Temporal analysis of tumor heterogeneity and volume for cervical cancer treatment outcome prediction: preliminary evaluation

In this paper, we present a method of quantifying the heterogeneity of cervical cancer tumors for use in radiation treatment outcome prediction. Features based on the distribution of masked wavelet decomposition coefficients in the tumor region of interest (ROI) of temporal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) studies were used along with the imaged tumor volume to assess the response of the tumors to treatment. The wavelet decomposition combined with ROI masking was used to extract local intensity variations in the tumor. The developed method was tested on a data set consisting of 23 patients with advanced cervical cancer who underwent radiation therapy; 18 of these patients had local control of the tumor, and five had local recurrence. Each patient participated in two DCE-MRI studies: one prior to treatment and another early into treatment (2-4 weeks). An outcome of local control or local recurrence of the tumor was assigned to each patient based on a posttherapy follow-up at least 2 years after the end of treatment. Three different supervised classifiers were trained on combinational subsets of the full wavelet and volume feature set. The best-performing linear discriminant analysis (LDA) and support vector machine (SVM) classifiers each had mean prediction accuracies of 95.7%, with the LDA classifier being more sensitive (100% vs. 80%) and the SVM classifier being more specific (100% vs. 94.4%) in those cases. The K-nearest neighbor classifier performed the best out of all three classifiers, having multiple feature sets that were used to achieve 100% prediction accuracy. The use of distribution measures of the masked wavelet coefficients as features resulted in much better predictive performance than those of previous approaches based on tumor intensity values and their distributions or tumor volume alone.

Two-Point Correlation as a Feature for Histology Images: Feature Space Structure and Correlation Updating

The segmentation of tissues in whole-slide histology images is a necessary step for the morphological analyses of tissues and cellular structures. Previous works have demonstrated the potential of two-point correlation functions (TPCF) as features for tissue segmentation, however the feature space is not yet well understood and computational methods are lacking. This paper illustrates several fundamental aspects of TPCF feature space and contributes a fast algorithm for deterministic feature computation. Despite the high-dimensionality of TPCF feature space, the features corresponding to different tissues are shown to be characterized by low-dimensional manifolds. The relationship between TPCF and the familiar co-occurrence matrix is highlighted, and it is shown that costly cross correlations are not necessary to achieve an accurate segmentation. For computation, the method of correlation updating, based on the linearity of the correlation operator, is proposed and shown to achieve up to a 67X speedup over frequency domain computation methods. Segmentation results are demonstrated on multiple tissues and natural texture images.

Managing and Querying Image Annotation and Markup in XML

Proprietary approaches for representing annotations and image markup are serious barriers for researchers to share image data and knowledge. The Annotation and Image Markup (AIM) project is developing a standard based information model for image annotation and markup in health care and clinical trial environments. The complex hierarchical structures of AIM data model pose new challenges for managing such data in terms of performance and support of complex queries. In this paper, we present our work on managing AIM data through a native XML approach, and supporting complex image and annotation queries through native extension of XQuery language. Through integration with xService, AIM databases can now be conveniently shared through caGrid.

HIV-K: an integrative knowledge base for semantic integration of AIDS-related malignancy data and treatment outcomes

Technological innovations such as web services and collaborative Grid platforms like caGrid can create opportunities to converge the worlds of health care and clinical research, by facilitating access and integration of HIV-related malignancy clinical and outcomes data at more sophisticated, semantic levels. At the same time, large numbers of randomized clinical trial and outcomes data on AIDS-defining malignancies (ADM) and non-AIDS-defining malignancies (nADM) have been produced during the last few years. There is still much work to do, though, on obtaining clear conclusions from the integration of such information. This is a white paper on work in progress from Emory University's HIV/AIDS related malignancy data integrative knowledge base project (HIV-K). We are working to increase the understanding of available clinical trial data and outcomes of ADM such as lymphoma, as well as nADM such as anal cancer, Hodgkin lymphoma, or liver cancer. Our hypothesis is that, by creating prototypes of tools for semantics-enabled integrative knowledge bases for HIV/AIDS-related malignancy data, we will facilitate the identification of patterns and potential new overall evidence, as well as the linking of integrated data and results to registries of interest.

Stroma classification for neuroblastoma on graphics processors

Neuroblastoma is one of the most common childhood cancers. We are developing an image analysis system to assist pathologists in their prognosis. Since this system operates on relatively large-scale images and requires sophisticated algorithms, computerised analysis takes a long time to execute. In this paper, we propose a novel approach to benefit from high memory bandwidth and strong floating-point capabilities of graphics processing units. The proposed approach achieves a promising classification accuracy of 99.4% and an execution performance with a gain factor up to 45 times compared to hand-optimised C++ code running on the CPU.

HPC AND GRID COMPUTING FOR INTEGRATIVE BIOMEDICAL RESEARCH

Integrative biomedical research projects query, analyze, and integrate many different data types and make use of datasets obtained from measurements or simulations of structure and function at multiple biological scales. With the increasing availability of high-throughput and high-resolution instruments, the integrative biomedical research imposes many challenging requirements on software middleware systems. In this paper, we look at some of these requirements using example research pattern templates. We then discuss how middleware systems, which incorporate Grid and high-performance computing, could be employed to address the requirements.

A knowledge-anchored integrative image search and retrieval system

Clinical data that may be used in a secondary capacity to support research activities are regularly stored in three significantly different formats: (1) structured, codified data elements; (2) semi-structured or unstructured narrative text; and (3) multi-modal images. In this manuscript, we will describe the design of a computational system that is intended to support the ontology-anchored query and integration of such data types from multiple source systems. Additional features of the described system include (1) the use of Grid services-based electronic data interchange models to enable the use of our system in multi-site settings and (2) the use of a software framework intended to address both potential security and patient confidentiality concerns that arise when transmitting or otherwise manipulating potentially privileged personal health information. We will frame our discussion within the specific experimental context of the concept-oriented query and integration of correlated structured data, narrative text, and images for cancer research.

VirtualPACS--a federating gateway to access remote image data resources over the grid

Collaborations in biomedical research and clinical studies require that data, software, and computational resources be shared between geographically distant institutions. In radiology, there is a related issue of sharing remote DICOM data over the Internet. This paper focuses on the problem of federating multiple image data resources such that clients can interact with them as if they are stored in a centralized PACS. We present a toolkit, called VirtualPACS, to support this functionality. Using the toolkit, users can perform standard DICOM operations (query, retrieve, and submit) across distributed image databases. The key features of the toolkit are: (1) VirtualPACS makes it easy to use existing DICOM client applications for data access; (2) it can easily be incorporated into an imaging workflow as a DICOM source; (3) using VirtualPACS, heterogeneous collections of DICOM sources are exposed to clients through a uniform interface and common data model; and (4) DICOM image databases without DICOM messaging can be accessed.

Tensor classification of N-point correlation function features for histology tissue segmentation

In this paper, we utilize the N-point correlation functions (N-pcfs) to construct an appropriate feature space for achieving tissue segmentation in histology-stained microscopic images. The N-pcfs estimate microstructural constituent packing densities and their spatial distribution in a tissue sample. We represent the multi-phase properties estimated by the N-pcfs in a tensor structure. Using a variant of higher-order singular value decomposition (HOSVD) algorithm, we realize a robust classifier that provides a multi-linear description of the tensor feature space. Validated results of the segmentation are presented in a case-study that focuses on understanding the genetic phenotyping differences in mouse placentae.

Architectural Implications for Spatial Object Association Algorithms

Spatial object association, also referred to as crossmatch of spatial datasets, is the problem of identifying and comparing objects in two or more datasets based on their positions in a common spatial coordinate system. In this work, we evaluate two crossmatch algorithms that are used for astronomical sky surveys, on the following database system architecture configurations: (1) Netezza Performance Server^®, a parallel database system with active disk style processing capabilities, (2) MySQL Cluster, a high-throughput network database system, and (3) a hybrid configuration consisting of a collection of independent database system instances with data replication support. Our evaluation provides insights about how architectural characteristics of these systems affect the performance of the spatial crossmatch algorithms. We conducted our study using real use-case scenarios borrowed from a large-scale astronomy application known as the Large Synoptic Survey Telescope (LSST).

An Integrated Framework for Parameter-based Optimization of Scientific Workflows

Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not a ect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework.

Parallel Materialization of Large ABoxes

This paper is concerned with the efficient computation of materialization in a knowledge base with a large ABox. We present a framework for performing this task on a shared-nothing parallel machine. The framework partitions TBox and ABox axioms using a min-min strategy. It utilizes an existing system, like SwiftOWLIM, to perform local inference computations and coordinates exchange of relevant information between processors. Our approach is able to exploit parallelism in the axioms of the TBox to achieve speedup in a cluster. However, this approach is limited by the complexity of the TBox. We present an experimental evaluation of the framework using datasets from the Lehigh University Benchmark (LUBM).

An imaging workflow for characterizing phenotypical change in large histological mouse model datasets

MOTIVATION

This paper presents a workflow designed to quantitatively characterize the 3D structural attributes of macroscopic tissue specimens acquired at a micron level resolution using light microscopy. The specific application is a study of the morphological change in a mouse placenta induced by knocking out the retinoblastoma gene.

RESULT

This workflow includes four major components: (i) serial section image acquisition, (ii) image preprocessing, (iii) image analysis involving 2D pair-wise registration, 2D segmentation and 3D reconstruction, and (iv) visualization and quantification of phenotyping parameters. Several new algorithms have been developed within each workflow component. The results confirm the hypotheses that (i) the volume of labyrinth tissue decreases in mutant mice with the retinoblastoma (Rb) gene knockout and (ii) there is more interdigitation at the surface between the labyrinth and spongiotrophoblast tissues in mutant placenta. Additional confidence stem from agreement in the 3D visualization and the quantitative results generated.

AVAILABILITY

The source code is available upon request.

e-Science, caGrid, and Translational Biomedical Research

Translational research projects target a wide variety of diseases, test many different kinds of biomedical hypotheses, and employ a large assortment of experimental methodologies. Diverse data, complex execution environments, and demanding security and reliability requirements make the implementation of these projects extremely challenging and require novel e-Science technologies.

Reconstruction of cellular biological structures from optical microscopy data

Developments in optical microscopy imaging have generated large high-resolution data sets that have spurred medical researchers to conduct investigations into mechanisms of disease, including cancer at cellular and subcellular levels. The work reported here demonstrates that a suitable methodology can be conceived that isolates modality-dependent effects from the larger segmentation task and that 3D reconstructions can be cognizant of shapes as evident in the available 2D planar images. In the current realization, a method based on active geodesic contours is first deployed to counter the ambiguity that exists in separating overlapping cells on the image plane. Later, another segmentation effort based on a variant of Voronoi tessellations improves the delineation of the cell boundaries using a Bayesian formulation. In the next stage, the cells are interpolated across the third dimension thereby mitigating the poor structural correlation that exists in that dimension. We deploy our methods on three separate data sets obtained from light, confocal, and phase-contrast microscopy and validate the results appropriately.

Image analysis for neuroblastoma classification: segmentation of cell nuclei

Neuroblastoma is a childhood cancer of the nervous system. Current prognostic classification of this disease partly relies on morphological characteristics of the cells from H&E-stained images. In this work, an automated cell nuclei segmentation method is developed. This method employs morphological top-hat by reconstruction algorithm coupled with hysteresis thresholding to both detect and segment the cell nuclei. Accuracy of the automated cell nuclei segmentation algorithm is measured by comparing its outputs to manual segmentation. The average segmentation accuracy is 90.24+/-5.14%

Translational Research Design Templates, Grid Computing, and HPC

Design templates that involve discovery, analysis, and integration of information resources commonly occur in many scientific research projects. In this paper we present examples of design templates from the biomedical translational research domain and discuss the requirements imposed on Grid middleware infrastructures by them. Using caGrid, which is a Grid middleware system based on the model driven architecture (MDA) and the service oriented architecture (SOA) paradigms, as a starting point, we discuss architecture directions for MDA and SOA based systems like caGrid to support common design templates.

A Run-time System for Efficient Execution of Scientific Workflows on Distributed Environments

Scientific workflow systems have been introduced in response to the demand of researchers from several domains of science who need to process and analyze increasingly larger datasets. The design of these systems is largely based on the observation that data analysis applications can be composed as pipelines or networks of computations on data. In this work, we present a runtime support system that is designed to facilitate this type of computation in distributed computing environments. Our system is optimized for data-intensive workflows, in which efficient management and retrieval of data, coordination of data processing and data movement, and check-pointing of intermediate results are critical and challenging issues. Experimental evaluation of our system shows that linear speedups can be achieved for sophisticated applications, which are implemented as a network of multiple data processing components.

caGrid 1.0: an enterprise Grid infrastructure for biomedical research

OBJECTIVE

To develop software infrastructure that will provide support for discovery, characterization, integrated access, and management of diverse and disparate collections of information sources, analysis methods, and applications in biomedical research.

DESIGN

An enterprise Grid software infrastructure, called caGrid version 1.0 (caGrid 1.0), has been developed as the core Grid architecture of the NCI-sponsored cancer Biomedical Informatics Grid (caBIG) program. It is designed to support a wide range of use cases in basic, translational, and clinical research, including 1) discovery, 2) integrated and large-scale data analysis, and 3) coordinated study.

MEASUREMENTS

The caGrid is built as a Grid software infrastructure and leverages Grid computing technologies and the Web Services Resource Framework standards. It provides a set of core services, toolkits for the development and deployment of new community provided services, and application programming interfaces for building client applications.

RESULTS

The caGrid 1.0 was released to the caBIG community in December 2006. It is built on open source components and caGrid source code is publicly and freely available under a liberal open source license. The core software, associated tools, and documentation can be downloaded from the following URL: https://cabig.nci.nih.gov/workspaces/Architecture/caGrid.

CONCLUSIONS

While caGrid 1.0 is designed to address use cases in cancer research, the requirements associated with discovery, analysis and integration of large scale data, and coordinated studies are common in other biomedical fields. In this respect, caGrid 1.0 is the realization of a framework that can benefit the entire biomedical community.

Sharing data and analytical resources securely in a biomedical research Grid environment

OBJECTIVES

To develop a security infrastructure to support controlled and secure access to data and analytical resources in a biomedical research Grid environment, while facilitating resource sharing among collaborators.

DESIGN

A Grid security infrastructure, called Grid Authentication and Authorization with Reliably Distributed Services (GAARDS), is developed as a key architecture component of the NCI-funded cancer Biomedical Informatics Grid (caBIG). The GAARDS is designed to support in a distributed environment 1) efficient provisioning and federation of user identities and credentials; 2) group-based access control support with which resource providers can enforce policies based on community accepted groups and local groups; and 3) management of a trust fabric so that policies can be enforced based on required levels of assurance.

MEASUREMENTS

GAARDS is implemented as a suite of Grid services and administrative tools. It provides three core services: Dorian for management and federation of user identities, Grid Trust Service for maintaining and provisioning a federated trust fabric within the Grid environment, and Grid Grouper for enforcing authorization policies based on both local and Grid-level groups.

RESULTS

The GAARDS infrastructure is available as a stand-alone system and as a component of the caGrid infrastructure. More information about GAARDS can be accessed at http://www.cagrid.org.

CONCLUSIONS

GAARDS provides a comprehensive system to address the security challenges associated with environments in which resources may be located at different sites, requests to access the resources may cross institutional boundaries, and user credentials are created, managed, revoked dynamically in a de-centralized manner.

A roadmap for caGrid, an enterprise Grid architecture for biomedical research

caGrid is a middleware system which combines the Grid computing, the service oriented architecture, and the model driven architecture paradigms to support development of interoperable data and analytical resources and federation of such resources in a Grid environment. The functionality provided by caGrid is an essential and integral component of the cancer Biomedical Informatics Grid (caBIG) program. This program is established by the National Cancer Institute as a nationwide effort to develop enabling informatics technologies for collaborative, multi-institutional biomedical research with the overarching goal of accelerating translational cancer research. Although the main application domain for caGrid is cancer research, the infrastructure provides a generic framework that can be employed in other biomedical research and healthcare domains. The development of caGrid is an ongoing effort, adding new functionality and improvements based on feedback and use cases from the community. This paper provides an overview of potential future architecture and tooling directions and areas of improvement for caGrid and caGrid-like systems. This summary is based on discussions at a roadmap workshop held in February with participants from biomedical research, Grid computing, and high performance computing communities.

The Cancer Biomedical Informatics Grid (caBIG) Security Infrastructure

Security is a high priority issue in medical domain, because many institutions performing biomedical research work with sensitive medical data regularly. This issue becomes more complicated, when it is desirable or needed to access and analyze data in a multi-institutional setting. In the NCI cancer Biomedical Informatics Grid (caBIG) program, several security issues were raised that existing security technologies could not address. Considering caBIG is envisioned to span a large number of cancer centers and investigator laboratories, these issues pose considerable challenge. In this paper we present these issues and the infrastructure, referred to as GAARDS, which has been developed to address them.

caGrid 1.0: a Grid enterprise architecture for cancer research

caGrid is the core Grid architecture of the NCI-sponsored cancer Biomedical Informatics Grid (caBIG) program. The current release, caGrid version 1.0, is developed as the production Grid software infrastructure of caBIG. Based on feedback from adopters of the previous version (caGrid 0.5), it has been significantly enhanced with new features and improvements to existing components. This paper presents an overview of caGrid 1.0, its main components, and enhancements over caGrid 0.5.

Computerized pathological image analysis for neuroblastoma prognosis

We present a pathological image analysis system for the computer-aided prognosis of neuroblastoma, a childhood cancer. The image analysis system automatically classifies Schwannian stromal development of pathological tissues and determines the grade of differentiation. Due to the demanding computational cost of processing large digitized slides, the system was implemented on a cluster of computers with automated load balancing within a multi-resolution framework. In our experiments, the overall accuracies for stromal classification and the grade of differentiation were 96.6% and 95.3%, respectively. Additionally, the multi-resolution framework reduced the run time of the single resolution approach by 53% and 34% on average for stromal classification and grade of differentiation, respectively. For these two cases, parallelization on a 16-node cluster reduced the sequential run time by 92% and 88% on average. Accuracy and efficiency of these techniques are promising for the development a computer-assisted neuroblastoma prognosis system.

GridIMAGE: a novel use of grid computing to support interactive human and computer-assisted detection decision support

This paper describes a Grid-aware image reviewing system (GridIMAGE) that allows practitioners to (a) select images from multiple geographically distributed digital imaging and communication in medicine (DICOM) servers, (b) send those images to a specified group of human readers and computer-assisted detection (CAD) algorithms, and (c) obtain and compare interpretations from human readers and CAD algorithms. The currently implemented system was developed using the National Cancer Institute caGrid infrastructure and is designed to support the identification of lung nodules on thoracic computed tomography. However, the infrastructure is general and can support any type of distributed review. caGrid data and analytical services are used to link DICOM image databases and CAD systems and to interact with human readers. Moreover, the service-oriented and distributed structure of the GridIMAGE framework enables a flexible system, which can be deployed in an institution (linking multiple DICOM servers and CAD algorithms) and in a Grid environment (linking the resources of collaborating research groups). GridIMAGE provides a framework that allows practitioners to obtain interpretations from one or more human readers or CAD algorithms. It also provides a mechanism to allow cooperative imaging groups to systematically perform image interpretation tasks associated with research protocols.

Detection and visualization of surface-pockets to enable phenotyping studies

In this paper, we propose a technique for detecting pockets on a surface-of-interest. A sequence of propagating fronts converging to the target surface is used as the basis for inspection. We compute a correspondence function between the initial and the target surface. This leads to a natural definition of the local feature size measured as the evolution distance between mapped points. Surface pockets are then extracted as salient clusters embedded in the feature space. The level-set initialization also determines the scale-space of the extracted pockets. Results are presented on a case-study in which the focus is to chronicle the phenotyping differences in genetically modified mouse placenta. Our results are validated based on manually verified ground-truth.

Preventing signal degradation during elastic matching of noisy DCE-MR eye images

Motion during the acquisition of dynamic contrast enhanced MRI can cause model-fitting errors requiring co-registration. Clinical implementations use a pharmacokinetic model to determine lesion parameters from the contrast passage. The input to the model is the time-intensity plot from a region of interest (ROI) covering the lesion extent. Motion correction meanwhile involves interpolation and smoothing operations thereby affecting the time-intensity plots. This paper explores the trade-offs in applying an elastic matching procedure on the lesion detection and proposes enhancements. The method of choice is the 3D realization of the Demon's elastic matching procedure. We validate our enhancements using synthesized deformation of stationary datasets that also serve as ground-truth. The framework is tested on 42 human eye datasets. Hence, we show that motion correction is beneficial in improving the model-fit and yet needs enhancements to correct for the intensity reductions during parameter estimation.

Rb is critical in a mammalian tissue stem cell population

The inactivation of the retinoblastoma (Rb) tumor suppressor gene in mice results in ectopic proliferation, apoptosis, and impaired differentiation in extraembryonic, neural, and erythroid lineages, culminating in fetal death by embryonic day 15.5 (E15.5). Here we show that the specific loss of Rb in trophoblast stem (TS) cells, but not in trophoblast derivatives, leads to an overexpansion of trophoblasts, a disruption of placental architecture, and fetal death by E15.5. Despite profound placental abnormalities, fetal tissues appeared remarkably normal, suggesting that the full manifestation of fetal phenotypes requires the loss of Rb in both extraembryonic and fetal tissues. Loss of Rb resulted in an increase of E2f3 expression, and the combined ablation of Rb and E2f3 significantly suppressed Rb mutant phenotypes. This rescue appears to be cell autonomous since the inactivation of Rb and E2f3 in TS cells restored placental development and extended the life of embryos to E17.5. Taken together, these results demonstrate that loss of Rb in TS cells is the defining event causing lethality of Rb(-/-) embryos and reveal the convergence of extraembryonic and fetal functions of Rb in neural and erythroid development. We conclude that the Rb pathway plays a critical role in the maintenance of a mammalian stem cell population.

caGrid: design and implementation of the core architecture of the cancer biomedical informatics grid

MOTIVATION

The complexity of cancer is prompting researchers to find new ways to synthesize information from diverse data sources and to carry out coordinated research efforts that span multiple institutions. There is a need for standard applications, common data models, and software infrastructure to enable more efficient access to and sharing of distributed computational resources in cancer research. To address this need the National Cancer Institute (NCI) has initiated a national-scale effort, called the cancer Biomedical Informatics Grid (caBIGtrade mark), to develop a federation of interoperable research information systems.

RESULTS

At the heart of the caBIG approach to federated interoperability effort is a Grid middleware infrastructure, called caGrid. In this paper we describe the caGrid framework and its current implementation, caGrid version 0.5. caGrid is a model-driven and service-oriented architecture that synthesizes and extends a number of technologies to provide a standardized framework for the advertising, discovery, and invocation of data and analytical resources. We expect caGrid to greatly facilitate the launch and ongoing management of coordinated cancer research studies involving multiple institutions, to provide the ability to manage and securely share information and analytic resources, and to spur a new generation of research applications that empower researchers to take a more integrative, trans-domain approach to data mining and analysis.

AVAILABILITY

The caGrid version 0.5 release can be downloaded from https://cabig.nci.nih.gov/workspaces/Architecture/caGrid/. The operational test bed Grid can be accessed through the client included in the release, or through the caGrid-browser web application http://cagrid-browser.nci.nih.gov.

Distinct proteome features of plasma microparticles

Plasma microparticles (MPs) are spherical cell membrane fragments derived from either apoptotic or activated cells. Characterized by a rich phospholipid moiety and many protein constituents, MPs normally circulate in the blood and contribute to numerous physiological processes. In disease states, MPs derived from the injured organ likely contain valuable markers for determining the site, type, and extent of disease pathology. However, the basic protein characteristics of plasma MPs have yet to be described. In this study, MPs from a pooled plasma sample derived from 16 healthy donors, all of group A blood type, were prepared by ultracentrifugation. Flow cytometry confirmed that a majority of these MPs are smaller than 1 microm. Factor Xa generation assay revealed the presence of tissue factor activity in these MPs, confirming MPs' role in initiating blood coagulation. The MP proteome was analyzed by two-dimensional (2-D) gel electrophoresis performed in triplicate, and compared with a 2-D gel of pooled whole plasma and blood platelets. Overall, plasma MPs displayed distinct protein features and a greater number of protein spots (1021-1055) than that detected in whole plasma (331-370). Protein spots expressed in high abundance in the MP proteome were then excised and submitted for protein identity determination. This process provided protein identification for 169 protein spots and reported their relative protein quantities within the MP proteome. These 169 protein spots represented 83 different proteins and their respective isoforms. Thirty of these proteins have never before been reported in previous proteome analyses of human plasma. These results provide unprecedented information on the MP proteome and create a basis for future studies to understand MP biology and pathophysiology.

The virtual microscope

We present the design and implementation of the Virtual Microscope, a software system employing a client/server architecture to provide a realistic emulation of a high power light microscope. The system provides a form of completely digital telepathology, allowing simultaneous access to archived digital slide images by multiple clients. The main problem the system targets is storing and processing the extremely large quantities of data required to represent a collection of slides. The Virtual Microscope client software runs on the end user's PC or workstation, while database software for storing, retrieving and processing the microscope image data runs on a parallel computer or on a set of workstations at one or more potentially remote sites. We have designed and implemented two versions of the data server software. One implementation is a customization of a database system framework that is optimized for a tightly coupled parallel machine with attached local disks. The second implementation is component-based, and has been designed to accommodate access to and processing of data in a distributed, heterogeneous environment. We also have developed caching client software, implemented in Java, to achieve good response time and portability across different computer platforms. The performance results presented show that the Virtual Microscope systems scales well, so that many clients can be adequately serviced by an appropriately configured data server.

Information warehouse as a tool to analyze Computerized Physician Order Entry order set utilization: opportunities for improvement

A Computerized Physician order entry (CPOE) system was successfully implemented at the Ohio State University Medical Center (OSUMC) in February 2000. The electronic entry and use of order sets is designed to standardize patient care and improve efficiency and patient safety. To evaluate the effectiveness of the CPOE system and to maximize its benefits, one needs to easily access and analyze the data. Since the CPOE system is not equipped to support such on demand analysis, the data from the system is extracted daily into the OSUMC's Information Warehouse (IW). This allows the CPOE data to be linked with other clinical and financial patient information in the IW to provide detailed and comprehensive analysis. Our focus in this paper is the use of the IW as a tool to analyze order set usage patterns and the opportunities such analysis provides for improvements in education, resource utilization and patient care.

Proteomic patterns analysis: a new era of screening cancers

Cancer is the second leading cause of death among Americans. It is estimated that 1.28 million new Americans are diagnosed with cancer annually (1). The estimated overall annual cost of cancer being $171 Billion (1). Decreasing the costs of the screening and diagnostic tests will automatically decrease the total cost of cancer by limiting not only the direct medical costs but also by containing the indirect costs of morbidity and mortality. New screening and diagnostic tests are obviously needed. Screening methods are emerging in the evaluation of proteomic patterns. In proteomic pattern analysis, we can screen for not only one cancer but a chip may be able to screen for multiple cancers. New screening and diagnostic methods (2) investigated by NCI and FDA (3) (4) are correlating gene and protein expression patterns for early detection of cancer. Many papers have been published in the last 12 months (3) (4) (5) utilizing this new technique of molecular analysis in screening and diagnosing cancers with high sensitivity and specificity.

Impact of CPOE order sets on lab orders

Introduction of the computerized physician order (CPOE) is intended to promote best practices, decrease practice variation among practitioners, and optimize the utilization of resources consistent with evidence based practice guidelines. Implicit in the use of CPOE is the assumption that the use of order sets might decrease utilization of resources such as the ordering of unnecessary laboratory tests. Conversely compliance with practice guidelines may necessitate ordering of certain tests that are deemed to be consistent with the good practice of medicine. In order to develop an understanding of these issues, we compared the utilization of laboratory orders prior to and following implementation of order sets in CPOE. In addition, we analyzed the impact of CPOE on the timely placement of certain orders based on critical levels of some laboratory results, in this instance potassium.

Order sets utilization in a clinical order entry system

An order set is a predefined template that has been utilized in the standard care of hospitals for many years. While in the past, it took the form of pen and paper, today, it is, indeed, electronic. Within order sets are distinct ordering patterns that may yield fruitful results for clinicians and informaticians, alike. Protocols like there electronic counterpart, order sets, provide an 'indication' identifying the clinical scenario of the patient's condition when the ordering event occurred. This 'indication' is rarely captured by individual orders, and provides difficult challenges to developers of information systems. While mandating an 'indication' be entered for every medication or lab order makes the job much more tasking on the physician provider, it is appealing to researchers and accountants. We have attempted to bypasses that consideration by identifying ordering patterns that predict diagnostic related codes (DRGs) and diagnostic codes which would greatly facilitate the information gathering process and still provide a flexible and user friendly physician interface.

Development of a universal connectivity and data management system

Point-of-care testing (POCT) is an increasingly popular method of delivering laboratory testing. Management of POCT is challenging given the variety of devices, locations, and staff that need to be coordinated to ensure quality results and meet regulatory guidelines. Electronic capture and transfer of data are preferred for managing POCT, but there is currently no standard method of connecting different devices. Johns Hopkins Medical Institutions (JHMI) developed a common data management system with interfaces to all of its POCT devices. All POCT data are collected in one database and analyzed in a similar fashion. Where data were once collected by carrying laptops to each nursing unit, the POCT devices can now connect directly to the database over the Internet. Algorithms have been created to automate the data analysis and review process. Over the several years that this software has been used, JHMI has experienced improved quality, accuracy, and management of its POCT program. The labor saved by increased automation of data review is refocused on enhancing the performance and scope of the program. Current connectivity and analysis algorithms have future application to remote consultation, management of home self-monitoring patients, and examination of real-time data.

Digital dynamic telepathology--the Virtual Microscope

The Virtual Microscope is being designed as an integrated computer hardware and software system that generates a highly realistic digital simulation of analog, mechanical light microscopy. We present our work over the past year in meeting the challenges in building such a system. The enhancements we made are discussed, as well as the planned future improvements. Performance results are provided showing the system scales well, so that many users can be adequately serviced by an appropriately configured data server.

A graphical tool for ad hoc query generation

Medical data are characterized by complex taxonomies and evolving terminology. Questions that clinicians, medical administrators, and researchers may wish to answer using medical databases are not easily formulated as SQL queries. In this paper we describe a graphical tool that facilitates formulation of ad hoc questions as SQL queries. This tool manages multiple attribute hierarchies and creates SQL query strings by navigating through the hierarchies. This interactive tool has been optimized using indexing to improve the overall speed of the query building and the data retrieval process. Indexed queries performed 5 to 100 times faster than query strings. However, query string generation time depends on the size of the taxonomies describing the hierarchies, while the index generation time depends on the size of the data warehouse.

The Virtual Microscope

We present the design of the Virtual Microscope, a software system employing a client/server architecture to provide a realistic emulation of a high power light microscope. We discuss several technical challenges related to providing the performance necessary to achieve rapid response time, mainly in dealing with the enormous amounts of data (tens to hundreds of gigabytes per slide) that must be retrieved from secondary storage and processed. To effectively implement the data server, the system design relies on the computational power and high I/O throughput available from an appropriately configured parallel computer.

The prognostic significance of recall antigen testing in melanoma patients

A quantitative assessment of the long-term prognostic value and clinical usefulness of recall antigen reactions in patients with malignant melanoma is not available. The authors evaluated longitudinal observations of survival made in 846 patients over a 12-year period. Each patient was initially studied with Mantoux-type recall antigen skin tests. The patients were categorized with respect to the following: high (greater than 5 mm) or low (less than or equal to 5 mm) averaged skin test reaction diameters at 48 hr; Clark level; tumor stage (I = localized tumor, II = local extension and/or region lymph node metastasis, III = systemic metastasis); ulceration; site of primary; histologic type; age; and sex. The percentage of high reactors in Stages I, II, and III were 44.3%, 37.4%, and 25%, respectively. Survival was evaluated with the Cox-Mantell hazard function model and the Cox regression model. The significant (chi-squared; probability) risk factors detected were tumor stage (94.58; less than or equal to 0.0001), Clark level (19.37; less than or equal to 0.0001), sex (16.97; less than or equal to 0.0001), and skin test reactivity (7.48; less than or equal to 0.0062). A significant relationship also was detected between skin test reactor status and the tumor stage (p less than or equal to 0.0330). When evaluated within each stage of disease, skin test reactivity predicted survival only in Stage II patients (p less than or equal to 0.0080). Five-year survival estimates among Stage II patients were 58% among high reactors and 38% among low reactors.(ABSTRACT TRUNCATED AT 250 WORDS)