = 0.58, q = 0.046; fusiform: r = 0.67, q = 0.049). CONCLUSIONS: 7 T provides better visualization of intracranial aneurysm wall with higher sharpness than 3 T. 3 T overestimates the wall thickness relative to 7 T. Aneurysm wall thickness is positively correlated with aneurysm size. 7 T MRI is a promising tool to evaluate aneurysm wall in vivo. KEY POINTS: • 7 T provides better visualization of intracranial aneurysm wall with higher sharpness than 3 T. • 3 T overestimates the wall thickness comparing with 7 T. • Aneurysm wall thickness is positively correlated with aneurysm size.

Case Report: De novo Vertebral Artery Dissection After Intravascular Stenting of the Contralateral Unruptured Vertebral Artery Aneurysm.

Spontaneous vertebral artery dissecting aneurysm has been increasingly attributed as a major cause of focal neurological deficits due to vertebrobasilar artery ischemia or subarachnoid hemorrhage (SAH). Although the development of spontaneous vertebral artery dissecting aneurysm (VADA) is rare, de novo VADA after treatment of contralateral vertebral artery (VA) is more less frequently observed. There are only a few reports related to de novo VADA after treatment of the contralateral VA in the medical literature. The mechanisms responsible for de novo dissection after treatment of unilateral VADA are still not clearly understood. In this manuscript, we report an unusual case of a patient with a de novo VADA after placement of a pipeline embolization device (PED) stent on the contralateral VA along with a thorough review of the literature. A 42-years old male patient was referred to the hospital with sudden onset of dizziness, nausea, and vomiting. Initial digital subtraction angiography (DSA) images demonstrated a VADA in the fourth segment of the left VA without the involvement of the posterior inferior cerebellar artery (PICA). There were no significant abnormalities found in the right vertebral artery. He underwent an endovascular pipeline embolization to treat the dissecting aneurysm (DA). Surprisingly, follow-up DSA imaging 14 months after the initial treatment showed a segmental dilatation and narrowing of the right VA, which suggested a de novo VADA on the right side that had occurred postoperatively. This was followed by a tent-assisted coil embolization therapy for occluding this de novo VADA. This patient showed an uneventful postoperative course with no neurological abnormalities. In addition to hemodynamic stress changes, the unique clinicopathological features of dissecting aneurysms may contribute significantly to the pathogenesis of de novo VA dissection. Given that VA in VADA patients may be vulnerable on both sides, it is important to consider the risk of de novo dissection after initial aneurysm treatment. The bilateral vertebral artery has to be carefully observed when treating any VADA patient to prevent any complications.

Deep neural network-based detection and segmentation of intracranial aneurysms on 3D rotational DSA.

OBJECTIVE: Accurate diagnosis and measurement of intracranial aneurysms are challenging. This study aimed to develop a 3D convolutional neural network (CNN) model to detect and segment intracranial aneurysms (IA) on 3D rotational DSA (3D-RA) images. METHODS: 3D-RA images were collected and annotated by 5 neuroradiologists. The annotated images were then divided into three datasets: training, validation, and test. A 3D Dense-UNet-like CNN (3D-Dense-UNet) segmentation algorithm was constructed and trained using the training dataset. Diagnostic performance to detect aneurysms and segmentation accuracy was assessed for the final model on the test dataset using the free-response receiver operating characteristic (FROC). Finally, the CNN-inferred maximum diameter was compared against expert measurements by Pearson's correlation and Bland-Altman limits of agreement (LOA). RESULTS: A total of 451 patients with 3D-RA images were split into n = 347/41/63 training/validation/test datasets, respectively. For aneurysm detection, observed FROC analysis showed that the model managed to attain a sensitivity of 0.710 at 0.159 false positives (FP)/case, and 0.986 at 1.49 FP/case. The proposed method had good agreement with reference manual aneurysmal maximum diameter measurements (8.3 ± 4.3 mm vs. 7.8 ± 4.8 mm), with a correlation coefficient r = 0.77, small bias of 0.24 mm, and LOA of -6.2 to 5.71 mm. 37.0% and 77% of diameter measurements were within ±1 mm and ±2.5 mm of expert measurements. CONCLUSIONS: A 3D-Dense-UNet model can detect and segment aneurysms with relatively high accuracy using 3D-RA images. The automatically measured maximum diameter has potential clinical application value.

A novel noncontact detection method of surgeon's operation for a master-slave endovascular surgery robot.

Master-slave endovascular interventional surgery (EIS) robots have brought revolutionary advantages to traditional EIS, such as avoiding X-ray radiation to the surgeon and improving surgical precision and safety. However, the master controllers of most of the current EIS robots always lead to bad human-machine interaction, because of the difference in nature between the rigid operating handle and the flexible medical catheter used in EIS. In this paper, a noncontact detection method is proposed, and a novel master controller is developed to realize real-time detection of surgeon's operation without interference to the surgeon. A medical catheter is used as the operating handle. It is enabled by using FAST corner detection algorithm and optical flow algorithm to track the corner points of the continuous markers on a designed sensing pipe. A mathematical model is established to calculate the axial and rotational motion of the sensing pipe according to the moving distance of the corner points in image coordinates. A master-slave EIS robot system is constructed by integrating the proposed master controller and a developed slave robot. Surgical task performance evaluation in an endovascular evaluator (EVE) is conducted, and the results indicate that the proposed detection method breaks through the axial measuring range limitation of the previous marker-based detection method. In addition, the rotational detection error is reduced by 92.5% compared with the previous laser-based detection method. The results also demonstrate the capability and efficiency of the proposed master controller to control the slave robot for surgical task implementation. Graphical abstract A novel master controller is developed to realize real-time noncontact detection of surgeon's operation without interference to the surgeon. The master controller is used to remotely control the slave robot to implement certain surgical tasks.

Wall enhancement of intracranial saccular and fusiform aneurysms may differ in intensity and extension: a pilot study using 7-T high-resolution black-blood MRI.

PURPOSE: To evaluate and compare wall enhancement patterns in saccular and fusiform intracranial aneurysms using high-resolution black-blood MRI at 7 T. METHODS: Thirty-one patients with 32 unruptured intracranial aneurysms (21 saccular and 11 fusiform) underwent 7-T black-blood MRI. Aneurysm wall enhancement (AWE) was categorized as follows: no wall enhancement (NWE), focal wall enhancement (FWE), and uniform wall enhancement (UWE). The degree of enhancement was scored as follows: 0 (no enhancement), 1 (signal intensity (SI) of the aneurysm wall less than that of the pituitary infundibulum), and 2 (equal to that of the pituitary infundibulum). The chi-squared test was used to compare the AWE pattern and degree between saccular and fusiform aneurysms. RESULTS: In saccular aneurysms, 12/21 (57%) enhanced. Of these, 9 showed FWE (5 grade 1 and 4 grade 2), and 3 showed UWE (2 grade 1 and 1 grade 2). In fusiform aneurysms, 11/11 (100%) enhanced. Of these, 1 showed FWE and 10 showed UWE. All fusiform aneurysms had grade-2 enhancement. Fusiform aneurysms had more extensive and higher SI AWE than saccular aneurysms (p < 0.01) despite having a similar size (6.9 ± 3.0 mm vs. 8.0 ± 2.9, p = 0.23). For saccular aneurysm, larger aneurysm size was correlated with higher degree of enhancement with Pearson's r = 0.64 (p = 0.002). CONCLUSION: Intracranial fusiform aneurysms had enhancement of higher SI and that covered a more extensive area than saccular aneurysms, which might indicate differences in vessel wall pathology. KEY POINTS: • Intracranial aneurysm wall enhancement can be reliably characterized by 7-T black-blood MRI. • AWE in intracranial fusiform aneurysms presents over a larger surface area and with greater signal intensity as compared with that in saccular aneurysms, which might indicate differences in pathology. • Stronger signal intensity of AWE correlates with the aneurysm size in saccular aneurysms.

Automatic Diagnosis Based on Spatial Information Fusion Feature for Intracranial Aneurysm.

Timely and accurate auxiliary diagnosis of intracranial aneurysm can help radiologist make treatment plans quickly, saving lives and cutting costs at the same time. At present, Digital Subtraction Angiography (DSA) is the gold standard for the diagnosis of intracranial aneurysm, but as radiologists interpret those imaging sequences frame by frame, misdiagnosis might occur. The utilization of computer-aided diagnosis (CAD) can ease the burdens of radiologists and improve the detection accuracy of aneurysms. In this article, a deep learning method is applied to detect the intracranial aneurysm in 3D Rotational Angiography (3D-RA) based on a spatial information fusion (SIF) method, and instead of a 3D vascular model, 2D image sequences are used. Given the intracranial aneurysm and vascular overlap having similar feature in the most time, rather than focusing on distinguishing them in one frame, the morphological differences between frames are considered as major feature. In the training data, consecutive frames of every imaging time series are extracted and concatenated in a specific way, so that the spatial contextual information could be embedded into a single two-dimensional image. This method enables the time series with obvious correlation between frames be directly trained on 2D convolutional neural network (CNN), instead of 3D-CNN with huge computational cost. Finally, we got an accuracy of 98.89%, with sensitivity and specificity of 99.38% and 98.19%, respectively, which proves the feasibility and availability of the SIF feature.

Mitochondrial damage in hippocampal neurons of rats with epileptic protein expression of Fas and caspase-3.

Epilepsy model in rats was established to observe the behavior and pathological changes, and to detect mitochondrial dysfunction, exploring its possible molecular mechanisms. The epileptic status of Sprague-Dawley (SD) rats was induced by intraperitoneal injection of lithium chloride, and the change of behavior was recorded. Electroencephalogram (EEG) was used to measure the abnormal discharge of neurons in rats. The brain tissue was fixed with polyformaldehyde and the paraffin sections were prepared, and the damage of the hippocampal neurons was observed with Nissl staining. Mitochondrial ATP and mitochondrial DNA were examined to assess mitochondrial dysfunction. Finally, qPCR and western blot analysis were used to detect mRNA and protein expression of fatty acid synthetase (Fas), Fas ligand (FasL) and caspase-3 in rat hippocampal neurons. The correlation between the mitochondrial dysfunction of rat hippocampal neurons and Fas and caspase-3 was analyzed. Compared with the normal group rats, the model group showed typical seizures, which were determined by the Racine attack score. EEG of the hippocampus of the model group was recorded in cluster in model group rats. Nissl staining showed a different degree of damage to the hippocampal neurons in the model group compared with normal rats. The mitochondrial ATP content and DNA content of rat hippocampal neurons in the model group were significantly lower than that of normal rats (P<0.01). The qPCR and western blot results showed that the mRNA and protein expression levels of Fas, FasL and caspase-3 were significantly increased in the hippocampus of rat model group (P<0.01). The expression level of Fas and caspase-3 in hippocampal tissues of rats was negatively correlated with mitochondrial DNA content. In conclusion, seizures cause damage of neuron mitochondria in rat hippocampus leading to death of hippocampal neurons, the mitochondrial damage of hippocampal neurons in epileptic rats was closely related to the expression of Fas and caspase-3.

Impact of a clinical pathway on hospital costs, length of stay and early outcomes after hepatectomy for hepatocellular carcinoma.

BACKGROUND: A clinical pathway (CP) can standardize and improve perioperative care for a number of interventions. In hepatic surgery, however, pertinent evidence is very limited. This study was conducted to implement a CP for hepatocellular carcinoma (HCC) patients undergoing hepatectomy, and to evaluate its effects on hospital costs, length of hospital stay (LOHS) and early clinical outcomes. MATERIALS AND METHODS: Medical records for HCC patients undergoing hepatectomy were retrospectively reviewed before implementation of a CP (the non-CP group) from March 2012 to August 2012. This information was compared with the data collected prospectively from patients after implementation of the CP (the CP group) between September 2012 and April 2013. Hospital costs, LOHS and early clinical outcomes were evaluated and compared between groups. RESULTS: There were no significant differences in terms of patient clinical characteristics between the two groups. For clinical outcome measures, no significant differences were found in postoperative complications, mortality and readmission rate. The hospital costs were significantly reduced from 24,844 RMB in the non-CP group to 19,761 RMB in the CP group (p<0.01). In addition, patients of the CP group also had shorter LOHS compared with the non-CP group (8.3 versus 12.3 days, p<0.001). CONCLUSIONS: The CP proved to be an effective approach to minimize hospital costs and LOHS with hepatectomy for HCC without compromising patient care.

Clinical and histopathological features of familial amyloidotic polyneuropathy with transthyretin Val30Ala in a Chinese family.

Familial amyloidotic polyneuropathy (FAP) is characterized by extracellular deposition of amyloid fibrils caused by a point mutation in the transthyretin (TTR) gene. TTR amyloidosis is linked to a vast number of mutations with varying phenotype and tissue distribution. Several Chinese kindred with FAP type 1 have been reported in Beijing, Hong Kong, Taiwan, and elsewhere. Here, histopathological features and TTR gene polymorphism were analyzed by using autopsy and blood specimens from a Chinese proband of a family with FAP. This proband is a 34-year old man with FAP type 1 who developed motor, sensory and autonomic impairments with neuropathy, gastrointestinal dysfunction, and orthostatic hypotension. Genetic findings of TTR revealed a T to C transition in codon 30 causing the mutation TTR Ala30. This patient died of respiratory and circulatory failure 7 years after onset. Autopsy showed heavy amyloid deposition in the peripheral nerves, liver, testes, thyroid, pancreas and muscles. There was moderate deposition in the heart, kidneys, bladder, gastrointestinal tract, tongue, lung, blood vessels, and gall bladder. The spleen showed only slight deposition, and none was observed in the central nervous system. TTR amyloidosis was confirmed by immunochemical staining with a specific TTR antibody. These results indicate that the distribution of amyloid deposition, (i.e., heavy in the liver, testes and slight in the spleen), is a characteristic feature and reflects the severity of FAP with TTR Val30Ala.

Hospital costs and length of hospital stay for hepatectomy in patients with hepatocellular carcinoma:results of a prospective case series.

BACKGROUND/AIMS: Hepatocellular carcinoma (HCC) has become an important health concern. However, data on its economic burden are limited. This study was undertaken to estimate the hospital costs and length of hospital stay (LOHS) for hepatectomy in patients with HCC, and identify the contributing factors to them. METHODOLOGY: A prospective case series observation was performed from January 2009 to December 2009 at two general hospitals in China. The information, such as demographic and clinical data, of HCC patients undergoing hepatectomy was recorded. Hospital costs and LOHS were analyzed to estimate the distribution of healthcare utilization. The multiple linear regression analysis was utilized to determine the impact of different demographic and clinical factors on costs and LOHS. RESULTS: A total of 220 patients were enrolled. The median hospital costs were $3453.7. The median LOHS was 13 days. The major fraction of the total costs was medication costs, accounting for 40.2%. LOHS was the key determinant of hospital costs. Blood transfusion, postoperative complication and Child-Pugh score were also independent but less contributory determinants. Additionally, predictors for prolonged LOHS included postoperative complication and surgical procedure. CONCLUSIONS: Hospital costs and LOHS for hepatectomy have their particular contributing factors. The results may be useful in resource allocation and cost-effectiveness analysis of intervention for HCC treatment.