Research and applications: Semantic analysis of SNOMED CT for a post-coordinated database of histopathology findings

Objective

This research investigated the use of SNOMED CT to represent diagnostic tissue morphologies and notable tissue architectures typically found within a pathologist''s microscopic examination report to identify gaps in expressivity of SNOMED CT for use in anatomic pathology.

Methods

24 breast biopsy cases were reviewed by two board certified surgical pathologists who independently described the diagnostically important tissue architectures and diagnostic morphologies observed by microscopic examination. In addition, diagnostic comments and details were extracted from the original diagnostic pathology report. 95 unique clinical statements were extracted from 13 malignant and 11 benign breast needle biopsy cases.

Results

75% of the inventoried diagnostic terms and statements could be represented by valid SNOMED CT expressions. The expressions included one pre-coordinated expression and 73 post-coordinated expressions. No valid SNOMED CT expressions could be identified or developed to unambiguously assert the meaning of 21 statements (ie, 25% of inventoried clinical statements). Evaluation of the findings indicated that SNOMED CT lacked sufficient definitional expressions or the SNOMED CT concept model prohibited use of certain defined concepts needed to describe the numerous, diagnostically important tissue architectures and morphologic changes found within a surgical pathology microscopic examination.

Conclusions

Because information gathered during microscopic histopathology examination provides the basis of pathology diagnoses, additional concept definitions for tissue morphometries and modifications to the SNOMED CT concept model are needed and suggested to represent detailed histopathologic findings in computable fashion for purposes of patient information exchange and research.

Trial registration number

UNMC Institutional Review Board ID# 342-11-EP.     

Research and applications: Identifying survival associated morphological features of triple negative breast cancer using multiple datasets

Background and objective

Biomarkers for subtyping triple negative breast cancer (TNBC) are needed given the absence of responsive therapy and relatively poor prediction of survival. Morphology of cancer tissues is widely used in clinical practice for stratifying cancer patients, while genomic data are highly effective to classify cancer patients into subgroups. Thus integration of both morphological and genomic data is a promising approach in discovering new biomarkers for cancer outcome prediction. Here we propose a workflow for analyzing histopathological images and integrate them with genomic data for discovering biomarkers for TNBC.

Materials and methods

We developed an image analysis workflow for extracting a large collection of morphological features and deployed the same on histological images from The Cancer Genome Atlas (TCGA) TNBC samples during the discovery phase (n=44). Strong correlations between salient morphological features and gene expression profiles from the same patients were identified. We then evaluated the same morphological features in predicting survival using a local TNBC cohort (n=143). We further tested the predictive power on patient prognosis of correlated gene clusters using two other public gene expression datasets.

Results and conclusion

Using TCGA data, we identified 48 pairs of significantly correlated morphological features and gene clusters; four morphological features were able to separate the local cohort with significantly different survival outcomes. Gene clusters correlated with these four morphological features further proved to be effective in predicting patient survival using multiple public gene expression datasets. These results suggest the efficacy of our workflow and demonstrate that integrative analysis holds promise for discovering biomarkers of complex diseases.     

Research and applications: Automatic glaucoma diagnosis through medical imaging informatics

Background

Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease.

Objective

To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient''s genome information for screening.

Materials and methods

2258 cases from a population study were used to evaluate the screening software. These cases were attributed with patient personal data, retinal images and quality controlled genome data. Utilizing the multiple kernel learning-based classifier, AGLAIA-MII, combined patient personal data, major image features, and important genome single nucleotide polymorphism (SNP) features.

Results and discussion

Receiver operating characteristic curves were plotted to compare AGLAIA-MII''s performance with classifiers using patient personal data, images, and genome SNP separately. AGLAIA-MII was able to achieve an area under curve value of 0.866, better than 0.551, 0.722 and 0.810 by the individual personal data, image and genome information components, respectively. AGLAIA-MII also demonstrated a substantial improvement over the current glaucoma screening approach based on intraocular pressure.

Conclusions

AGLAIA-MII demonstrates for the first time the capability of integrating patients’ personal data, medical retinal image and genome information for automatic glaucoma diagnosis and screening in a large dataset from a population study. It paves the way for a holistic approach for automatic objective glaucoma diagnosis and screening.     

Research and applications: Imaging informatics for consumer health: towards a radiology patient portal

Objective

With the increased routine use of advanced imaging in clinical diagnosis and treatment, it has become imperative to provide patients with a means to view and understand their imaging studies. We illustrate the feasibility of a patient portal that automatically structures and integrates radiology reports with corresponding imaging studies according to several information orientations tailored for the layperson.

Methods

The imaging patient portal is composed of an image processing module for the creation of a timeline that illustrates the progression of disease, a natural language processing module to extract salient concepts from radiology reports (73% accuracy, F₁ score of 0.67), and an interactive user interface navigable by an imaging findings list. The portal was developed as a Java-based web application and is demonstrated for patients with brain cancer.

Results and discussion

The system was exhibited at an international radiology conference to solicit feedback from a diverse group of healthcare professionals. There was wide support for educating patients about their imaging studies, and an appreciation for the informatics tools used to simplify images and reports for consumer interpretation. Primary concerns included the possibility of patients misunderstanding their results, as well as worries regarding accidental improper disclosure of medical information.

Conclusions

Radiologic imaging composes a significant amount of the evidence used to make diagnostic and treatment decisions, yet there are few tools for explaining this information to patients. The proposed radiology patient portal provides a framework for organizing radiologic results into several information orientations to support patient education.     

Research and applications: Ontology-guided organ detection to retrieve web images of disease manifestation: towards the construction of a consumer-based health image library

Background

Visual information is a crucial aspect of medical knowledge. Building a comprehensive medical image base, in the spirit of the Unified Medical Language System (UMLS), would greatly benefit patient education and self-care. However, collection and annotation of such a large-scale image base is challenging.

Objective

To combine visual object detection techniques with medical ontology to automatically mine web photos and retrieve a large number of disease manifestation images with minimal manual labeling effort.

Methods

As a proof of concept, we first learnt five organ detectors on three detection scales for eyes, ears, lips, hands, and feet. Given a disease, we used information from the UMLS to select affected body parts, ran the pretrained organ detectors on web images, and combined the detection outputs to retrieve disease images.

Results

Compared with a supervised image retrieval approach that requires training images for every disease, our ontology-guided approach exploits shared visual information of body parts across diseases. In retrieving 2220 web images of 32 diseases, we reduced manual labeling effort to 15.6% while improving the average precision by 3.9% from 77.7% to 81.6%. For 40.6% of the diseases, we improved the precision by 10%.

Conclusions

The results confirm the concept that the web is a feasible source for automatic disease image retrieval for health image database construction. Our approach requires a small amount of manual effort to collect complex disease images, and to annotate them by standard medical ontology terms.     

Bionimbus: a cloud for managing,analyzing and sharing large genomics datasets

Background

As large genomics and phenotypic datasets are becoming more common, it is increasingly difficult for most researchers to access, manage, and analyze them. One possible approach is to provide the research community with several petabyte-scale cloud-based computing platforms containing these data, along with tools and resources to analyze it.

Methods

Bionimbus is an open source cloud-computing platform that is based primarily upon OpenStack, which manages on-demand virtual machines that provide the required computational resources, and GlusterFS, which is a high-performance clustered file system. Bionimbus also includes Tukey, which is a portal, and associated middleware that provides a single entry point and a single sign on for the various Bionimbus resources; and Yates, which automates the installation, configuration, and maintenance of the software infrastructure required.

Results

Bionimbus is used by a variety of projects to process genomics and phenotypic data. For example, it is used by an acute myeloid leukemia resequencing project at the University of Chicago. The project requires several computational pipelines, including pipelines for quality control, alignment, variant calling, and annotation. For each sample, the alignment step requires eight CPUs for about 12 h. BAM file sizes ranged from 5 GB to 10 GB for each sample.

Conclusions

Most members of the research community have difficulty downloading large genomics datasets and obtaining sufficient storage and computer resources to manage and analyze the data. Cloud computing platforms, such as Bionimbus, with data commons that contain large genomics datasets, are one choice for broadening access to research data in genomics.     

数字成像和激光照像结肠双对比造影的评价

目的：探讨数字化Ｘ线机及激光照像机进行结肠造影的临床应用价值。方法：２００例病人进行了普通Ｘ线结肠双对比和应用数字化Ｘ线机图像后处理结肠双对比造影,对图像进行分析比较。结果：普通Ｘ线机结肠造影１５例照片质量欠佳,而所有数字化成像激光打印图像清晰、对比度佳,细微结构显示清晰,并可动态观察结肠功能。结论：数字化成像结肠双对比造影激光打印机图像技术明显优于普通成像双对比造影技术。     

用聚合收缩仪Acuvol测定填料含量对复合树脂聚合收缩率的影响

目的：研究改变填料含量对复合树脂聚合收缩率的影响,探讨Acuvol聚合收缩仪的应用价值。方法：研究共使用9种复合树脂,包括6种定制的填料含量分别为80%,78%,76%,70%,60%,50%的复合树脂,以及3种复合树脂商品：含80%填料的Syn D6树脂、含74%填料的Syn Compact Nano树脂和含55%填料的Syn Flow树脂（填料含量均以质量分数表示）。制备半球形树脂样本,用功率为650 mW/cm²卤灯固化40 s（n=10）,分别使用Acuvol的SVVR模式和MVVR模式测定树脂样本的聚合收缩率。结果：3种商品树脂的聚合收缩率为：Syn Flow>Syn Compact Nano>Syn D6。6种定制树脂的聚合收缩率为填料含量50%>60%>70%>76%>78%>80%。商品树脂、配制树脂的填料含量和聚合收缩均呈负相关关系（r为-0.982、-0.968,P<0.001）。使用多帧成像（multiple-view volumetric reconstruction,MVVR)模式、单帧成像（single-view volumetric reconstruction,SVVR)模式测定复合树脂的聚合收缩率,其结果差异无统计学意义（t=0.385, P>0.05）。结论：填料含量是影响复合树脂聚合收缩的重要因素,Acuvol聚合收缩仪可以快速、准确地测定复合树脂的聚合收缩率,MVVR模式和SVVR模式结果差异无统计学意义,SVVR模式更加快捷。     

超声医学多媒体网络学习系统的研发与应用

结合超声医学特点和本院实际,介绍了多媒体网络学习系统的结构、标准、功能和特点,指出该系统实现了超声临床教学的数字化、网络化、信息化,是对超声医学临床教学模式的创新和突破,有效弥补了教学培训资源的不足,缓解了学生临床工作与专业学习的矛盾,全面促进了学生医疗业务能力的提升.     

多层螺旋CT原位观测作物根系的研究

目的：利用多层螺旋CT技术研究一套能够原位，快速，准确，准确，无损地进行作物根系观察和测量的方法。方法：选取有代表意义的3种作物，该3种作物分别种植在2种不同生长基质共9个花盆中，将作物连同基质，花盆一起利用德国西门子sensation 4多层螺旋CT机进行容积扫描，扫描参数为：120kV,90mAs,层厚1mm，8层/s，螺距5mm/转。把获得的作物根系图像的容积数据导入CT3D工作站，利用机器所带的图像后处理软件MIP，SSD，MPR，VR对图像进行成像。经处理过的图像由两位CT专职医生和两位植物根系研究人员对图像质量进行评价，图像质量分为很好，好，不好3个等级。结果 图像质量很好的3种，好的17种，不好的16种。结论：多层螺旋CT可作为一种原位，快速，准确，无损观察作物根系构形的方法，其中以有机培养基质中显示的根系最理想；几种CT成像方法中，以最大密度投影（MIP）成像法图像效果最好。     

医用显微图像处理系统的研制和软件开发

目的 建立医用显微图像处理系统。方法 用普通光学显微镜、工业用摄像机、视频卡和计算朵等构成硬件系统,用ＶＣ＋＋５．０和微软基础类库（Ｍｉｃｒｏｓｏｆｔ Ｆｕｎｃｔｉｏｎ Ｃｌａｓｓ,ＭＦＣ）开发了显微图像处理的专用类ＣＤＩＢ,并结合数据库系统,完成了外周血白细胞分类诊断系统。结果 应用该系统对１３０例病人进行白细胞分类与人工镜检对比,两者结果相符合。结论 该系统具有很好的实用价值和推广价值,可广泛     

三维超声成像技术在宫内节育器定型与定位诊断中的应用

目的：探讨三维超声成像技术在宫内节育器（IUD）定型与定位诊断中的应用价值。方法：对30例经阴道二维超声检查怀疑IUD异常的患者行三维超声成像检查,通过对子宫腔与IUD的三维超声图像重建,明确IUD的形状与位置,将三维超声成诊断结果与宫腔镜,腹腔镜或剖腹诊治结果进行对照。结果：29例患者（96．7％）获取满意的三维超声声像图,可清晰显示IUD的形状,位置及其与子宫腔的关系;1例因绝经9年,子宫内菲薄而未能获取满意声像图,仅显示IUD嵌顿而无法显示IUD片段与宫腔的关系,诊断IUD完整者17例（单金属圆环10例,γ型IUD3例,宫腔型IUD2例,T型IUD2例）,IUD断裂者13例;IUD嵌顿24例,γ型IUD转位3例,宫腔型IUD转位1例,正常位置T型IUD2型,28例IUD异常患者中26例行宫腔镜,腹腔镜或剖腹取器手术,均一次取器成功,术中所见与三维超声诊断结果一致。结论：三维超声成像技术对IUD的定型和定位诊断具有很高的价值。对临床治疗方案的选择有指导意义。     

骨科机器人系统全程规划模块在长骨骨折精确牵引中的研究

目的 评价新设计的骨科机器人系统模块的精度及临床可行性。方法 针对9例塑料胫骨模型,测量拼接图像上的胫骨全长,并同模型的实际长度进行比较,记录差值,统计分析拼接精度。针对1例尸体双下肢胫骨标本,人为制造胫骨骨折（伴有短缩成角畸形）,拼接出骨折后的整条胫骨图像,在拼接图像上进行全程规划,确定骨折牵引距离,利用胫骨牵引支架进行自动化的定量闭合牵引,分析该模块的精度和有效性。同时,利用视频相机跟踪牵引动作中胫骨的长度变化,确保牵引过程的安全。针对1例胫骨骨折临床病例,进行拼接、规划和牵引,验证本模块的临床可行性。结果 拼接一幅完整的胫骨图像需要术中采集7—10幅C臂图像,图像拼接精度为1．5mm。图像采集的平均操作时间为1．5min,拼接与规划的时间约为3min,牵引支架安装和牵引操作的平均时间为4min。尸体标本和临床试验在牵引后均达到骨折端复位准确,符合手术要求。结论 科机器人系统全程规划模块能够为长骨骨折治疗提供有效、精确的牵引复位信息。该模块操作简单,并能达到微创手术的目的,同时还大大减少了手术中医生的X线放射损害。     

2005-2010年柳州市结核病管理信息系统报告情况分析

目的对结核病管理信息系统中柳州市肺结核报告情况进行分析,为真实了解和掌握柳州市肺结核疫情动态提供依据。方法通过《中国疾病预防控制信息系统》的子系统《结核病管理信息系统》获取柳州市2005-2010年的肺结核病人报告数据并进行比较分析。结果 2005-2010年柳州市肺结核及疑似病人报告数约为10 000例/年,但经结防机构追踪查重和排除后,实际的病人数由2005年的9 758例降至2010年的5 240例,下降了46.30%,肺结核报告发病率由2005年的275.26/10万降至2010年的142.56/10万。结论柳州市疫情报告的肺结核病人普遍存在误报、重报,这些误报信息导致柳州市肺结核报告数居高不下,影响了柳州市结核病预防控制策略和措施的制订。应加强医疗机构的诊断培训,进一步规范和提高疫情报告质量,控制肺结核的误报数和重报数,使柳州市肺结核报告数能真实反映疫情变化。     

Research and applications: Patient-level temporal aggregation for text-based asthma status ascertainment

Objective

To specify the problem of patient-level temporal aggregation from clinical text and introduce several probabilistic methods for addressing that problem. The patient-level perspective differs from the prevailing natural language processing (NLP) practice of evaluating at the term, event, sentence, document, or visit level.

Methods

We utilized an existing pediatric asthma cohort with manual annotations. After generating a basic feature set via standard clinical NLP methods, we introduce six methods of aggregating time-distributed features from the document level to the patient level. These aggregation methods are used to classify patients according to their asthma status in two hypothetical settings: retrospective epidemiology and clinical decision support.

Results

In both settings, solid patient classification performance was obtained with machine learning algorithms on a number of evidence aggregation methods, with Sum aggregation obtaining the highest F₁ score of 85.71% on the retrospective epidemiological setting, and a probability density function-based method obtaining the highest F₁ score of 74.63% on the clinical decision support setting. Multiple techniques also estimated the diagnosis date (index date) of asthma with promising accuracy.

Discussion

The clinical decision support setting is a more difficult problem. We rule out some aggregation methods rather than determining the best overall aggregation method, since our preliminary data set represented a practical setting in which manually annotated data were limited.

Conclusion

Results contrasted the strengths of several aggregation algorithms in different settings. Multiple approaches exhibited good patient classification performance, and also predicted the timing of estimates with reasonable accuracy.     

CR与DR系统的临床应用与成像对比

目的：比较计算机X线摄影系统（comuted radiograhy,CR）与数字X线摄影系统（digital radiograhy,DR）成像结果及其在临床摄影工作中的应用价值。方法：随机抽取我院2008年1月～2010年1月CR、DR胶片共1809张。并按评片标准对其进行质量评估和分类（甲级,乙级和丙级）,统计各级片所占比率,比较CR与DR的废片率。结果：DR的合格率为99.78%,其中优质片率为96.32%,废片率为0.22%。CR的合格率为98.91%,其中优质片率为91.67%,废片率为1.09%。DR优质片率明显高于CR。结论：DR在临床工作中明显优于CR。     

医学教学进度网络管理系统的设计与主要技术

文章分析了建立医学教学进度网络管理系统的必要性,同时介绍了运用ASP和Access数据库构建医学教学进度网络管理系统的思路和方法、用户群组和功能、数据库设计与结构以及缓存变量、函数与过程、批量新增与修改、组合查询等主要技术。应用实践证明,该系统实用有效。     

基于网络的毕业生就业信息管理系统的设计与开发

针对目前一些高校使用的《全国高校毕业生就业信息管理系统1.1版》存在的不足,构建基于网络的高校毕业生就业信息管理系统,以提高毕业生就业管理的工作效率,构建一个毕业生、毕业生所在学院（系）和学校就业管理部门多方信息沟通的综合管理网络平台,将其设计、开发过程进行简要介绍。     

Magnetic resonance imaging: present and future applications

Magnetic resonance (MR) imaging has created considerable excitement in the medical community, largely because of its great potential to diagnose and characterize many different disease processes. However, it is becoming increasingly evident that, because MR imaging is similar to computed tomography (CT) scanning in identifying structural disorders and because it is more costly and difficult to use, this highly useful technique must be judged against CT before it can become an accepted investigative tool. At present MR imaging has demonstrated diagnostic superiority over CT in a limited number of important, mostly neurologic, disorders and is complementary to CT in the diagnosis of certain other disorders. For most of the remaining organ systems its usefulness is not clear, but the lack of ionizing radiation and MR's ability to produce images in any tomographic plane may eventually prove to be advantageous. The potential of MR imaging to display in-vivo spectra, multinuclear images and blood-flow data makes it an exciting investigative technique. At present, however, MR imaging units should be installed only in medical centres equipped with the clinical and basic research facilities that are essential to evaluate the ultimate role of this technique in the care of patients.