益心舒胶囊联合常规疗法治疗非瓣膜性心房颤动临床研究

目的：观察益心舒胶囊联合常规疗法治疗非瓣膜性心房颤动的疗效。方法：纳入96 例气阴亏虚兼瘀血内阻型非瓣膜性心房颤动患者，采用随机数字表法分为对照组和观察组各48 例。2 组患者均予常规降压、降糖、调脂以及调整心态、饮食等治疗措施，并服用华法林钠片治疗，观察组加予益心舒胶囊治疗。2 组疗程均为3 个月。观察2 组治疗前及治疗3 个月后凝血功能指标、心房颤动发作情况、血浆N 末端B 型利钠肽原（NT-proBNP） 以及血液流变学水平的变化。结果：治疗后，2 组凝血酶原时间（PT）、活化部分凝血活酶时间（APTT）、凝血酶时间（TT）、国际标准化比值（INR） 均较治疗前增加（P＜0.01），观察组PT、APTT、TT、INR 均长于或高于对照组（P＜0.01）。2 组心房颤动发作时间与发作频率均较治疗前缩短或降低（P＜0.01），观察组心房颤动发作时间与发作频率均短于或低于对照组（P＜0.01）。2 组血浆NT-proBNP 水平均较治疗前降低（P＜0.01），观察组血浆NT-proBNP 水平均低于对照组（P＜0.01）。2 组红细胞比容（HCT）、纤维蛋白原（Fb）、全血黏度、血浆比黏度均较治疗前降低（P＜0.01），观察组4 项指标值均低于对照组（P＜0.01）。结论：在常规治疗基础上加服益心舒胶囊治疗气阴亏虚兼瘀血内阻型非瓣膜性心房颤动，在改善患者的凝血功能、心功能以及缓解临床症状方面疗效明显，且可有效改善血液流变学水平。     

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

足趾移植长手指全形再造术的临床应用

目的探讨足趾移植长手指全形再造手术的临床疗效。方法自2015年6月至2019年6月,对16例因外伤致手指缺损患者采用足趾移植长手指全形再造术,术后评估供区及受区的感觉功能、运动功能及外观形态,分析指甲畸形及增生性瘢痕的发生情况,并记录术后发生感染、血肿、皮片坏死及供区愈合不良等情况;通过调查问卷的方式分析患者的满意度。结果所有患者术后获随访1~12个月,其中2例受区发生感染,1例受区皮片边缘发生坏死,经换药后予以缓解;其余患者的供、受区均未出现长时间的痛疼感觉,受区感觉功能恢复达87.50%,受区运动功能恢复均较满意,手指外形基本满意。供区感觉受影响者2例,运动功能受限者1例,外形一般者2例。所有患者无指甲畸形及增生性瘢痕发生;满意者1例,基本满意者14例,不满意者1例。结论采用足趾移植长手指全形再造手术,基本可以满足患者及医师对于缺损手指进行完美修复的目标。     

Chest wall pain following lung stereotactic body radiation therapy using 48 Gy in three fractions: A search for predictors

Purpose

Chest wall pain is an uncommon but bothersome late complication following lung stereotactic body radiation therapy. Despite numerous studies investigating predictors of chest wall pain, no clear consensus has been established for a chest wall constraint. The aim of our study was to investigate factors related to chest wall pain in a homogeneous group of patients treated at our institution.

Porocarcinoma coexisting at a site of Bowen disease in a 63‐year‐old woman

Porocarcinoma is an unusual, locally aggressive and potentially fatal neoplasm. Several cutaneous malignancies have been described in association with porocarcinoma, including squamous cell carcinoma, basal cell carcinoma and tricholemmal carcinoma. Previous reports have indicated that the occurrence of malignant tumours in combination with porocarcinoma is extremely rare, in particular with regard to Bowen disease (BD). We report an uncommon case of porocarcinoma occurring synchronously in a single BD lesion in a 63‐year‐old woman with multiple BD lesions. The clinical and histological findings confirmed this diagnosis.