Managing the CT Data Explosion: Initial Experiences of Archiving Volumetric Datasets in a Mini-PACS

Two image datasets (one thick section dataset and another volumetric dataset) were typically reconstructed from each single CT projection data. The volumetric dataset was stored in a mini-PACS with 271-Gb online and 680-Gb nearline storage and routed to radiologists’ workstations, whereas the thick section dataset was stored in the main PACS. Over a 5-month sample period, 278 Gb of CT data (8976 examinations) was stored in the main PACS, and 738 Gb of volumetric datasets (6193 examinations) was stored in the mini-PACS. The volumetric datasets formed 32.8% of total data for all modalities (2.20 Tb) in the main PACS and mini-PACS combined. At the end of this period, the volumetric datasets of 1892 and 5162 examinations were kept online and nearline, respectively. Mini-PACS offers an effective method of archiving every volumetric dataset and delivering it to radiologists.     

基于网络药理学和GEO数据集的翻白草治疗2型糖尿病分子机制探讨

目的:网络药理学结合GEO数据集探讨翻白草治疗2型糖尿病的分子机制。方法:通过中药系统药理学数据库与分析平台(TCMSP)下载翻白草化学成分信息,经SwissADME对各成分进行筛选和SwissTargetPrediction预测靶点后,得到药物作用靶点。采用R软件下载GEO数据中GSE系列,提取表达矩阵,对原始数据进行归一化处理后提取临床信息,采用limma程序包进行2型糖尿病差异基因分析。通过微生信在线平台获取药物作用靶点和疾病差异基因交集,即为翻白草治疗2型糖尿病的潜在作用靶点,对潜在作用靶点进行生物信息学分析,采用分子对接方法对分析结果进行验证。结果:筛选得到翻白草中20个化学成分,经SwissTargetPrediction预测后,得到374个药物作用靶点,R软件分析获得2型糖尿病差异基因658个,取交集后得到17个潜在作用靶点。经过基因本体(GO)富集分析和京都基因与基因组百科全书(KEGG)通路分析可知,翻白草有效成分有原儿茶酸、咖啡酸、山柰酚、3,4,5-三羟基苯甲酸、罗索酸,通过作用蛋白激酶C-θ (PKC-θ)、核转录因子-κB p65亚单位(RELA)、核糖体蛋白S6激酶α3 (RPS6KA3)、信号传导和转录激活因子3 (STAT3)、乳酸脱氢酶B (LDHB)和6-磷酸果糖激酶-2/果糖-2,6-二磷酸酶同工酶3 (PFKFB3)靶点,参与胰岛素抵抗通路(P<0.01)、低氧诱导因子-1 (HIF-1)信号通路(P<0.01)和脂肪细胞因子信号通路(P<0.01)来发挥治疗2型糖尿病作用。分子对接结果显示,PKC-θ、RELA、STAT3与咖啡酸,RPS6KA3与罗索酸,LDHB与3,4,5-三羟基苯甲酸,PFKFB3与山柰酚之间均具有较好的对接活性。结论:通过网络药理学结合GEO数据集,初步预测了翻白草治疗2型糖尿病的潜在靶点和可能的作用机制,为翻白草治疗2型糖尿病的进一步研究提供理论依据。     

Measurement of the penetration of 56 cosmetic relevant chemicals into and through human skin using a standardized protocol

OECD test guideline 428 compliant protocol using human skin was used to test the penetration of 56 cosmetic-relevant chemicals. The penetration of finite doses (10 μL/cm²) of chemicals was measured over 24 hours. The dermal delivery (DD) (amount in the epidermis, dermis and receptor fluid [RF]) ranged between 0.03 ± 0.02 and 72.61 ± 8.89 μg/cm². The DD of seven chemicals was comparable with in vivo values. The DD was mainly accounted for by the amount in the RF, although there were some exceptions, particularly of low DD chemicals. While there was some variability due to cell outliers and donor variation, the overall reproducibility was very good. As six chemicals had to be applied in 100% ethanol due to low aqueous solubility, we compared the penetration of four chemicals with similar physicochemical properties applied in ethanol and phosphate-buffered saline. Of these, the DD of hydrocortisone was the same in both solvents, while the DD of propylparaben, geraniol and benzophenone was lower in ethanol. Some chemicals displayed an infinite dose kinetic profile; whereas, the cumulative absorption of others into the RF reflected the finite dosing profile, possibly due to chemical volatility, total absorption, chemical precipitation through vehicle evaporation or protein binding (or a combination of these). These investigations provide a substantial and consistent set of skin penetration data that can help improve the understanding of skin penetration, as well as improve the prediction capacity of in silico skin penetration models.     

基于眼动实验的显著目标检测图像库构建

构建一个合理的显著目标检测标准图像库,探讨图像分割对于显著目标检测的重要意义。首先以Berkeley的BSDS500图像库为基础,通过使用眼动仪记录10位被试观看图片时的眼动轨迹,并结合手工勾画的标准分割图像,构建了一个显著目标检测图像库（含500幅自然图像）。其次,基于gPb-UCM图像分割算法结果,同时引入中央偏置和图像边界先验作为后处理,建立了一个简单的显著目标检测方法。实验结果显示,与以往的二值显著目标图像库不同,本研究为多目标图像提供了每个目标的相对显著水平,可作为更为符合人类感知经验的显著目标检测参考图像。另外,研究发现,基于已有的图像分割算法,并结合简单的后处理就能有效地实现复杂图像中的显著性目标检测（和标准图像的相关系数为0.53）,其效果接近现有最优算法的水平（相关系数为0.54）,证明了图像分割在显著目标检测中的重要作用。     

Detection of metallic objects on digital radiographs with convolutional neural networks: A MRI screening tool

IntroductionScreening for metallic implants and foreign bodies before magnetic resonance imaging (MRI) examinations, are crucial for patient safety. History of health are supplied by the patient, a family member, screening of electronic health records or the picture and archive systems (PACS). PACS securely store and transmits digital radiographs (DR) and related reports with patient information. Convolutional neural networks (CNN) can be used to detect metallic objects in DRs stored in PACS. This study evaluates the accuracy of CNNs in the detection of metallic objects on DRs as an MRI screening tool.MethodsThe musculoskeletal radiographs (MURA) dataset consisting of 14.863 upper extremity studies were stratified into datasets with and without metal. For each anatomical region: Elbow, finger, hand, humerus, forearm, shoulder and wrist we trained and validated CNN algorithms to classify radiographs with and without metal. Algorithm performance was evaluated with area under the receiver-operating curve (AUC), sensitivity, specificity, predictive values and accuracies compared with a reference standard of manually labelling.ResultsSensitivities, specificities and area under the ROC-curves (AUC) for the six anatomic regions ranged from 85.33% (95% CI: 78.64%–90.57%) to 100.00% (95% CI: 98.16%–100.00%), 75.44% (95% CI: 62.24%–85.87%) to 93.57% (95% CI: 88.78%–96.75%) and 0.95 to 0.99, respectively.ConclusionCNN algorithms classify DRs with metallic objects for six different anatomic regions with near-perfect accuracy. The rapid and iterative capability of the algorithms allows for scalable expansion and as a substitute MRI screening tool for metallic objects.Implications for practiceAll CNNs would be able to assist in metal detection of digital radiographs prior to MRI, an substantially decrease screening time.     

Dataset for reporting of carcinoma of the urethra (in urethrectomy specimens): recommendations from the International Collaboration on Cancer Reporting (ICCR)

The International Collaboration on Cancer Reporting (ICCR) is a not‐for‐profit organisation sponsored by the Royal Colleges of Pathologists of Australasia and the United Kingdom, the College of American Pathologists, the Canadian Association of Pathologists in association with the Canadian Partnership Against Cancer, the European Society of Pathology, the American Society of Clinical Pathology and the Faculty of Pathology, Royal College of Physicians of Ireland. Its goal is to produce standardised, internationally agreed‐upon, evidence‐based datasets for cancer pathology reporting throughout the world. This paper describes the development of a cancer dataset by the multidisciplinary ICCR expert panel for the reporting of carcinoma of the urethra in urethrectomy specimens. The dataset is composed of ‘required’ (mandatory) and ‘recommended’ (non‐mandatory) elements, which are based on a review of the most recent evidence and supported by explanatory commentary. Fourteen required elements and eight recommended elements were agreed by the international dataset authoring committee to represent the essential/required (core) and recommended (non‐core) information for the reporting of carcinoma of the urethra in urethrectomy specimens. Use of an internationally agreed, structured pathology dataset for reporting carcinoma of the urethra (in urethrectomy specimens) will provide the necessary information for optimal patient management, will facilitate consistent data collection and will provide valuable data for research and international benchmarking. The dataset will be valuable for those countries and institutions that are not in a position to develop their own datasets.     

National Emergency Medical Services Information System (NEMSIS)

The absence of emergency medical services (EMS) patient care data has hindered development andevaluation of EMS systems. The National Highway Traffic Safety Administration (NHTSA), in cooperation with the Health Resources andServices Administration (HRSA), has provided funding to the National Association of State EMS Directors to develop a National EMS Information System (NEMSIS). NEMSIS is being designed to provide a uniform national EMS dataset, with standard terms, definitions, andvalues, as well as a national EMS database, with aggregated data from all states on a limited number of data elements. Forty-eight of the states, the District of Columbia, andthree territories signed a memorandum of agreement documenting support for the NEMSIS project andexpressing a desire for full implementation of the NEMSIS dataset. NHTSA has agreed to house the National EMS Database at its National Center for Statistics andAnalysis. NHTSA, in cooperation with HRSA andthe Centers for Disease Control andPrevention, recently entered into a cooperative agreement with the University of Utah School of Medicine to operate a NEMSIS Technical Assistance Center that will provide related assistance to official EMS agencies andto commercial software vendors. The Technical Assistance Center will also biannually assess state andterritorial capabilities to provide data to the national EMS database. NEMSIS will provide a uniform national EMS dataset, with standard terms, definitions, andvalues, as well as a national EMS database, with aggregated data from all states on a limited number of data elements. Many of the potential benefits of implementation of NEMSIS are enumerated in this report.     

乳腺癌中KIF4A基因表达的临床意义及基因富集分析

目的 探讨驱动蛋白超家族4A （kinesin family member 4A,KIF4A）在乳腺癌中的表达及与临床病理特征、预后的关系。方法 利用GEO数据库的GSE3494公共数据集和TCGA数据库的乳腺癌样本及其临床资料,采用χ²检验进行KIF4A与临床病理特征的相关性分析,Kaplan-Meier法进行生存分析。通过基因富集分析预测乳腺癌中高表达KIF4A所富集的基因集。结果 KIF4A在不同Elston组织学分级和TNM分期的乳腺癌肿瘤样本中表达差异有统计学意义（P=0.000）。GSE3494和TCGA数据库中KIF4A与ER水平、PR水平均显著相关（P=0.000）;与年龄仅TCGA数据库分析结果差异有统计学意义（P=0.000）。此外,GSE3494数据集中,KIF4A与肿瘤大小、淋巴结浸润均显著相关（P=0.000）;TCGA数据库中,KIF4A仅与T分期显著相关（P=0.000）,与N分期（P=0.081）、M分期（P=0.372）均不相关。KIF4A高表达的乳腺癌患者预后较差,其疾病特异生存期（P=0.001）和总体生存率（P=0.005）均远低于KIF4A低表达患者,且富集了与细胞分裂、细胞周期调控、DNA复制及DNA损伤修复有关的基因集。结论 KIF4A与乳腺癌多个临床病理指标相关,可作为潜在的乳腺癌预后标志物和治疗靶标进一步研究。     

基于薄层CT扫描大数据的骨结构常规分析

目的骨密度降低和骨结构劣化是骨质疏松症发生病理性骨折的根本原因,因此,准确、快速判断骨结构优劣具有重要的诊断及骨折风险评估价值。此研究通过薄层CT扫描大数据的描述性统计学分析,命名为"骨结构常规",探讨量化判断骨结构优劣的可行性。方法 721例薄层CT扫描数据,在Mimics 14.0以三维分割的方式将股骨分割为股骨头、股骨颈和转子间三个部分,并且加入一步随意分割股骨颈的步骤。通过Excel宏命令获得数据:1CT值平均值:Hu;2标准差系数:V_σ(σ/Hu)。统计学分析:1年龄与Hu,V_σ的相关及回归分析。2股骨头、股骨颈、转子间部分与年龄相关性的比较。3随意分割的股骨颈与准确切割的股骨颈之间的单因素方差分析。结果相关分析:1年龄与松质骨及完整股骨的Hu呈负相关,松质骨R~2为0.799-0.8278,完整股骨R~2为0.7951-0.8251;2年龄与松质骨及完整股骨的V_σ呈正相关,松质骨R2为0.8829~0.9411,完整股骨R2为0.8381~0.8839;3年龄与松质骨的相关性优于完整股骨。相关性比较:股骨颈与年龄的相关性高于股骨头与转子间部分。单因素方差分析:随意分割的股骨颈与准确分割的股骨颈相比较,在松质骨部分二者的CT值均值和标准差系数并无显著差异。结论骨结构常规具有良好的质量控制,可用于量化判断骨结构优劣。