Objective:To compare the effect of middle meningeal artery embolization (MMAE) versus conventional therapy for chronic subdural hematoma (CSDH).Methods:Retrospective analysis of 38 patients with 48 CSDHs treated with MMAE from May 2019 to May 2021 was performed. Comparisons were made with a conventional treatment for 126 patients with 126 CSDHs from January 2016 to May 2021. The MMAE and conventional treatment patients were matched by the propensity score matching method, and a total of 25 pairs of patients (31 pairs of CSDHs) were successfully matched. The CSDH recurrence, rescue treatment, radiographic follow-up outcome, clinical improvement and complication between the two groups were compared by t test, χ 2 test or Fisher exact probability methods. Results:The rescue treatment rate in MMAE group was significantly lower than that in conventional treatment group [0 (0/31) vs 19.4% (6/31), P=0.024] and the complete resolution rate at 6 months follow-up in MMAE group was significantly higher than that in conventional treatment group [96.8 (30/31) vs 74.2% (23/31), P=0.026]. In terms of CSDH recurrence, there was a trend of lower recurrence in the MMAE group [3.2%(1/31) vs 22.6% (7/31), P=0.053]. The complete resolution rate at 3 months follow-up was 61.3% (19/31) in MMAE group and 45.2% (14/31) in conventional treatment, clinical improvement rate was 92.0% (23/25) in MMAE group and 88.0% (22/23) in conventional treatment, good outcome rate (mRS≤2) was 92.0% (23/25) in MMAE group and 84.0% (21/25) in conventional treatment, complication rate was 0(0/25) in MMAE group and 4.0% (1/25) in conventional treatment, and there were no significant differences in all above-mentioned parameters ( P>0.05). Conclusions:The MMAE may be considered as a safe and effective treatment for CSDH, and MMAE for CSDH is associated with lower trend of recurrence, lower rescue treatment rate and better radiographic follow-up outcome than conventional therapy.

Infectious intracranial aneurysm (IIA) is a kind of rare infectious neurovascular disease. The most common cause of IIA is bacterial embolism after acute or subacute infective endocarditis. The most common clinical symptoms of IIA include subarachnoid hemorrhage or intracranial hematoma resulted from aneurysm rupture, which has high disability and mortality rate. The treatment options for IIA mainly include anti-infective therapy, surgical treatment and endovascular interventional therapy. However, due to pathogenesis differences and aneurysm size, shape, location and rupture, the treatment plans for IIA patients need to be implemented individually. This article mainly focuses on the research progress of the different treatments of IIA in order to provide references for IIA clinical treatment.

Infectious intracranial aneurysms (IIA) is a rare infectious neurovascular disease. The most common clinical manifestation of IIA is subarachnoid hemorrhage or intracranial hematoma caused by aneurysm rupture, which has a high morbidity and mortality. For unruptured IIA, the clinical manifestations are hidden and the diagnosis is more difficult. This article mainly reviews the research progress of diagnosis and treatment of IIA, in order to provide reference for the diagnosis and treatment of IIA.

Objective:To explore the lesion characteristics and predictors of invasive coronary angiography (ICA)-verified obstructive lesions with fractional flow reserve (FFR)>0.80, that is, anatomy-function mismatch.Methods:A total of 515 obstructive vessels in 419 coronary disease patients from 11 Chinese medical centers undergoing coronary CT angiography and ICA and FFR were retrospectively analyzed. All vessels had one target lesion with diameter stenosis ≥50 % by ICA. There were 229 vessels in the match group (FFR≤0.80) and 286 vessels in the mismatch group (FFR>0.80). The lesion characteristics including lesion territory, the distance of the coronary artery ostium to the proximal end of the lesion, minimum lumen area, reference lumen area, plaque length and burden, plaque volume and component volume, remodeling index and plaque morphological complexity were measured and compared between the two groups. Optimal thresholds of quantitative plaque characteristics were defined by Yoden index. Logistic regression analysis was used to analyze the predictors of anatomy-function mismatch. Area under receiver operating characteristic curve (AUC) was used to analyze the ability of different lesion features to predict mismatched lesions.Results:The coronary stenosis, plaque burden and length, plaque volume (including each component volume) in the mismatch group were smaller than those in the match group, and FFR, minimum lumen area were larger (all P<0.05). Left anterior descending artery (LAD) lesion and severe complex plaque were more common in the match group than the mismatch group with a statistically significant difference. Univariate logistic regression analysis showed that LAD lesion, minimum lumen area>4 mm 2, plaque burden and length, plaque calcification volume<27 mm 3, plaque lipid volume<30 mm 3, plaque fiber volume<150 mm 3 and plaque morphological complexity were predictiors of anatomic function mismatched lesions; Multivariate logistic regression showed that the minimum lumen area>4 mm 2 (OR=3.371, 95%CI 1.903-5.973, P<0.001), plaque lipid volume<30 mm 3 (OR=3.014, 95%CI 1.691-5.373, P<0.001), plaque morphological complexity (mild OR=17.772, 95%CI 8.072-39.128, P<0.001, moderate OR=6.383, 95%CI 3.739-10.896, P<0.001) were independent predictors of mismatched lesions. The AUC of the model based on the minimum lumen area, plaque lipid volume and morphological complexity was 0.824, which was superior to either of the plaque feature alone ( P<0.001). Conclusions:The minimum lumen area, lipid volume and plaque morphological complexity are independent predictors of the anatomical-functional mismatch lesions, and the combination can significantly improve the prediction value.

Objective:To explore the value and significance of sensorimotor cortex (SMC) in the recovery of upper limb motor function after cerebral infarction in the striatum with blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI).Methods:A total of 17 patients with primary onset of striatal intracapsular infarction (SCI) with unilateral severe upper limb paralysis, who were strictly screened from the Department of Neurology, Affiliated Hospital of Yangzhou University from June 2015 to December 2017, were selected as research subjects, and 15 healthy volunteers were selected as controls. BOLD-fMRI under the passive finger extension (FE) task on the hemiplegic side was performed within one week, one month and three months after the onset of the disease. The activation of SMC was observed by SPM8 software. The activation of corresponding brain activation areas in BOLD-fMRI was observed by Xjview software and compared with the standard brain activation areas dynamically. The upper limb section of the Fugl-Meyer Scale (FM-UL) was used to track the motor function of the upper limb. The upper limb motor function of the selected patients was evaluated before functional magnetic resonance imaging (fMRI) scanning, at one month and three months after onset of the disease.Results:In the controls, fMRI showed that the main brain activation areas were located in the contralateral SMC and bilateral supplementary motor area. According to the activation time course of the affected side SMC and the comparison results with the standard brain activation area, the study patients were divided into three groups: group 1 (six patients), in which the activation intensity of SMC was stronger than that of standard brain activation area in the early stage of onset; group 2 (five patients), in which the activation intensity of SMC in the affected side was stronger than that in the standard brain activation area at one month after onset; group 3 (six patients), in which the activation intensity of SMC in the affected side increased gradually in three months, but still did not exceed the standard brain activation area. The activated voxel values of the affected side SMC in group 1 patients at the first time, one month and three months were 3 570.2±1 125.9, 1 205.8±328.2 and 1 121.5±407.5, respectively, the difference within the group being statistically significant ( F=12.8, P=0.001); the activated voxel values of the affected SMC in group 2 patients were 556.2±171.7, 648.6±177.3 and 993.2±182.9, respectively, and the differences within the group were statistically significant ( F=6.5, P=0.018); the activated SMC values of the affected SMC in group 3 patients were 520.0±375.9, 573.5±375.0 and 680.9±359.8, respectively, and there was no statistically significant difference within the group ( P>0.05). The three times FM-UL scores corresponding to group 1 patients were (10.0±3.3) points, (52.3±4.6) points and (63.7±2.9) points; the three times FM-UL scores corresponding to group 2 patients were (10.6±5.7) points, (36.6±2.4) points and (59.2±3.1) points; and the three times FM-UL scores corresponding to group 3 patients were (9.2±4.0) points, (12.5±3.0) points and (13.3±5.0) points; FM-UL scores in group 1 and group 2 patients showed statistically significant differences within the groups ( F=348.4, 183.6; P<0.001), whereas that in group 3 patients showed no statistically significant difference within the group ( P>0.05). There was no statistically significant difference in the initial FM-UL score among the groups ( P>0.05), while the differences among the groups at one month and three months were statistically significant ( F=191.7,304.5; P<0.001). Conclusions:The survival of SMC on the affected side after cerebral infarction is a prerequisite for the rehabilitation of limb motor function. Its early activation cannot predict the clinical prognosis, but the dynamic enhancement of SMC activation on the affected side is related to the rehabilitation speed of the affected limb.

Objective:To study the dynamic changes of cortical functional reorganization in striatocapsular infarction (SCI) by blood oxygenation level dependent-functional MR imaging (BOLD-fMRI) and its relation with recovery of motor function in the upper extremity of the hemiplegia.Methods:A total of 17 patients with SCI at first onset and combined with upper extremity of the hemiplegia, admitted to our hospital from June 2015 to December 2017, were included; 15 healthy volunteers recruited at the same time were selected as control group. Within 1 week of onset, and 1 and 3 months after onset, BOLD-fMRI was performed under passive finger flexion and extension task on hemiplegia side, and the activation of brain functional areas at different stages was observed by SPM8 software. The Xjview software was used to observe the activation of the corresponding brain activation areas in BOLD-fMRI and make dynamic comparison with the standard brain activation areas. The upper limb part of Fugl-Meyer (FM-UL) scale was used to track the motor function of the upper limb.Results:The activation in the control group was mainly located in the contralateral sensorimotor cortex (SMC) and bilateral supplementary motor area (SMA). The regions of interest in early BOLD-fMRI for stroke patients at early stage can be divided into 3 types. The activation in patients with type I was mainly at the affected side, enjoying bilateral SMC and SMA activation; 1 and 3 months after onset, activation at the contralateral SMC decreased gradually, and activation at the ipsilateral SMC region gradually increased. The activation in patients with type Ⅱ was merely at the ipsilateral SMC and SMA regions; 1 month after onset, bilateral SMC area and SMA area were significantly activated, mainly at the contralateral side; 3 months after onset, the SMC area was further activated at the ipsilateral side and weakened at the ipsilateral side. The activation in patients with type Ⅲ was only at SMC or M1 at the ipsilateral side; 1 month after onset, activation at the ipsilateral SMC area increased slightly; 3 months after onset, activation at the ipsilateral SMC area further enhanced, and SMA area was without activation performance. The FM-UL scores of the affected limbs of patients with type I, type II and type III at the early onst were 10.0±3.3, 10.6±5.7 and 9.2±4.0, respectively, without statistical differences ( P>0.05); 1 month after onset, the FM-UL scores of patients with type I, type II and type III were 52.3±4.6, 36.6±2.4 and 12.5±3.0, respectively, with significant differences ( P<0.05); 3 months after onset, FM-UL scores of patients with type I, type II and type III were 63.7±2.9, 59.2±3.1 and 13.3±5.0, respectively, with statistical differences ( P<0.05). Conclusions:Intracranial functional reorganization is a dynamic process. The early and dynamic activation of the affected SMC region and the early activation of the contralateral SMC region and bilateral SMA regions are of great significance in the process of stroke rehabilitation.

Objective: To explore the relationship between cerebral hemorrhagic transformation (HT) and angiographic early venous filling (EVF) following mechanical thrombectomy for acute ischemic stroke. Methods: A retrospective imaging analysis was performed in the consecutive patients treated from January 2015 to November 2018 for acute anterior circulation large vessel occlusion using mechanical thrombectomy on the Affiliated Hospital of Yangzhou University. The demography, vascular risk factors and other clinical data of the patients were also collected. According to the experimental study of European Cooperative Acute Stroke Study Ⅱ (ECASS Ⅱ), the modified classification of HT after mechanical thrombectomy was divided into HT negative, HT-Ⅰ type and HT-Ⅱ type. The differences in EVF, clinical and demographic characteristics were compared. Multivariate logistic regression analysis was used to identify the independent risk factors for HT and clinical outcome. Diagnostic test characteristics of EVF for HT-Ⅱ type were determined using a receiver operating characteristic curve (ROC) analysis. Results: A total of 98 patients with acute ischemic stroke who received mechanical thrombectomy were enrolled, including HT negative in 48 cases (49.0%, 48/98), HT-Ⅰ in 40 cases (40.8%, 40/98) and HT-Ⅱ in 10 cases (10.2%, 10/98). Significant differences were noted in age, and incidence of atrial fibrillation, EVF and poor outcomes among three groups (P<0.05). Multivariate logistic regression analysis showed that EVF [odds ratio (OR) 5.960, 95%CI 1.750-8.960, P=0.001] and atrial fibrillation (OR 3.485, 95%CI 1.962-18.986, P=0.028) were risk factors for the occurrence of HT-Ⅱ after mechanical thrombectomy. No risk factor for HT-Ⅰ was noted. Baseline National Institute of Health Stroke Scale (NIHSS) score (OR 1.162, 95%CI 1.021-1.345, P=0.038), EVF (OR 5.358, 95%CI 1.665-13.653, P=0.006) and HT-Ⅱ (OR 1.326, 95%CI 1.226-2.038, P=0.032) were independent risk factors for poor outcomes. And the sensitivity and specificity of EVF in prediction for HT-Ⅱ were 80.0% and 86.4% respectively, with the area under the ROC curve of 0.832. Conclusion Presence of EVF after mechanical thrombectomy may be the predictor for HT-Ⅱ, which indicates the poor clinical outcomes for acute ischemic stroke patients.

Objective To evaluate the role of non?invasive fractional flow reserve (FFR) derived from coronary CT angiography (CCTA) in assessing the hemodynamic relevance of myocardial bridging (MB). Methods A total of 60 patients without obstructive coronary artery disease but with CCTA?confirmed MB of the left anterior descending coronary artery and 30 patients with negative CCTA findings as control group were retrospectively included in this study. The 60 patients with MB were divided into 2 groups (superficial and deep MB group) according to the depth of MB. Age and sex were matched among three groups. The location, length, depth, and degree of systolic compression of the MB were measured. The FFRCT values (including systolic and diastolic phases) were measured at three points (segments 1 to 2 cm proximal to a MB, mid?tunneled segment and segments 1 to 2 cm distal to the MB) by cFFR software. Patients with FFRCT<0.75 were deemed to have hemodynamic relevance (abnormal group). χ2 test, ANOVA test, Mann?Whitney U test, Kruskal?Wallis H test and logistic regression model were used for statistical analysis. Results The FFRCT values decreased from diastolic phase to systolic phase in deep MB group [0.90 (0.81-0.94) vs. 0.93 (0.91-0.97), Z=-2.172, P=0.03]. Compared to control group, the FFRCT values decreased in both diastolic phase and systolic phase in superficial MB group as well as deep MB group [systole 0.92 (0.90-0.94) control vs. 0.84 (0.77-0.88) superficial vs. 0.67 (0.50-0.88) deep, H=37.193, P<0.001; diastole 0.93 (0.89-0.94) control vs. 0.85 (0.73-0.92) superficial vs. 0.81 (0.65-0.87) deep, H=26.508, P<0.001]. Abnormal FFRCT values (<0.75) were found in 28 (47.7%) MB patients (9 superficial vs. 19 deep). The length (OR=1.067, 95% CI: 1.016-1.122, P=0.010) and depth (OR=2.028, 95%CI: 1.129-3.644, P=0.018) of MB were associated with the abnormal FFRCT values.Conclusions The FFRCT values of coronary artery distal to MB were lower than that without MB. Abnormal FFRCT values are more prevalent in deep MB. MB length and depth demonstrate moderate predictive value for an abnormal FFRCT value.

Objective: To investigate whether a deep learning-based model using unenhanced computed tomography (CT) at baseline could predict the malignancy of pulmonary nodules. Methods: A deep learning model was trained and applied for the discrimination of pulmonary nodule in Dr. Wise Lung Analyzer. This study retrospectively recruited 130 consecutive participants with pulmonary nodules detected on CT who undergoing biopsy or surgery from May 2009 to June 2017 in Jinling hospital. A total of 136 pulmonary nodules were included in this study, including 86 malignant nodules and 50 benign ones. All patients underwent CT scans 2 times at least, the first scan was defined as baseline and the last scan before the pathological results was defined as final scan. The ROC curve of deep learning model was plotted and the AUCs were calculated. Delong test was used to examine the difference of AUCs baseline and final scan. The nodules were further divided into subsolid nodule group (pure ground-glass nodule and part solid nodule) (n=87) and solid nodule group (n=49). The difference of AUCs at baseline and final scans was evaluated intra two groups. Results: The AUCs of the deep learning model at final and baseline scans were 0.876 and 0.819, respectively. There was no significant difference between them (P=0.075). The result indicated that the model could predict the consequences of pulmonary nodules well at baseline. In small nodules (longest diameter ≤10mm), the AUC at final scan (0.847) was better than it at baseline scan (0.734), but there was no significant difference between them (P=0.058). In solid nodule group, The AUC at final scan (0.932) was better than it at baseline scan (0.835), but there was no significant difference between them (P=0.066). In subsolid nodule group, the deep learning model exhibited consistent performance at final scan (AUC, 0.759) with the baseline scan (AUC, 0.728, P=0.580). Conclusions The deep learning model could predict the malignancy of pulmonary nodules including small ones at baseline, and the model exhibited consistent performance between baseline and final scans in subsolid nodules.

Objective: To investigate the imaging features of cerebral small vessel disease(SVD) in systemic lupus erythematosus(SLE) patients with impaired renal function and their related risk factors. Methods: Seventy-six SLE patients and forty age- and sex-matched healthy controls were recruited, and SLE patients were divided into the impaired renal function group [estimated glomerular filtration rate (eGFR) <90 ml/(min·1.73 m²)] (n=38) and the normal renal function group [eGFR≥90 ml/(min·1.73 m²)] (n=38) according to their eGFR. All subjects underwent brain MRI, cognitive and psychiatric testing. The SVD scores were measured, total white matter hyperintensity (WMH) and SVD scores were calculated, and the risk factors of SVD scores were analyzed by using ordinal logistic regression. Results: SLE patients in the impaired renal function group showed higher basal ganglia PVS, centrum semiovale perivascular space (PVS), periventricular WMH, deep WMH and total SVD scores compared with normal controls or patients with normal renal function (H=44.568, 31.380, 31.172, 43.419, 24.317, P<0.001) . The ordinal logistic regression analysis showed that C-reactive protein was a risk factor for SVD in patients with SLE(OR=1.323, P<0.01). Conclusion SLE patients with impaired renal function had a higher SVD burden on MR imaging, particularly PVS in the basal ganglia and deep WMH, which was affected by the C-reactive protein level.