肝脓肿介入治疗辽宁专家共识（2022）

【摘要】 介入联合抗生素已成为肝脓肿的主要治疗手段，但临床工作中仍存在诸多治疗难点且缺乏治疗规范。本共识对肝脓肿介入治疗的适应证及禁忌证、介入治疗操作细节、并发症防治和特殊情况下介入治疗策略进行归纳和说明，并阐述肝脓肿形成原因、危险因素、抗生素选择和预防措施等，旨在为广大临床工作者提供肝脓肿介入治疗的有益指导。     

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.     

结核病与糖尿病共病的治疗管理专家共识

结核病与糖尿病均是临床上的常见病和多发病,两者可合并存在,相互影响。活动性结核病作为感染因素可加重糖尿病病情,而糖尿病患者又是发生结核病的高危人群,结核病与糖尿病双重负担将成为重大的全球公共卫生问题。因此,需重视结核病与糖尿病共病的治疗管理。本共识重点介绍了结核病与糖尿病共病的危害、发病机制、双向筛查、临床特点、诊断、治疗和管理等内容。     

2020 中国心力衰竭医疗质量控制报告

心力衰竭(心衰)是各种心脏疾病的严重和终末阶段,已经成为影响我国居民健康的重要公共卫生问题。针对目前我国心衰规范化诊治方面存在的问题,积极开展心衰医疗质量评价和改进,提高心衰诊治的规范性,具有重要的意义。自从2018年3月成立国家心血管病中心心力衰竭专病医联体(HFMU-NCCD),加入医院已超过1000家。国家心血管病医疗质量控制中心专家委员会心力衰竭专家工作组(NCCQI-HF)纳入2017~2020年期间在医联体单位住院的心衰患者,开展全国心衰医疗质量评价,包括心衰的诊断与评估、指南指导的药物治疗及器械治疗、临床结局等,并依据该研究结果和我们的思考,撰写成本报告。此外,通过与China-HF注册研究(2012~2015年)结果及美国心脏学会(AHA)的“跟着指南走——心力衰竭(GWTG-HF)”项目结果做比较,发现当前我国在心衰诊疗规范化方面较以前有明显改善,但仍存在诊疗不足、治疗不当及治疗过度等现象,不同等级医院之间也存在差异,而且与美国比较仍有一定差距,也体现出心衰患者特点以及国情的不同。未来需要提高数据填报数量和质量,持续开展医疗质量控制和改进,以便从整体上提高我国心衰的诊治水平。