Quantification of Blood Flow in Dialysis Access Using Custom-Acquisition Protocol and Imaging Methods: A Clinical Validation Study

PurposeTo determine access blood flow (ABF) rate using 2D image sequences acquired with digital subtraction angiography (DSA) and fluoroscopy.Materials and MethodsA total of 23 patients with known or suspected malfunctioning accesses were imaged using 2 filming modes: DSA at 3 or 6 frames/s (F/s), and fluoroscopy at 10 or 15 pulses/s (P/s). ABF rates were quantified using a bolus tracking method based on cross-correlation algorithm and compared with catheter-based thermal dilution (TD) flow measurements. The indicator-dilution curves were fitted with a gamma-variate (GV) curve fitting model to assess the effect on accuracy. Radiation doses were calculated to examine any increased susceptibility to tissue reactions and stochastic effects.ResultsFor DSA images, the absolute percent deviations (mean ± standard error of mean) in computed flow vs TD flow measurements at 3 F/s and 6 F/s were 34% ± 4.5% and 20% ± 4.7%, respectively, without curve fitting, and 31% ± 3.3% and 20% ± 4.1%, respectively, with curve fitting. For fluoroscopic images, the deviations at 10 P/s and 15 P/s were 44% ± 7.3% and 68% ± 10.7%, respectively, without curve fitting and 36% ± 6.4% and 48% ± 7.1%, respectively, with curve fitting. The mean peak skin dose and effective dose at 6 F/s were 3.28 mGy and 75 μSv, respectively.ConclusionsDigital subtraction angiography images obtained at 6 F/s offered the highest accuracy for dialysis access blood flow quantification.     

A Diagnostic Algorithm for Posterior Fossa Tumors in Children: A Validation Study

BACKGROUND AND PURPOSE:Primary posterior fossa tumors comprise a large group of neoplasias with variable aggressiveness and short and long-term outcomes. This study aimed to validate the clinical usefulness of a radiologic decision flow chart based on previously published neuroradiologic knowledge for the diagnosis of posterior fossa tumors in children.MATERIALS AND METHODS:A retrospective study was conducted (from January 2013 to October 2019) at 2 pediatric referral centers, Children''s Hospital of Philadelphia, United States, and Great Ormond Street Hospital, United Kingdom. Inclusion criteria were younger than 18 years of age and histologically and molecularly confirmed posterior fossa tumors. Subjects with no available preoperative MR imaging and tumors located primarily in the brain stem were excluded. Imaging characteristics of the tumors were evaluated following a predesigned, step-by-step flow chart. Agreement between readers was tested with the Cohen κ, and each diagnosis was analyzed for accuracy.RESULTS:A total of 148 cases were included, with a median age of 3.4 years (interquartile range, 2.1–6.1 years), and a male/female ratio of 1.24. The predesigned flow chart facilitated identification of pilocytic astrocytoma, ependymoma, and medulloblastoma sonic hedgehog tumors with high sensitivity and specificity. On the basis of the results, the flow chart was adjusted so that it would also be able to better discriminate atypical teratoid/rhabdoid tumors and medulloblastoma groups 3 or 4 (sensitivity = 75%–79%; specificity = 92%–99%). Moreover, our adjusted flow chart was useful in ruling out ependymoma, pilocytic astrocytomas, and medulloblastoma sonic hedgehog tumors.CONCLUSIONS:The modified flow chart offers a structured tool to aid in the adjunct diagnosis of pediatric posterior fossa tumors. Our results also establish a useful starting point for prospective clinical studies and for the development of automated algorithms, which may provide precise and adequate diagnostic tools for these tumors in clinical practice.

In the past 10 years, there has been an exponential increase in knowledge of the molecular characteristics of pediatric brain tumors, which was only partially incorporated in the 2016 World Health Organization Classification of Tumors of the Central Nervous System.¹ The main update in the 2016 Classification was the introduction of the molecular profile of a tumor as an important factor for predicting different biologic behaviors of entities which, on histology, look very similar or even indistinguishable.² A typical example is the 4 main groups of medulloblastoma: wingless (WNT), sonic hedgehog (SHH) with or without the p53 mutation, group 3, and group 4. Although they may appear similar on microscopy, these categories have distinct molecular profiles, epidemiology, prognosis, and embryologic origin.³Subsequent to the publication of the 2016 World Health Organization Classification, further studies have identified even more molecular subgroups of medulloblastoma with possible prognostic implications⁴ and also at least 3 new molecular subgroups of atypical teratoid/rhabdoid tumor (AT/RT)⁵ and several subgroups of ependymoma.⁶ MR imaging shows promise as a technique for differentiating histologic tumors and their molecular subgroups. This capability relies on not only various imaging characteristics but also the location and spatial extension of the tumor, evident on MR imaging, which can be traced to the embryologic origin of the neoplastic cells.^5,7-10One approach to the challenge of identifying imaging characteristics of different tumors in children is to use artificial intelligence. Yet despite this exciting innovation, correctly identifying the location of the mass and its possible use as an element for differential diagnosis still requires the expertise of an experienced radiologist. Previously, D''Arco et al¹¹ proposed a flow chart (Fig 1) for the differential diagnosis of posterior fossa tumors in children based on epidemiologic, imaging signal, and location characteristics of the neoplasm. The aims of the current study were the following: 1) to validate, in a retrospective, large cohort of posterior fossa tumors from 2 separate pediatric tertiary centers, the diagnostic accuracy of that flow chart, which visually represents the neuroadiologist''s mental process in making a diagnosis of posterior fossa tumors in children, 2) to describe particular types of posterior fossa lesions that are not correctly diagnosed by the initial flow chart, and 3) to provide an improved, clinically accessible flow chart based on the results.Open in a separate windowFIG 1.Predesigned radiologic flow chart created according to the literature before diagnostic accuracy analysis. The asterisk indicates brain stem tumors excluded from the analysis. Double asterisks indicate relative to gray matter. Modified with permission from D''Arco et al.¹¹     

Validation of a Hemodynamic Model for the Study of the Cerebral Venous Outflow System Using MR Imaging and Echo-Color Doppler Data

BACKGROUND AND PURPOSE:A comprehensive parameter model was developed to investigate correlations between cerebral hemodynamics and alterations in the extracranial venous circulation due to posture changes and/or extracranial venous obstruction (stenosis). The purpose of this work was to validate the simulation results by using MR imaging and echo-color Doppler experimental blood flow data in humans.MATERIALS AND METHODS:To validate the model outcomes, we used supine average arterial and venous extracerebral blood flow, obtained by using phase-contrast MR imaging from 49 individuals with stenosis in the acquisition plane at the level of the disc between the second and third vertebrae of the left internal jugular vein, 20 with stenosis in the acquisition plane at the level of the disc between the fifth and sixth vertebrae of the right internal jugular vein, and 38 healthy controls without stenosis. Average data from a second group of 10 healthy volunteers screened with an echo-color Doppler technique were used to evaluate flow variations due to posture change.RESULTS:There was excellent agreement between experimental and simulated supine flows. Every simulated CBF fell inside the standard error from the corresponding average experimental value, as well as most of the simulated extracerebral arterial flow (extracranial blood flow from the head and face, measured at the level of the disc between second and third vertebrae) and venous flows. Simulations of average jugular and vertebral blood flow variations due to a change of posture from supine to upright also matched the experimental data.CONCLUSIONS:The good agreement between simulated and experimental results means that the model can correctly reproduce the main factors affecting the extracranial circulation and could be used to study other types of stenotic conditions not represented by the experimental data.

Cerebral hemodynamics plays a key role in brain physiology.¹ The interest in understanding the hemodynamics of the brain arises from human brain function being critically dependent on the proper values of cerebral blood inflow and outflow.² Unfortunately, experimental access to cerebral circulation dynamics is limited.Within the complex problem of cerebral hemodynamics, the cerebral venous system plays an important role. Indeed, cranial and extracranial veins form an intricate network of vessels, stressed by complex phenomena involving postural changes and the gravity field, which affect the dynamics of circulating blood.² In particular, the internal jugular vein (IJV), which is the dominant outflow vein from the brain,³ is a collapsible vessel characterized by marked changes in its cross-sectional area, depending on transmural pressure on the vessel wall.^4,5 Section changes, in turn, affect its conductance. The overall phenomenon is influenced by the hydrostatic pressure gradient during the transition from the supine to sitting position.^6,7Due to the plethora of biophysical factors affecting brain circulation, it is difficult to gain an accurate quantitative understanding of its behavior and of the clinical implications of its alteration.Recently, we developed a comprehensive lumped parameter model that links intracranial hemodynamics and the cerebral venous outflow system.⁸ Its aim is to simulate the cranial and extracranial vessel pathway behavior and the mechanisms involved in the drainage process and to link them with the intracranial circulation and the action of cerebrovascular regulation mechanisms. The model represents a new tool for improving our understanding of this complex system.The aim of this work was to provide a validation of the model, by using in vivo measurements performed in both healthy subjects and individuals with venous obstruction. We illustrate how the model parameters can be tuned to reproduce MR imaging and echo-color Doppler (ECD) data of average blood flow. With this model, we can simulate some important phenomena affecting the extracranial venous system, such as the posture change or the presence of jugular obstructions (stenosis).^9,10 We took advantage of the availability of both MR imaging and ECD experimental data of blood flow to exploit the different advantages they provide. MR imaging includes phase-contrast imaging for flow quantification, along with 2D (TOF) MRV for anatomic assessment.^11–13 MR imaging–based techniques allow the inclusion of information about minor vessels besides the common carotid, internal carotid, and vertebral arteries; IJVs; and vertebral veins. The ECD technique,¹⁴ even if not useful for detecting minor routes, is a cheaper and faster methodology, suitable for measurement of blood flow in both the supine and upright conditions. We used ECD to obtain information about the percentage variation of average flows due to a change of posture.     

Fully Automatic Coronary Calcium Score Software Empowered by Artificial Intelligence Technology: Validation Study Using Three CT Cohorts

ObjectiveThis study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard.Materials and MethodsWe developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1–10, 11–100, 101–400, > 400) was evaluated.ResultsIn 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions).ConclusionThe atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.     

斜坡滑动撞击建立兔轻型脑损伤模型及功能MRI实验研究

目的建立一个控制性与重复性好的脑损伤动物模型,并应用MRI分子影像方法检查轻型脑损伤的病理生理微观改变。材料与方法自制斜坡滑动装置,建立兔轻型脑损伤模型。对实验兔在撞击前、撞击后即刻和安静休息1h后分别进行CT平扫和MRI T1WI、T2WI、FLAIR、SWI、MRS、DTI(FA值)、DWI(ADC值)检查。根据兔脑受撞击后意识表现、CT检查和MRI T1WI、T2WI、FLAIR、SWI检查阴性,诊断兔轻型脑损伤。对比实验兔撞击前后的MRS、DTI、DWI检查结果,分析兔轻型脑损伤的异常功能表现。结果兔脑受轻微撞击后,NAA、Cho、Cr及Cr2峰值均立即显著降低,1h后检查又基本恢复到撞击前水平。脑干DTI的FA值在撞击后也立即显著降低,1h后检查基本恢复到撞击前水平。而脑干DWI的ADC值和端脑的FA值及ADC值在撞击前后并没有显著改变。结论本实验方法可以建立符合要求的兔轻型脑损伤模型,功能MRI检测表明轻型脑损伤主要是脑细胞代谢障碍引起的生理改变,这种功能性改变可以恢复。     

Clinical Feasibility of Synthetic MRI in Multiple Sclerosis: A Diagnostic and Volumetric Validation Study

BACKGROUND AND PURPOSE:Quantitative MR imaging techniques are gaining interest as methods of reducing acquisition times while additionally providing robust measurements. This study aimed to implement a synthetic MR imaging method on a new scanner type and to compare its diagnostic accuracy and volumetry with conventional MR imaging in patients with MS and controls.MATERIALS AND METHODS:Twenty patients with MS and 20 healthy controls were enrolled after ethics approval and written informed consent. Synthetic MR imaging was implemented on a Siemens 3T scanner. Comparable conventional and synthetic proton-density–, T1-, and T2-weighted, and FLAIR images were acquired. Diagnostic accuracy, lesion detection, and artifacts were assessed by blinded neuroradiologic evaluation, and contrast-to-noise ratios, by manual tracing. Volumetry was performed with synthetic MR imaging, FreeSurfer, FMRIB Software Library, and Statistical Parametric Mapping. Repeatability was quantified by using the coefficient of variance.RESULTS:Synthetic proton-density–, T1-, and T2-weighted images were of sufficient or good quality and were acquired in 7% less time than with conventional MR imaging. Synthetic FLAIR images were degraded by artifacts. Lesion counts and volumes were higher in synthetic MR imaging due to differences in the contrast of dirty-appearing WM but did not affect the radiologic diagnostic classification or lesion topography (P = .50–.77). Synthetic MR imaging provided segmentations with the shortest processing time (16 seconds) and the lowest repeatability error for brain volume (0.14%), intracranial volume (0.12%), brain parenchymal fraction (0.14%), and GM fraction (0.56%).CONCLUSIONS:Synthetic MR imaging can be an alternative to conventional MR imaging for generating diagnostic proton-density–, T1-, and T2-weighted images in patients with MS and controls while additionally delivering fast and robust volumetric measurements suitable for MS studies.

In conventional MR imaging, multiple sequences with different contrast weightings are obtained. This process is time-consuming with redundant data acquisition. Techniques such as MR fingerprinting and synthetic MR imaging can reduce acquisition times and thereby increase MR imaging availability for both clinical applications and research.^1–3 SyMRI is a synthetic MR imaging method based on a quantitative approach in which a single saturation recovery TSE sequence is used to estimate absolute physical properties, the proton density (PD), longitudinal relaxation rate, and transverse relaxation rate, including correction for B₁-inhomogeneities. Rather than predetermining acquisition parameters such as TE, TI, and TR to maximize tissue contrast,³ synthetic MR imaging produces a free range of synthetic weightings based on a single sequence through mathematic inference.^4,5 The quantitative nature of the method and its ability to probe multiple physical properties in a single sequence make it suitable for volumetric analysis.^6–10 Synthetic MR imaging has shown promising initial results for use in MS and patients with an ischemic event.^11,12 The technique is consequently gaining interest as a potentially time-efficient alternative to conventional MR imaging to visualize and quantify brain tissue properties.MS is a chronic neuroinflammatory disorder affecting 2.5 million people globally.¹³ MS has a heterogeneous clinical expression, which complicates the choice of disease-modifying therapy.¹⁴ MR imaging is a cornerstone for the diagnosis and monitoring of MS, but qualitative MR imaging measurements are poorly correlated with the clinical outcome. Volumetric MR imaging measurements have an independent predictive value in MS but require laborious image postprocessing, limiting its clinical potential.^15,16 Robust and fully automatic volumetry approved for clinical use would thus be important for clinical care and research purposes. The synthetic MR imaging technique has initially been developed for use on Philips (Best, the Netherlands) and GE Healthcare (Milwaukee, Wisconsin) MR imaging systems, but it is not available for other systems and independent evaluations of the method are scarce.^5,6The purpose of this study was to implement the synthetic MR imaging technique for use on Siemens (Erlangen, Germany) MR imaging scanners and to compare the diagnostic accuracy of synthetic and conventional images in MS. A secondary aim was to test the repeatability of the volumetric synthetic MR imaging measurements and compare the volumetric results and practicality with other commonly used brain volumetric methods.     

应用荧光探针H2DCF-DA检测光照致细胞内活性氧产生的初步研究

目的应用荧光显微镜及新型荧光探针H2DCF-DA检测在光源照射过程中人脐静脉血管内皮细胞(ECV304)内活性氧的产生情况。方法将ECV304细胞接种于35mm培养皿中,24h后加入H2DCF-DA,使其终浓度为10μmol/L,孵育30min。利用荧光显微镜的激发光源作为照射光源,在采集荧光图像的同时完成光源照射,光源输出波长范围为460～490nm,功率密度约为100mW/cm2。连续采集DCF的荧光图,观察细胞内DCF荧光的变化情况,采用计算机图像处理和分析技术,求得细胞内不同照射时间点DCF平均荧光强度,进而得到平均荧光强度-时间曲线。另外,使用线粒体特异标记探针MitoFluorRed589与H2DCF-DA共同孵育细胞,分别采集同一细胞的DCF的荧光图和MitoFluorRed589的荧光图,并利用像素-荧光强度分析方法来确定DCF荧光在细胞内的分布位置。结果在光源照射的开始阶段,ECV304细胞内的DCF荧光强度迅速增加,在第10s时便上升至第2s时的2.2倍,但是随着照射时间逐渐延长,荧光强度增幅逐渐变小,到第60s时上升至第2s时的4.7倍。观察时间进一步延长,DCF的荧光强度的变化似乎进入平台期,继而开始出现下降。考察DCF荧光在ECV304细胞内的分布位置主要通过比较细胞内不同区域的I1/I2值,通过图像分析与计算得到线粒体区、细胞核区以及细胞质非线粒体区内I1/I2值分     

Carotid Plaque Characterization Using 3D T1-Weighted MR Imaging with Histopathologic Validation: A Comparison with 2D Technique

BACKGROUND AND PURPOSE:3D FSE T1WI has recently been used for carotid plaque imaging, given the potential advantages in contrast and spatial resolutions. However, its diagnostic performance remains unclear. Hence, we compared the ability of this technique to readily assess plaque characteristics with that of conventional images and validated the results with histologic classification.MATERIALS AND METHODS:We prospectively examined 34 patients with carotid stenosis who underwent carotid endarterectomy by using 1.5T scanners and obtained 3D-FSE T1WI and 2D spin-echo T1WI scans. After generating reformatted images obtained from the 3D-FSE T1-weighted images, we calculated the contrast ratios for the plaques and the adjacent muscles and compared these findings with the pathologic classifications.RESULTS:Carotid plaques were histologically classified as types VII, VIII, IV–V, or VI. With 3D-FSE T1WI, the range of contrast ratios for each classification was the following: 0.94–0.97 (median, 0.95), 0.95–1.29 (median, 1.10), 1.33–1.54 (median, 1.42), and 1.53–2.12 (median, 1.80), respectively. With 2D imaging, the range of contrast ratios for each classification was the following: 0.79–1.02 (median, 0.90), 0.88–1.19 (median, 1.01), 1.17–1.46 (median, 1.23), and 1.55–2.51 (median, 2.07), respectively. Results were significantly different among the 4 groups (P < .001). Sensitivity and specificity for discriminating vulnerable plaques (IV–VI) from stable plaques (VII, VIII) were both 100% for the 3D technique and 100% and 91%, respectively, for the 2D technique.CONCLUSIONS:3D-FSE T1WI accurately characterizes intraplaque components of the carotid artery, with excellent sensitivity and specificity compared with those of 2D-T1WI.

Cervical carotid stenosis is an important cause of cerebral infarction and transient ischemic attack. Carotid endarterectomy or carotid artery stent placement is performed to prevent future stroke events but may also cause embolic complications during the surgery, especially if the plaque contains substantial vulnerable components such as intraplaque hemorrhage or lipid.^1,2 Therefore, establishing a method for characterizing intraplaque components is an important prerequisite for predicting perisurgical complications.Several modalities have been used for plaque characterization, including ultrasonography and MR imaging. Although ultrasonography is widely used, the interpretation is typically subjective and may be impossible in the presence of extensive calcification or a high-positioned carotid bifurcation. Although gray-scale median and integrated backscatter have been introduced as quantitative metrics, previous reports suggest that they are unsuitable for evaluating intraplaque components.^3,4 MR plaque imaging is another popular method for assessing plaque characteristics. Although various imaging techniques have been used, a 2D spin-echo (SE) T1WI technique with appropriate scanning parameters has been reported to accurately quantify intraplaque components, compared with other conventional techniques.^5–8 Recently, a 3D T1WI FSE technique has been adopted for this purpose because it can minimize partial volume effects and motion artifacts, as well as enhance black-blood effects, while maintaining T1WI contrast. However, whether the 3D-FSE technique can more accurately discriminate among intraplaque components than the more conventional techniques, such as 2D-SE T1WI, remains unknown. Hence, in the present study, we investigated whether the diagnostic accuracy of 3D-FSE T1WI, in terms of carotid plaque characterization, is comparable with that of 2D-SE T1WI, by using pathologic specimens excised from carotid endarterectomy as our validation standards.     

Differentiation between Cystic Pituitary Adenomas and Rathke Cleft Cysts: A Diagnostic Model Using MRI

BACKGROUND AND PURPOSE:Cystic pituitary adenomas may mimic Rathke cleft cysts when there is no solid enhancing component found on MR imaging, and preoperative differentiation may enable a more appropriate selection of treatment strategies. We investigated the diagnostic potential of MR imaging features to differentiate cystic pituitary adenomas from Rathke cleft cysts and to develop a diagnostic model.MATERIALS AND METHODS:This retrospective study included 54 patients with a cystic pituitary adenoma (40 women; mean age, 37.7 years) and 28 with a Rathke cleft cyst (18 women; mean age, 31.5 years) who underwent MR imaging followed by surgery. The following imaging features were assessed: the presence or absence of a fluid-fluid level, a hypointense rim on T2-weighted images, septation, an off-midline location, the presence or absence of an intracystic nodule, size change, and signal change. On the basis of the results of logistic regression analysis, a diagnostic tree model was developed to differentiate between cystic pituitary adenomas and Rathke cleft cysts. External validation was performed for an additional 16 patients with a cystic pituitary adenoma and 8 patients with a Rathke cleft cyst.RESULTS:The presence of a fluid-fluid level, a hypointense rim on T2-weighted images, septation, and an off-midline location were more common with pituitary adenomas, whereas the presence of an intracystic nodule was more common with Rathke cleft cysts. Multiple logistic regression analysis showed that cystic pituitary adenomas and Rathke cleft cysts can be distinguished on the basis of the presence of a fluid-fluid level, septation, an off-midline location, and the presence of an intracystic nodule (P = .006, .032, .001, and .023, respectively). Among 24 patients in the external validation population, 22 were classified correctly on the basis of the diagnostic tree model used in this study.CONCLUSIONS:A systematic approach using this diagnostic tree model can be helpful in distinguishing cystic pituitary adenomas from Rathke cleft cysts.

Pituitary adenoma is a benign neoplasm that arises from the adenohypophysis and is the most common intrasellar pathology, accounting for 10%–15% of all intracranial neoplasms.^1,2 Typical imaging findings of an uncomplicated pituitary adenoma include slow enhancement compared with that of the pituitary gland, lateral deviation of the infundibulum, and isointense signal intensity relative to gray matter on T1-weighted imaging.³ Intratumoral hemorrhage and ischemic infarction are common with larger pituitary adenomas, which may result in hemorrhagic or cystic changes or both, leading to various signal intensities on MR imaging.^4–8Rathke cleft cyst (RCC) is a benign epithelial cyst believed to originate from the remnants of the Rathke pouch.⁹ Typical imaging findings include a nonenhancing, noncalcified, intrasellar/suprasellar cyst with an intracystic nodule.^9–12 Depending on its cystic content and the presence of an associated intracystic nodule, an RCC may show various signal intensities on both T1- and T2-weighted images.^13–15 More specifically, T1 hyperintensity and T2 hypointensity of an RCC associated with a high intracystic protein content can mimic cystic pituitary adenoma with hemorrhage, which makes imaging diagnosis of a cystic pituitary adenoma or an RCC a challenge.Preoperative differentiation between a cystic pituitary adenoma and an RCC is important for treatment planning.^16–18 Partial resection of the wall and evacuation of cyst contents are sufficient for an RCC, whereas a cystic pituitary adenoma may require total resection, not only to relieve mass effect but also to correct hormone excess.^9,19–21 Unnecessary surgical excision of an RCC may lead to serious complications, such as CSF leaks, infection, and hypothalamic injury, though the incidences thereof are very low.^21,22 Thus, obtaining the correct preoperative diagnosis with which to determine the proper surgical indication and to plan the optimal surgical procedure is a major concern for neurosurgeons.⁹ To date, several characteristic MR imaging appearances of pituitary adenomas and RCCs have been reported,^{2,9,12,20,23–25} but there are some cases for which the diagnoses are inconclusive when 1 or 2 imaging findings are used, and none of the studies has systemically analyzed the MR imaging appearances of cystic pituitary adenomas to differentiate them from RCCs. Therefore, we evaluated the diagnostic potential of a multifactor analysis of MR imaging findings and developed a diagnostic tree model to increase the diagnostic accuracy in differentiating cystic pituitary adenomas and RCCs before surgery.     

CTA对星形细胞肿瘤血管定量评价的初步研究

目的评价CT血管成像(CTA)星形细胞肿瘤血管指数(tumor vessel index,TVI)与微血管密度(MVD)之间的相关性及其对星形细胞肿瘤分级的敏感性和特异性。资料与方法选择我院神经外科53例脑肿瘤患者行CTA检查,经手术病理学证实为星形细胞肿瘤者30例纳入本研究。采用GE LightSpeed 64层螺旋CT机进行CTA,在ADW 4.2后处理工作站重组肿瘤血管,并测定血管密集区和对侧正常组织的血管指数。手术获取脑肿瘤标本,进行组织病理学分级和MVD测定。结果高、低级别星形细胞肿瘤的TVI均显著高于对应正常侧脑组织(P<0.01),高级别组TVI显著高于低级别组(P<0.01),TVI与MVD具有显著等级相关(rs=0.805,P<0.01)。受试者工作特征(ROC)曲线分析表明,TVI对鉴别高、低级别星形细胞肿瘤的ROC曲线下面积为0.928(P<0.01),采用TVI=48.295%作为分界点对鉴别高、低级别星形细胞肿瘤的敏感性和特异性分别为84.2%和91.9%。结论CTA可用于评价星形细胞肿瘤新生血管的形态特征,CTA TVI对鉴别高、低级别星形细胞肿瘤具有较高的敏感性和特异性。     

Stent-Protected Carotid Angioplasty Using a Membrane Stent: A Comparative Cadaver Study

Purpose To evaluate the performance of a prototype membrane stent, MembraX, in the prevention of acute and late embolization and to quantify particle embolization during carotid stent placement in human carotid explants in a proof of concept study. Methods Thirty human carotid cadaveric explants (mild stenoses 0–29%, n = 23; moderate stenoses 30–69%, n = 3; severe stenoses 70–99%, n = 2) that included the common, internal and external carotid arteries were integrated into a pulsatile-flow model. Three groups were formed according to the age of the donors (mean 58.8 years; sample SD 15.99 years) and randomized to three test groups: (I) MembraX, n = 9; (II) Xpert bare stent, n = 10; (III) Xpert bare stent with Emboshield protection device, n = 9. Emboli liberated during stent deployment (step A), post-dilatation (step B), and late embolization (step C) were measured in 100 μm effluent filters. When the Emboshield was used, embolus penetration was measured during placement (step D) and retrieval (step E). Late embolization was simulated by compressing the area of the stented vessel five times. Results Absolute numbers of particles (median; >100 μm) caught in the effluent filter were: (I) MembraX: A = 7, B = 9, C = 3; (II) bare stent: A = 6.5, B = 6, C = 4.5; (III) bare stent and Emboshield: A = 7, B = 7, C.=.5, D = 8, E = 10. The data showed no statistical differences according to whether embolic load was analyzed by weight or mean particle size. When summing all procedural steps, the Emboshield caused the greatest load by weight (p = 0.011) and the largest number (p = 0.054) of particles. Conclusions On the basis of these limited data neither a membrane stent nor a protection device showed significant advantages during ex vivo carotid angioplasty. However, the membrane stent seems to have the potential for reducing the emboli responsible for supposed late embolization, whereas more emboli were observed when using a protection device. Further studies are necessary and warranted.     

微泡介导下诊断超声开放人血脑屏障的可行性实验研究

目的:探讨微泡介导下诊断超声波开放人血脑屏障的可行性。材料和方法:①用青年人颞骨代替兔顶骨,制作模拟超声波经人颅骨的动物模型;②经兔耳缘静脉匀速注射微泡(0.5ml/kg)时,诊断超声波经人颅骨连续辐照兔脑10min。立即用伊文思蓝(Evens blue,EB)示踪法评价靶区血脑屏障的开放情况,并观察有无神经细胞的损伤。结果:微泡介导下诊断超声波经青年人颅骨能够促EB跨兔血脑屏障进入脑组织,且不导致神经细胞的损伤。结论:微泡介导下诊断超声波经青年人颅骨能够安全、有效地开放兔血脑屏障,表明微泡介导下诊断超声开放人血脑屏障具有可行性,而且相对安全。     

Development and Validation of a Deep Learning–Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images

BACKGROUND AND PURPOSE:Differentiating glioblastoma from solitary brain metastasis preoperatively using conventional MR images is challenging. Deep learning models have shown promise in performing classification tasks. The diagnostic performance of a deep learning–based model in discriminating glioblastoma from solitary brain metastasis using preoperative conventional MR images was evaluated.MATERIALS AND METHODS:Records of 598 patients with histologically confirmed glioblastoma or solitary brain metastasis at our institution between February 2006 and December 2017 were retrospectively reviewed. Preoperative contrast-enhanced T1WI and T2WI were preprocessed and roughly segmented with rectangular regions of interest. A deep neural network was trained and validated using MR images from 498 patients. The MR images of the remaining 100 were used as an internal test set. An additional 143 patients from another tertiary hospital were used as an external test set. The classifications of ResNet-50 and 2 neuroradiologists were compared for their accuracy, precision, recall, F1 score, and area under the curve.RESULTS:The areas under the curve of ResNet-50 were 0.889 and 0.835 in the internal and external test sets, respectively. The area under the curve of neuroradiologists 1 and 2 were 0.889 and 0.768 in the internal test set and 0.857 and 0.708 in the external test set, respectively.CONCLUSIONS:A deep learning–based model may be a supportive tool for preoperative discrimination between glioblastoma and solitary brain metastasis using conventional MR images.

Glioblastoma (GBM) and brain metastases are the most common malignant tumors in adults.¹ These 2 entities have different treatment options, and it is therefore essential to distinguish them promptly to determine the proper treatment strategy. In patients with a history of underlying malignancy and conventional MR imaging findings of multiple enhancing lesions, a diagnosis can be made easily. However, approximately 25%–30% of brain metastases present as single lesions, and in lung cancer—the most common cancer to metastasize to the brain—approximately 50% of patients are thought to have brain metastases as the initial presentation.^2,3 In addition, GBM and solitary brain metastasis have overlapping MR imaging features, including rim enhancement with perilesional T2 hyperintensity, and are thus difficult to differentiate preoperatively.⁴ However, GBM has an infiltrative growth pattern; therefore, tumor cells diffusely infiltrate beyond the enhancing portion, manifesting as a perilesional T2 hyperintense region. Brain metastases have similar MR imaging features; however, this perilesional T2 hyperintensity is primarily due to vasogenic edema caused by the leaky capillary vessels of the enhancing tumor.^5,6 In an effort to detect these microstructural differences, various advanced MR imaging techniques, such as perfusion MR imaging, MR spectroscopy, and diffusion tensor imaging, have been applied to distinguish GBM from solitary brain metastasis, with particular emphasis on the aforementioned perilesional T2 hyperintense region.^7-10 Collectively, these studies have shown promising results indicating that the perilesional T2 hyperintense region, along with the enhancing portion itself, carries valuable information that may preoperatively distinguish these 2 entities. However, advanced imaging techniques require additional scanning time, and their quantitative values can vary depending on the imaging parameters, posing difficult challenges for practical application.Recently, radiomics have been used to analyze various textural and handcrafted features to classify or predict prognosis of disease through medical images that are beyond the perception of human eye.^11,12 However, radiomics needs careful preprocessing steps, including delicate segmentation. Deep learning—a subfield in machine learning—extracts information directly from the data, omitting the step of manual feature extraction in decision making.¹³ In the field of neuro-oncology, specifically glioma imaging, previous studies have shown the potential of deep learning for classifying gliomas based on genetic mutations or clinical outcomes.^14-16In this study, we hypothesized that deep learning may differentiate GBM from solitary brain metastasis without extraction of predefined features. Thus, we aimed to develop a deep learning–based model to differentiate GBM from solitary brain metastasis using preoperative T2-weighted and contrast-enhanced (CE) T1-weighted MR images and further validate its diagnostic performance.     

Development and Preliminary Validation of the Patient-Reported Outcome Measure for Vascular Malformation Questionnaire: A Prospective Cohort Study

PurposeTo develop and validate the Patient-Reported Outcome Measure for Vascular Malformation (PROVAM) questionnaire to assess the health-related quality of life in patients with vascular malformations.Materials and MethodsWe developed and validated PROVAM using a mixed methods design during a prospective clinical trial at a vascular anomalies clinic. From July 2019 to February 2020, 108 consecutive patients completed 130 questionnaires. The 30-item instrument assessed the domains of pain, emotional/social well-being, functional impact, and treatment satisfaction. Two additional items assessed ease of understanding and relevance. The primary outcomes of instrument reliability and validity were evaluated across several indices. The secondary outcome of responsiveness evaluated total score changes for patients who completed questionnaires both before and after treatment.ResultsInstrument reliability, as measured by Cronbach alpha, was ≥0.79 for pain, emotional/social well-being, and functional impact domains. Primary domain structure was confirmed by factor analysis (P <. 001) and convergent construct validity for all but 1 Likert scale item. In the subgroup analysis of 13 participants who completed PROVAM before and after treatment, instrument responsiveness, as measured by the total score, showed a significant decrease (median, ?10 points; interquartile range [IQR], ?3 to ?16; P = .04). Participants found the questions easy to understand (median, 5 points; IQR, 4–5 on a 5-point scale) and relevant (median score, 4; IQR, 3–5).ConclusionsPreliminary data support the reliability and validity of PROVAM in measuring the health-related quality of life in patients with vascular malformations.     

光影响生物节律的研究进展

生物体的各种行为、生理过程都呈现昼夜节律变化,这种生物节律受到生物钟的调控,可在没有外界信号刺激时自主振荡。光不仅用于视网膜成像,还能影响生物节律。光通过视网膜-下丘脑通路调节生物钟,使生物节律与外界环境的明暗周期同步。研究表明光引发的生物节律紊乱与认知和情感障碍、糖尿病、肥胖症及某些肿瘤的发病率增加有关,通过光照调节生物节律为这些疾病的防治提供了新思路。本文介绍了生物节律系统的生理特点,总结了异常光照引发的生物节律紊乱相关疾病的最新研究进展。     

Vein Graft-Coated Vascular Stents: A Feasibility Study in a Canine Model

Purpose: To evaluate different vein grafts for luminal coating of endovascular stents in normal canine arteries. Methods: Twenty-four tantalum Strecker stents were coated with either autologous (n= 10), denatured heterologous (n= 11), or denatured homologous vein grafts (n= 3). The carotid artery (n= 11) and the iliac artery (n= 13) were stented using a transfemoral approach. Angiograms were performed at days 0, 7, and 21, and months 3, 6, and 9. All grafts underwent histological examination. Results: Eight of 10 autologous vein grafts showed patency during the whole observation period of 9 months, without histological signs of inflammation. Denatured heterologous vein grafts revealed acute (n= 3), subacute (n= 5), or delayed (n= 3) vessel occlusion. Hyaloid transformation of the vein graft and lympho-plasmacellular formations were seen. Denatured homologous vein grafts showed acute vessel occlusion. Although significant inflammatory tissue response was seen, no host-versus-graft reaction was present. Conclusion: Autologous vein graft-coated stents showed good biocompatibility in canine arteries. Preparation was cumbersome and required surgical venae-sectio. Denatured vein grafts, however, were limited by inflammatory reactions.