碳离子和质子治疗胰腺癌安全性和有效性的系统评价及Meta分析

目的 评价碳离子和质子治疗胰腺癌（PaC）的安全性和有效性。方法 检索数据库纳入碳离子和质子治疗PaC的临床研究，检索时间为自建库至2019年6月。两位研究者独立筛选文献、提取资料。采用STATA 12.0和MetaAnalyst Beta 3.13软件进行Meta分析。结果 共纳入8篇文献，包含459例PaC患者。效应模型Meta分析显示，碳离子和质子治疗PaC的2、3、5级胃肠道溃疡发生率分别为7%、2%、0；2、3、4级厌食症发生率分别为6%、3%、0；1、2年总生存率（OS）分别为77%、45%；2年局部控制率为81%；1年无局部进展率为88%；1年局部复发率为15%。碳离子和质子治疗PaC的2、3、2～3级胃肠道溃疡发生率分别为6.8%、1.5%、9.2%和3.5%、8.3%、6.1%（均P>0.05）；1、2年OS分别为77.1%、44.4%和77.6%、49.7%（P均>0.05）。结论 碳离子和质子治疗PaC安全有效，两者的安全性和有效性相似。     

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.     

中西医结合治疗癫痫伴抑郁Meta分析

目的:系统评价中西医结合治疗癫痫伴抑郁患者的临床疗效。方法:计算机检索中国知网、万方数据库、中国医学生物文献数据库、维普资讯、PubMed及Web of Science等中英文数据库,筛选出自各数据库建库以来至2018年7月关于中西医结合治疗癫痫伴抑郁的临床随机对照试验(randomized controlled trial,RCT)研究,根据系统评价标准依次进行文献质量评价和Meta分析。结果:纳入14个研究,共1183例患者。Meta分析结果显示:中西医结合治疗癫痫伴抑郁在降低HAMD评分、提高QLOIE-31评分方面优于单纯西药治疗。癫痫发作频次改善率效应值合并量OR=3.53,95%CI(1.73,7.17),Z=3.48(P=0.0005),差异有统计学意义(P<0.05)。临床安全性效应值合并量OR=0.59,95%CI(0.35,0.98),Z=2.03(P=0.04),差异有统计学意义(P<0.05)。结论:中西医结合足疗程治疗癫痫伴抑郁临床疗效优于单纯西药治疗,且临床安全性良好,但因纳入研究数量和质量有限,上述结论尚待高质量的RCT研究验证。     

后外侧结构重建在新鲜股骨颈骨折人工股骨头置换术中的应用

目的探讨后外侧结构重建对后外侧入路人工股骨头置换术术后早期关节脱位的影响。方法选取2016年9月至2017年8月于我院行后外侧入路初次人工股骨头置换术的股骨颈骨折患者60例,根据术中是否修补关节囊及外旋肌群分为重建组(33例:舌形切开关节囊,术中将关节囊及外旋肌群原位缝合在大转子后方及臀中肌肌腱附着处)和对照组(27例:切除关节囊后,术中未进行外旋肌群修复重建)。比较两组的手术情况及术后近期关节功能情况。结果重建组的手术时间为(45.0±15.3) min,长于对照组的(35.0±12.4) min (P <0.05)。重建组术腔引流量为(200.0±80.0) m L,少于对照组的(420.0±120.6) m L (P <0.05)。重建组的早期脱位率为0.000%(0例),与对照组的7.407%(2例)比较无统计学差异(P>0.05)。重建组术后Harris评分为(92.0±3.4)分,高于对照组的(88.2±5.0)分(P <0.05)。结论在后外侧入路人工股骨头置换过程中行后外侧结构重建能够有效减少术腔引流量,提高髋关节Harris评分,对维持髋关节软组织平衡具有重要意义。     

放射治疗记录与验证系统质量控制指南

放疗记录与验证系统(RVS)是一套用于防止医用电子加速器等放疗设备治疗参数设置错误,并且记录所有放疗阶段执行参数的医用计算机软件控制系统。为确保患者的治疗安全,必须对记录与验证系统采取必要的质量控制措施。本指南内容涉及:RVS安装和参数设定过程中的质量控制;RVS的验收测试;RVS在临床使用过程中的持续质量控制;使用RVS过程中的典型错误类型;执行RVS验收测试的具体测试例。     

基因间长链非编码RNA152靶向微小RNA?103a?3p对非小细胞肺癌细胞增殖和侵袭迁移的影响

目的探讨基因间长链非编码RNA 152(LINC00152)靶向调控微小RNA-103a-3p(miR-103a-3p)表达及对非小细胞肺癌(NSCLC)细胞增殖和侵袭迁移的影响。方法采用实时定量PCR(QPCR)检测正常肺上皮细胞BEAS-2B及NSCLC细胞(ANIP-973、NCI-H157、A549和NCI-H1975)的LINC00152水平。选取LINC00152水平最高的细胞分别转染LINC00152特异性小干扰RNA(si-LINC00152组)或无关序列(si-NC组),另设未转染细胞为对照组。QPCR检测LINC00152水平,活细胞计数CCK-8法、Transwell小室和划痕实验测定细胞增殖、侵袭和迁移能力,Western blotting检测基质金属蛋白酶(MMP)-2、MMP-9和第10号染色体缺失的磷酸酶及张力蛋白同源基因(PTEN)的水平;荧光素酶报告实验验证LINC00152靶向结合miR-103a-3p的能力。结果NSCLC细胞的LINC00152水平均高于BEAS-2B细胞(P<0.05),尤其是NCI-H1975细胞的最高。si-LINC00152组的LINC00152水平为0.352±0.087,低于对照组的1.058±0.219和si-NC组的1.126±0.139(P<0.05)。与si-NC组和对照组相比,si-LINC00152组NCI-H1975细胞转染48、72 h的增殖活力下降(P<0.05);si-LINC00152组的划痕愈合率和穿膜细胞数分别为(27.386±2.428)%和(78.840±5.031)个,低于si-NC组的(77.675±4.803)%和(179.208±13.264)个及对照组的(76.371±5.385)%和(174.003±15.678)个(P<0.05);与si-NC组和对照组相比,si-LINC00152组的MMP-2和MMP-9水平均降低,而PTEN水平升高(P<0.05)。对照组和si-NC组上述指标的差异无统计学意义(P>0.05)。双荧光素酶报告分析证实,miR-103a-3p模拟物降低了野生型LINC00152的荧光素酶活性(P<0.05),但对突变型无影响(P>0.05)。结论LINC00152在NSCLC细胞中高表达并发挥促癌作用,与NSCLC的迁移侵袭密切相关,LINC00152与miR-103a-3p间的相互作用在NSCLC靶向治疗中有一定潜能。     

三级公立医院行政总值班管理现状分析与研究*

探索一套符合现代化医院的行政总值班管理体系，确保医院管理工作安全、稳定运行。分析国内外医院总值班现状和问题，运用系统性、科学性、规范性等综合干预措施，进一步提高值班人员责任意识、分析问题和解决问题能力等综合管理能力，使总值班管理从粗放式向精细化转变，实现目标标准化、过程规范化、结果同质化的服务格局。