Development of the Lymphoma Enterprise Architecture Database: A caBIG(tm) Silver level compliant System
Identifieur interne : 000086 ( Ncbi/Merge ); précédent : 000085; suivant : 000087Development of the Lymphoma Enterprise Architecture Database: A caBIG(tm) Silver level compliant System
Auteurs : Taoying Huang ; Pareen J. Shenoy ; Rajni Sinha ; Michael Graiser ; Kevin W. Bumpers ; Christopher R. FlowersSource :
- Cancer Informatics [ 1176-9351 ] ; 2009.
Abstract
Lymphomas are the fifth most common cancer in United States with numerous histological subtypes. Integrating existing clinical information on lymphoma patients provides a platform for understanding biological variability in presentation and treatment response and aids development of novel therapies. We developed a cancer Biomedical Informatics Grid™ (caBIG™) Silver level compliant lymphoma database, called the Lymphoma Enterprise Architecture Data-system™ (LEAD™), which integrates the pathology, pharmacy, laboratory, cancer registry, clinical trials, and clinical data from institutional databases. We utilized the Cancer Common Ontological Representation Environment Software Development Kit (caCORE SDK) provided by National Cancer Institute’s Center for Bioinformatics to establish the LEAD™ platform for data management. The caCORE SDK generated system utilizes an n-tier architecture with open Application Programming Interfaces, controlled vocabularies, and registered metadata to achieve semantic integration across multiple cancer databases. We demonstrated that the data elements and structures within LEAD™ could be used to manage clinical research data from phase 1 clinical trials, cohort studies, and registry data from the Surveillance Epidemiology and End Results database. This work provides a clear example of how semantic technologies from caBIG™ can be applied to support a wide range of clinical and research tasks, and integrate data from disparate systems into a single architecture. This illustrates the central importance of caBIG™ to the management of clinical and biological data.
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Development of the Lymphoma Enterprise Architecture Database: A caBIG(tm) Silver level compliant System</title><author><name sortKey="Huang, Taoying" sort="Huang, Taoying" uniqKey="Huang T" first="Taoying" last="Huang">Taoying Huang</name></author><author><name sortKey="Shenoy, Pareen J" sort="Shenoy, Pareen J" uniqKey="Shenoy P" first="Pareen J." last="Shenoy">Pareen J. Shenoy</name></author><author><name sortKey="Sinha, Rajni" sort="Sinha, Rajni" uniqKey="Sinha R" first="Rajni" last="Sinha">Rajni Sinha</name></author><author><name sortKey="Graiser, Michael" sort="Graiser, Michael" uniqKey="Graiser M" first="Michael" last="Graiser">Michael Graiser</name></author><author><name sortKey="Bumpers, Kevin W" sort="Bumpers, Kevin W" uniqKey="Bumpers K" first="Kevin W." last="Bumpers">Kevin W. Bumpers</name></author><author><name sortKey="Flowers, Christopher R" sort="Flowers, Christopher R" uniqKey="Flowers C" first="Christopher R." last="Flowers">Christopher R. Flowers</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">19492074</idno><idno type="pmc">2675136</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675136</idno><idno type="RBID">PMC:2675136</idno><date when="2009">2009</date><idno type="wicri:Area/Pmc/Corpus">000512</idno><idno type="wicri:Area/Pmc/Curation">000512</idno><idno type="wicri:Area/Pmc/Checkpoint">000642</idno><idno type="wicri:Area/Ncbi/Merge">000086</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Development of the Lymphoma Enterprise Architecture Database: A caBIG(tm) Silver level compliant System</title><author><name sortKey="Huang, Taoying" sort="Huang, Taoying" uniqKey="Huang T" first="Taoying" last="Huang">Taoying Huang</name></author><author><name sortKey="Shenoy, Pareen J" sort="Shenoy, Pareen J" uniqKey="Shenoy P" first="Pareen J." last="Shenoy">Pareen J. Shenoy</name></author><author><name sortKey="Sinha, Rajni" sort="Sinha, Rajni" uniqKey="Sinha R" first="Rajni" last="Sinha">Rajni Sinha</name></author><author><name sortKey="Graiser, Michael" sort="Graiser, Michael" uniqKey="Graiser M" first="Michael" last="Graiser">Michael Graiser</name></author><author><name sortKey="Bumpers, Kevin W" sort="Bumpers, Kevin W" uniqKey="Bumpers K" first="Kevin W." last="Bumpers">Kevin W. Bumpers</name></author><author><name sortKey="Flowers, Christopher R" sort="Flowers, Christopher R" uniqKey="Flowers C" first="Christopher R." last="Flowers">Christopher R. Flowers</name></author></analytic><series><title level="j">Cancer Informatics</title><idno type="eISSN">1176-9351</idno><imprint><date when="2009">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Lymphomas are the fifth most common cancer in United States with numerous histological subtypes. Integrating existing clinical information on lymphoma patients provides a platform for understanding biological variability in presentation and treatment response and aids development of novel therapies. We developed a cancer Biomedical Informatics Grid™ (caBIG™) Silver level compliant lymphoma database, called the Lymphoma Enterprise Architecture Data-system™ (LEAD™), which integrates the pathology, pharmacy, laboratory, cancer registry, clinical trials, and clinical data from institutional databases. We utilized the Cancer Common Ontological Representation Environment Software Development Kit (caCORE SDK) provided by National Cancer Institute’s Center for Bioinformatics to establish the LEAD™ platform for data management. The caCORE SDK generated system utilizes an n-tier architecture with open Application Programming Interfaces, controlled vocabularies, and registered metadata to achieve semantic integration across multiple cancer databases. We demonstrated that the data elements and structures within LEAD™ could be used to manage clinical research data from phase 1 clinical trials, cohort studies, and registry data from the Surveillance Epidemiology and End Results database. This work provides a clear example of how semantic technologies from caBIG™ can be applied to support a wide range of clinical and research tasks, and integrate data from disparate systems into a single architecture. This illustrates the central importance of caBIG™ to the management of clinical and biological data.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Zhong, Y" uniqKey="Zhong Y">Y Zhong</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jemal, A" uniqKey="Jemal A">A Jemal</name></author><author><name sortKey="Siegel, R" uniqKey="Siegel R">R Siegel</name></author><author><name sortKey="Ward, Em" uniqKey="Ward E">EM Ward</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ottensmeier, C" uniqKey="Ottensmeier C">C Ottensmeier</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Graiser, M" uniqKey="Graiser M">M Graiser</name></author><author><name sortKey="Moore, Sg" uniqKey="Moore S">SG Moore</name></author><author><name sortKey="Victor, R" uniqKey="Victor R">R 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R">RM Bradley</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Karp, Pd" uniqKey="Karp P">PD Karp</name></author><author><name sortKey="Chaudhri, Vk" uniqKey="Chaudhri V">VK Chaudhri</name></author><author><name sortKey="Thomere, J" uniqKey="Thomere J">J Thomere</name></author></analytic></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Cancer Inform</journal-id><journal-id journal-id-type="iso-abbrev">Cancer Inform</journal-id><journal-id journal-id-type="publisher-id">101258149</journal-id><journal-title-group><journal-title>Cancer Informatics</journal-title></journal-title-group><issn pub-type="epub">1176-9351</issn><publisher><publisher-name>Libertas Academica</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">19492074</article-id><article-id pub-id-type="pmc">2675136</article-id><article-id pub-id-type="publisher-id">cin-08-45</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Research</subject></subj-group></article-categories><title-group><article-title>Development of the Lymphoma Enterprise Architecture Database: A caBIG(tm) Silver level compliant System</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Huang</surname><given-names>Taoying</given-names></name></contrib><contrib contrib-type="author"><name><surname>Shenoy</surname><given-names>Pareen J.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Sinha</surname><given-names>Rajni</given-names></name></contrib><contrib contrib-type="author"><name><surname>Graiser</surname><given-names>Michael</given-names></name></contrib><contrib contrib-type="author"><name><surname>Bumpers</surname><given-names>Kevin W.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Flowers</surname><given-names>Christopher R.</given-names></name><xref ref-type="corresp" rid="c1-cin-08-45"></xref></contrib><aff id="af1-cin-08-45">Winship Cancer Institute, School of Medicine, Emory University, Atlanta, GA, U.S.A</aff></contrib-group><author-notes><corresp id="c1-cin-08-45">Correspondence: Christopher R. Flowers, Emory University, Winship Cancer Institute, 1365 Clifton Road, N.E. Building C, Suite 3006, Atlanta, GA, U.S.A. Tel: 404-778-5554; Fax: 404-778-5520; Email:
<email>christopher.flowers@emoryhealthcare.org</email></corresp></author-notes><pub-date pub-type="epub"><day>3</day><month>4</month><year>2009</year></pub-date><pub-date pub-type="collection"><year>2009</year></pub-date><volume>8</volume><fpage>45</fpage><lpage>64</lpage><permissions><copyright-statement>© 2009 The authors.</copyright-statement><copyright-year>2009</copyright-year><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/3.0"><license-p><pmc-comment>CREATIVE COMMONS</pmc-comment>This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/3.0/">http://creativecommons.org/licenses/by/3.0/</ext-link>).</license-p></license></permissions><abstract><p>Lymphomas are the fifth most common cancer in United States with numerous histological subtypes. Integrating existing clinical information on lymphoma patients provides a platform for understanding biological variability in presentation and treatment response and aids development of novel therapies. We developed a cancer Biomedical Informatics Grid™ (caBIG™) Silver level compliant lymphoma database, called the Lymphoma Enterprise Architecture Data-system™ (LEAD™), which integrates the pathology, pharmacy, laboratory, cancer registry, clinical trials, and clinical data from institutional databases. We utilized the Cancer Common Ontological Representation Environment Software Development Kit (caCORE SDK) provided by National Cancer Institute’s Center for Bioinformatics to establish the LEAD™ platform for data management. The caCORE SDK generated system utilizes an n-tier architecture with open Application Programming Interfaces, controlled vocabularies, and registered metadata to achieve semantic integration across multiple cancer databases. We demonstrated that the data elements and structures within LEAD™ could be used to manage clinical research data from phase 1 clinical trials, cohort studies, and registry data from the Surveillance Epidemiology and End Results database. This work provides a clear example of how semantic technologies from caBIG™ can be applied to support a wide range of clinical and research tasks, and integrate data from disparate systems into a single architecture. This illustrates the central importance of caBIG™ to the management of clinical and biological data.</p></abstract><kwd-group><kwd>biomedical informatics grid</kwd><kwd>non-Hodgkin’s lymphoma</kwd><kwd>large linked database</kwd><kwd>semantic integration</kwd><kwd>caCORE SDK</kwd></kwd-group></article-meta></front><floats-group><fig id="f1-cin-08-45" position="float"><label>Figure 1.</label><caption><p><bold>Layers of semantic interoperability in caBIG</bold>™. Semantic interoperability lies in UML model, use of publicly accessible Terminologies/vocabularies/ontologies (EVS–NCI Thesaurus) and use of publicly accessible metadata repository (caDSR).</p></caption><graphic xlink:href="CIN-08-45-g001"></graphic></fig><fig id="f2-cin-08-45" position="float"><label>Figure 2.</label><caption><p><bold>The caCORE workflow.</bold> This figure describes the steps involved in creating a silver level compliant system. A UML object model is the input into the workflow. The model is exported from the format native to the tool it was developed into the standard XMI representation. The XMI file is then annotated with terminology services. Once the annotated XMI is reviewed and approved, it is used as input to generate code and public APIs, and it is deposited into production caDSR.</p></caption><graphic xlink:href="CIN-08-45-g002"></graphic></fig><fig id="f3-cin-08-45" position="float"><label>Figure 3.</label><caption><p><bold>Logic Model for lymphoma clinical database developed using Enterprise Architecture.</bold> This figure demonstrates the relationships between the key data elements in LEAD™. Components within each key element are not represented in this figure due to practical constraints of resolution and size.</p></caption><graphic xlink:href="CIN-08-45-g003"></graphic></fig><fig id="f4-cin-08-45" position="float"><label>Figure 4.</label><caption><p><bold>Relations between entities and classes from all data sources.</bold> This figure depicts the color–coded sources of various key data elements of LEAD™. The codes for colors are shown in the legend.</p><p><bold>Abbreviations:</bold> ELCD, Emory Lymphoma Clinical Data; SEER, Surveillance, Epidemiology, and End Result; CT, Emory University Lymphoma Clinical Trials data.</p></caption><graphic xlink:href="CIN-08-45-g004"></graphic></fig><fig id="f5-cin-08-45" position="float"><label>Figure 5.</label><caption><p><bold>Architecture for LEAD™.</bold> This figure describes the architecture of the Lymphoma Enterprise Architecture Data–system. It contains the presentation tier, business tier and data source tier. The web server passes the requests from web browsers and transfers them to the application server which then accesses the backend database and generates the required content dynamically and sends the response back to the web browser through the web server. Outside community accesses the data through the provided programmatic API.</p></caption><graphic xlink:href="CIN-08-45-g005"></graphic></fig><fig id="f6-cin-08-45" position="float"><label>Figure 6.</label><caption><p><bold>Graphical user interface for entering adverse event data into LEAD™.</bold> This figure is a sample screen shot of the graphical user interface for entering clinical trial data into LEAD™.</p></caption><graphic xlink:href="CIN-08-45-g006"></graphic></fig><table-wrap id="t1-cin-08-45" position="float"><label>Table 1.</label><caption><p>World Health Organization classification for lymphomas.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="center" colspan="2" rowspan="1">Non Hodgkin Lymphomas (NHL)
<hr></hr></th></tr><tr><th align="left" rowspan="1" colspan="1">B-cell Neoplasm, NOS</th><th align="left" rowspan="1" colspan="1">T-cell and NK-cell Neoplasm, NOS</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1"><bold>Precursor B-cell Neoplasms</bold></td><td align="left" rowspan="1" colspan="1"><bold>Precursor T-cell Neoplasm</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">– Precursor B lymphoblastic leukemia (9835/3)</td><td align="left" rowspan="1" colspan="1">– Precursor T lymphoblastic leukemia (9837/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Precursor B lymphoblastic lymphoma (9728/3)</td><td align="left" rowspan="1" colspan="1">– Precursor T lymphoblastic lymphoma (9729/3)</td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">– Blastic NK cell lymphoma (9727/3)</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Peripheral (mature) B-cell Neoplasms</bold></td><td align="left" rowspan="1" colspan="1"><bold>Peripheral (mature) T-cell Neoplasms</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">– Chronic lymphocytic leukemia (9823/3)</td><td align="left" rowspan="1" colspan="1">– T-cell prolymphocytic leukemia (9834/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Small lymphocytic lymphoma (9670/3)</td><td align="left" rowspan="1" colspan="1">– T-cell large granular lymphocytic leukemia (9831/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– B-cell prolymphocytic leukemia (9833/3)</td><td align="left" rowspan="1" colspan="1">– Aggressive NK cell leukemia (9948/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Lymphoplasmacytic lymphoma (9671/3)</td><td align="left" rowspan="1" colspan="1">– Adult T-cell leukemia/lymphoma (9827/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Splenic marginal zone lymphoma (9689/3)</td><td align="left" rowspan="1" colspan="1">– Extranodal NK/T cell lymphoma, nasal type (9719/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Hairy cell leukemia (9940/3)</td><td align="left" rowspan="1" colspan="1">– Enteropathy type T-cell lymphoma (9717/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Plasmacytoma/Multiple myeloma (9732/3)</td><td align="left" rowspan="1" colspan="1">– Hepatosplenic T-cell lymphoma (9716/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Solitary plasmacytoma of bone (9731/3)</td><td align="left" rowspan="1" colspan="1">– Subcutaneous panniculitis-like T-cell lymphoma (9708/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Extraosseous plasmacytoma (9734/3)</td><td align="left" rowspan="1" colspan="1">– Mycosis fungoides (9700/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Extranodal marginal zone B-cell lymphoma of mucosa-associated lymphoid tissue (MALT-lymphoma) (9699/3)</td><td align="left" rowspan="1" colspan="1">– Sezary Syndrome (9701/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Nodal marginal zone B-cell lymphoma (9699/3)</td><td align="left" rowspan="1" colspan="1">– Primary cutaneous anaplastic large cell lymphoma (9718/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Follicular lymphoma, NOS (9690/3)</td><td align="left" rowspan="1" colspan="1">– Peripheral T-cell lymphoma, unspecified (9702/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Follicular lymphoma Grade 1 (9690/3)</td><td align="left" rowspan="1" colspan="1">– Angioimmunoblastic T-cell lymphoma (9705/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Follicular lymphoma Grade 2 (9690/3)</td><td align="left" rowspan="1" colspan="1">– Anaplastic large cell lymphoma (9714/3)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Follicular lymphoma Grade 3 (9690/3)</td><td align="left" rowspan="1" colspan="1">– Lymphomatoid papulosis (9718/1)</td></tr><tr><td align="left" rowspan="1" colspan="1">– Mantle cell lymphoma (9673/3)</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">– Diffuse large B-cell lymphoma (9680/3)</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">– Mediastinal (thymic) large cell lymphoma (9679/3)</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">– Intravascular large B-cell lymphoma (9680/3)</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">– Primary effusion lymphoma (9678/3)</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">– Burkitt lymphoma (9687/3)</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">– Burkitt lymphoma leukemia (9826/3)</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">– B-cell proliferations of uncertain malignant potential</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">– Lymphomatoid granulomatosis (9766/1)</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">– Post-transplant lymphoproliferative disorder, pleomorphic (9970/1)</td><td align="left" rowspan="1" colspan="1"></td></tr><tr><td align="center" colspan="2" rowspan="1"><hr></hr><bold>Hodgkin Lymphoma, NOS</bold><hr></hr></td></tr><tr><td align="left" colspan="2" rowspan="1">– Nodular lymphocyte predominant Hodgkin lymphoma (9659/3)</td></tr><tr><td align="left" colspan="2" rowspan="1">– Classical Hodgkin lymphoma (9650/3)</td></tr><tr><td align="left" colspan="2" rowspan="1">– Nodular sclerosis classical Hodgkin lymphoma (9663/3)</td></tr><tr><td align="left" colspan="2" rowspan="1">– Lymphocyte-rich classical Hodgkin lymphoma (9651/3)</td></tr><tr><td align="left" colspan="2" rowspan="1">– Mixed cellularity classical Hodgkin lymphoma (9652/3)</td></tr><tr><td align="left" colspan="2" rowspan="1">Lymphocyte-depleted classical Hodgkin lymphoma (9653/3)</td></tr></tbody></table><table-wrap-foot><fn id="tfn1-cin-08-45"><p><bold>Source:</bold> Jaffe et al.<xref rid="b31-cin-08-45" ref-type="bibr">31</xref></p></fn></table-wrap-foot></table-wrap><table-wrap id="t2-cin-08-45" position="float"><label>Table 2.</label><caption><p>Silver level compatibility guidelines.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1">Sections</th><th align="left" rowspan="1" colspan="1">Compatibility Requirements</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">Programming and Messaging Interfaces</td><td align="left" valign="top" rowspan="1" colspan="1"><list list-type="bullet"><list-item><p>Well–described API approved by the caBIG™ ARCHWS that provide access to data in the form of data objects that are instances of classes represented by a domain model.</p></list-item><list-item><p>Electronic data formats reviewed and approved by the caBIG ARCHWS that are supported for both input to and output from the system.</p></list-item><list-item><p>Messaging protocols approved by the caBIG™ ARCHWS that are supported wherever messaging is indicated by the use cases.</p></list-item></list></td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Vocabularies/Terminologies and Ontologies</td><td align="left" valign="top" rowspan="1" colspan="1"><list list-type="bullet"><list-item><p>Terminologies reviewed and validated by the caBIG™ VCDEWS that are used for all appropriate data collection fields and attributes of data objects.</p></list-item><list-item><p>Term definitions must meet VCDEWS workspace guidelines.</p></list-item></list></td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Data Elements</td><td align="left" valign="top" rowspan="1" colspan="1"><list list-type="bullet"><list-item><p>CDEs built from controlled terminologies and according to practices validated by the VCDEWS that are used throughout.</p></list-item><list-item><p>CDEs are registered as ISO/IEC 11179 metadata components in the caBIG™ Context of the caDSR.</p></list-item></list></td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Information Models</td><td align="left" valign="top" rowspan="1" colspan="1"><list list-type="bullet"><list-item><p>Object-oriented domain information models are expressed in UML as class diagrams and as XMI files, and are reviewed and validated by VCDEWS.</p></list-item></list></td></tr></tbody></table><table-wrap-foot><fn id="tfn2-cin-08-45"><p><bold>Abbreviations:</bold> API, application programming interfaces; caBIG™, Cancer Biomedical Informatics Grid™; ARCHWS, Architecture workspace; VCDEWS, Vocabulary/Common Data Elements Workspace; CDE, Common Data Elements; ISO, International Organization for Standardization; IEC, International Electrotechnical Commission; caDSR, cancer Data Standards Repository; UML, Unified Modeling Language; XMI, XML Metadata Interchange.</p></fn></table-wrap-foot></table-wrap><table-wrap id="t3-cin-08-45" position="float"><label>Table 3.</label><caption><p>caBIG™ tools, infrastructure and data resources.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1">Category</th><th align="center" rowspan="1" colspan="1">Role</th></tr></thead><tbody><tr><td align="left" rowspan="6" valign="top" colspan="1">Tools</td><td align="left" valign="top" rowspan="1" colspan="1">caIntegrator: a novel translational informatics platform that allows researchers and bioinformaticians to access and analyze clinical and experimental data across multiple clinical trials and studies.</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">caBIO: a domain model and architecture used to model the rapidly-changing genomics and proteomics domain and to integrate data from numerous sources providing a holistic view of the human and mouse genomes.</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">caTissue Core: a web tissue bank repository tool for biospecimen inventory, tracking, and basic annotation</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">caArray: a web and programmatically accessible array data management system</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">caXchange: a lab integration hub for clinical trials</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">C3PR: a web patient registration system for clinical trials</td></tr><tr><td align="left" rowspan="8" valign="top" colspan="1">Infrastructure</td><td align="left" valign="top" rowspan="1" colspan="1">caGrid: enables universal mechanisms for providing interoperable programmatic access to data and analytics in caBIG™, creates a self-described infrastructure wherein the structure and semantics of data can be programmatically determined, and provides a powerful means by which services available in caBIG™ can be programmatically discovered and leveraged</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">BRIDG: provides a shared view of the dynamic and static semantics that collectively define a shared domain-of-interest</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">CTODS: provides a single, unified set of APIs that can access clinical data from multiple data sources</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">caBIO: facilitates the communication and integration of information from the various initiatives supported by caBIG™ and NCI</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">caCORE: helps streamline the informatics development and providing a common data management and application development framework</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">caDSR: stores and manages CDEs developed by caBIG™ participants and various NCI-sponsored organizations</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">EVS: produces the NCI Thesaurus, Metathesaurus and provides NCI with licenses for MedDRA, SNOMED, ICD-O-3, and other proprietary vocabularies</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">caCORE SDK: a set of tools that aid in the design and creation of a “caCORE-like” software system.</td></tr><tr><td align="left" rowspan="6" valign="top" colspan="1">Data Resources</td><td align="left" valign="top" rowspan="1" colspan="1">caArray: an open-source, web and programmatically accessible array data management system</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">caBIO: a biomedical data system built using a model-driven approach to develop objects, data models middleware, vocabularies, and ontologies for biomedical research.</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Cancer Gene Data Curation Pilot: creates a database of associations between genes and diseases and genes and drug compounds derived from the biomedical literature.</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">caIntegrator: provides a mechanism for integrating and aggregating biomedical research data and access to a variety of data types</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">caMOD: provides information about animal models for human cancer to the public research community</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Pathway Interaction Database: a highly structured, curated collection of information about known biomolecular interactions and key cellular processes assembled into signaling pathways</td></tr></tbody></table><table-wrap-foot><fn id="tfn3-cin-08-45"><p><bold>Source:</bold><ext-link ext-link-type="uri" xlink:href="https://cabig.nci.nih.gov/inventory/">https://cabig.nci.nih.gov/inventory/</ext-link>.</p></fn></table-wrap-foot></table-wrap><table-wrap id="t4-cin-08-45" position="float"><label>Table 4.</label><caption><p>The key caCORE components.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1">Component</th><th align="center" rowspan="1" colspan="1">Description</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">EVS</td><td align="left" rowspan="1" colspan="1">A description-logic based thesaurus and ontology management system. It is a set of services and resources that address NCI’s needs for controlled vocabulary.</td></tr><tr><td align="left" rowspan="1" colspan="1">caDSR</td><td align="left" rowspan="1" colspan="1">A repository that the NCI and its partners use it to create, edit and deploy the Common Data Elements.</td></tr><tr><td align="left" rowspan="1" colspan="1">caBIO</td><td align="left" rowspan="1" colspan="1">A model driven information system using the cancer Common Ontological Representation Environment; a synthesis of software, vocabulary, and metadata models for cancer research. Each of the caBIO domain objects represents an entity found in biomedical research such as Gene, Chromosome, Single Nucleotide Polymorphisms.</td></tr><tr><td align="left" rowspan="1" colspan="1">CSM</td><td align="left" rowspan="1" colspan="1">A comprehensive and integrated solution to common security objectives. It helps eliminate the need for development teams to create their own security methodology.</td></tr></tbody></table><table-wrap-foot><fn id="tfn4-cin-08-45"><p><bold>Abbreviations:</bold> EVS, Enterprise Vocabulary System; caDSR, cancer Data Standards Repository; caBIO, cancer Bioinformatics Infrastructure Objects; CSM, Common Security Module.</p></fn></table-wrap-foot></table-wrap><table-wrap id="t5A-cin-08-45" position="float"><label>Table 5A.</label><caption><p>Queries used to identify NHL cases within linked legacy databases.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="2" valign="top" colspan="1">Query</th><th align="left" rowspan="2" valign="top" colspan="1">Source</th><th align="center" colspan="3" valign="top" rowspan="1">Criteria
<hr></hr></th><th align="center" colspan="3" valign="top" rowspan="1">Records Identified (% of all records)
<hr></hr></th></tr><tr><th align="center" valign="top" rowspan="1" colspan="1">FL</th><th align="center" valign="top" rowspan="1" colspan="1">DLBCL</th><th align="center" valign="top" rowspan="1" colspan="1">MCL</th><th align="center" valign="top" rowspan="1" colspan="1">FL</th><th align="center" valign="top" rowspan="1" colspan="1">DLBCL</th><th align="center" valign="top" rowspan="1" colspan="1">MCL</th></tr></thead><tbody><tr><td align="left" valign="top" rowspan="1" colspan="1">Q1</td><td align="center" valign="top" rowspan="1" colspan="1">Cancer Registry, ICD-O morphology codes plus behavior code 3</td><td align="center" valign="top" rowspan="1" colspan="1">Morphology codes 9690, 9691, 9695, 9698</td><td align="center" valign="top" rowspan="1" colspan="1">Morphology codes 9680, 9684</td><td align="center" valign="top" rowspan="1" colspan="1">Morphology codes 9693</td><td align="left" valign="top" rowspan="1" colspan="1">258 (40%)</td><td align="left" valign="top" rowspan="1" colspan="1">337 (38%)</td><td align="left" valign="top" rowspan="1" colspan="1">66 (8%)</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Q2</td><td align="center" valign="top" rowspan="1" colspan="1">Text search-pathology reports</td><td align="center" valign="top" rowspan="1" colspan="1">‘follicular lymphoma’</td><td align="center" valign="top" rowspan="1" colspan="1">‘diffuse large B’ and ‘diffuse large cell’</td><td align="center" valign="top" rowspan="1" colspan="1">‘lymphoma lymphocytic’ OR ‘lymphocytic diffuse’ OR ‘lymphocytic lymphoma’</td><td align="left" valign="top" rowspan="1" colspan="1">441 (69%)</td><td align="left" valign="top" rowspan="1" colspan="1">367 (41%)</td><td align="left" valign="top" rowspan="1" colspan="1">710 (82%)</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Q3</td><td align="center" valign="top" rowspan="1" colspan="1">Text search-pathology reports</td><td align="center" valign="top" rowspan="1" colspan="1">‘follicular lymphoma’ OR ‘nodular lymphoma’ OR ‘Brill-Symmers’ OR ‘reticulosarcoma— follicular’ OR ‘follicular lymphosarcoma’ OR ‘follicle center lymphoma’ OR ‘follicular non-Hodgkin’</td><td align="center" valign="top" rowspan="1" colspan="1">‘lymphoma diffuse histiocytic’ OR ‘lymphoma diffuse large’ OR ‘lymphoma large B’ OR ‘lymphoma large cell’ OR ‘lymphoma diffuse’ OR ‘diffuse non Hodgkin’ OR ‘lymphoma histiocytic’</td><td align="center" valign="top" rowspan="1" colspan="1">‘mantle cell lymphoma’ OR ‘lymphoma mantle cell’ OR ‘mantle zone lymphoma’ OR ‘lymphoma mantle zone’</td><td align="left" valign="top" rowspan="1" colspan="1">176 (27%)</td><td align="left" valign="top" rowspan="1" colspan="1">321 (36%)</td><td align="left" valign="top" rowspan="1" colspan="1">456 (52%)</td></tr><tr><td align="left" valign="top" rowspan="1" colspan="1">Q4</td><td align="center" valign="top" rowspan="1" colspan="1">Text search-all medical reports</td><td align="center" valign="top" rowspan="1" colspan="1">Same as Q2</td><td align="center" valign="top" rowspan="1" colspan="1">Same as Q2</td><td align="center" valign="top" rowspan="1" colspan="1">Same as Q2</td><td align="left" valign="top" rowspan="1" colspan="1">591 (92%)</td><td align="left" valign="top" rowspan="1" colspan="1">567 (64%)</td><td align="left" valign="top" rowspan="1" colspan="1">775 (89%)</td></tr><tr><td align="left" rowspan="2" valign="top" colspan="1">Q5</td><td align="center" valign="top" rowspan="1" colspan="1">Text search-all medical reports</td><td align="center" valign="top" rowspan="1" colspan="1">Same as Q3</td><td align="center" valign="top" rowspan="1" colspan="1">Same as Q3</td><td align="center" valign="top" rowspan="1" colspan="1">Same as Q3</td><td align="left" valign="top" rowspan="1" colspan="1">272 (42%)</td><td align="left" valign="top" rowspan="1" colspan="1">471 (53%)</td><td align="left" valign="top" rowspan="1" colspan="1">494 (57%)</td></tr><tr><td align="center" valign="top" rowspan="1" colspan="1">Total cases reviewed</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="left" valign="top" rowspan="1" colspan="1">643</td><td align="left" valign="top" rowspan="1" colspan="1">886</td><td align="left" valign="top" rowspan="1" colspan="1">871</td></tr></tbody></table><table-wrap-foot><fn id="tfn5-cin-08-45"><p><bold>Abbreviations:</bold> FL, Follicular lymphoma; DLBCL, Diffuse large B–cell lymphoma; MCL, Mantle cell lymphoma.</p></fn></table-wrap-foot></table-wrap><table-wrap id="t5B-cin-08-45" position="float"><label>Table 5B.</label><caption><p>Sensitivity and specificity for linked database queries.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1"></th><th align="center" colspan="2" rowspan="1">Follicular Lymphoma
<hr></hr></th><th align="center" colspan="2" rowspan="1">Diffuse Large B–cell Lymphoma
<hr></hr></th><th align="center" colspan="2" rowspan="1">Mantle Cell Lymphoma
<hr></hr></th></tr><tr><th align="left" rowspan="1" colspan="1">QUERY</th><th align="left" rowspan="1" colspan="1">Sensitivity (%)</th><th align="left" rowspan="1" colspan="1">Specificity (%)</th><th align="left" rowspan="1" colspan="1">Sensitivity (%)</th><th align="left" rowspan="1" colspan="1">Specificity (%)</th><th align="left" rowspan="1" colspan="1">Sensitivity (%)</th><th align="left" rowspan="1" colspan="1">Specificity (%)</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1"><bold>Q1</bold></td><td align="center" rowspan="1" colspan="1"><bold>53</bold></td><td align="center" rowspan="1" colspan="1"><bold>73</bold></td><td align="center" rowspan="1" colspan="1"><bold>65</bold></td><td align="center" rowspan="1" colspan="1"><bold>60</bold></td><td align="center" rowspan="1" colspan="1"><bold>54</bold></td><td align="center" rowspan="1" colspan="1"><bold>98</bold></td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Q2</bold></td><td align="center" rowspan="1" colspan="1"><bold>70</bold></td><td align="center" rowspan="1" colspan="1"><bold>33</bold></td><td align="center" rowspan="1" colspan="1"><bold>38</bold></td><td align="center" rowspan="1" colspan="1"><bold>66</bold></td><td align="center" rowspan="1" colspan="1"><bold>72</bold></td><td align="center" rowspan="1" colspan="1"><bold>17</bold></td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Q3</bold></td><td align="center" rowspan="1" colspan="1"><bold>37</bold></td><td align="center" rowspan="1" colspan="1"><bold>82</bold></td><td align="center" rowspan="1" colspan="1"><bold>55</bold></td><td align="center" rowspan="1" colspan="1"><bold>78</bold></td><td align="center" rowspan="1" colspan="1"><bold>85</bold></td><td align="center" rowspan="1" colspan="1"><bold>51</bold></td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Q4</bold></td><td align="center" rowspan="1" colspan="1"><bold>97</bold></td><td align="center" rowspan="1" colspan="1"><bold>13</bold></td><td align="center" rowspan="1" colspan="1"><bold>55</bold></td><td align="center" rowspan="1" colspan="1"><bold>50</bold></td><td align="center" rowspan="1" colspan="1"><bold>44</bold></td><td align="center" rowspan="1" colspan="1"><bold>6</bold></td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Q5</bold></td><td align="center" rowspan="1" colspan="1"><bold>57</bold></td><td align="center" rowspan="1" colspan="1"><bold>73</bold></td><td align="center" rowspan="1" colspan="1"><bold>78</bold></td><td align="center" rowspan="1" colspan="1"><bold>56</bold></td><td align="center" rowspan="1" colspan="1"><bold>97</bold></td><td align="center" rowspan="1" colspan="1"><bold>48</bold></td></tr></tbody></table></table-wrap><table-wrap id="t6-cin-08-45" position="float"><label>Table 6.</label><caption><p>Phase 1/Phase 2 Lymphoma clinical trial data collection schedule.</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="1" colspan="1">Procedures</th><th align="center" rowspan="1" colspan="1">Screen</th><th align="center" rowspan="1" colspan="1">Cycle 1–8 day 1</th><th align="center" rowspan="1" colspan="1">Cycle 1–8 day 8</th><th align="center" rowspan="1" colspan="1">End of cycle 2,4,6,8</th><th align="center" rowspan="1" colspan="1">End of induction</th><th align="center" rowspan="1" colspan="1">Every 3 months maintenance</th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Physical exam</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td></tr><tr><td align="left" rowspan="1" colspan="1">ECG</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">ECOG Performance Status</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td></tr><tr><td align="left" rowspan="1" colspan="1">Chemotherapy administration</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Bortezomib administration</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Adverse Event assessment</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td></tr><tr><td align="left" rowspan="1" colspan="1">Tumor assessment</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Chest, Abdomen, Pelvis CT scan</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td></tr><tr><td align="left" rowspan="1" colspan="1">Pathology slides</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Tumor tissue block</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Bone Marrow Biopsy</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">X (if in remission)</td><td align="center" rowspan="1" colspan="1">X (if in remission)</td><td align="center" rowspan="1" colspan="1">X (if in remission)</td></tr><tr><td align="left" rowspan="1" colspan="1">Minimal Residual Disease Analysis</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1">X (if in remission)</td><td align="center" rowspan="1" colspan="1">X (if in remission)</td><td align="center" rowspan="1" colspan="1">X (if in remission)</td></tr><tr><td align="left" rowspan="1" colspan="1"><bold>Labs</bold></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Hematology (CBC)</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td></tr><tr><td align="left" rowspan="1" colspan="1">Clinical Chemistry</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td></tr><tr><td align="left" rowspan="1" colspan="1">(basic metabolic)</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Clinical Chemistry (hepatic)</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Uric Acid</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">LDH</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Serum HCG</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">HIV–1</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Hepatitis B</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr><tr><td align="left" rowspan="1" colspan="1">Hepatitis C</td><td align="center" rowspan="1" colspan="1">X</td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td><td align="center" rowspan="1" colspan="1"></td></tr></tbody></table></table-wrap></floats-group></pmc><affiliations><list></list><tree><noCountry><name sortKey="Bumpers, Kevin W" sort="Bumpers, Kevin W" uniqKey="Bumpers K" first="Kevin W." last="Bumpers">Kevin W. Bumpers</name><name sortKey="Flowers, Christopher R" sort="Flowers, Christopher R" uniqKey="Flowers C" first="Christopher R." last="Flowers">Christopher R. Flowers</name><name sortKey="Graiser, Michael" sort="Graiser, Michael" uniqKey="Graiser M" first="Michael" last="Graiser">Michael Graiser</name><name sortKey="Huang, Taoying" sort="Huang, Taoying" uniqKey="Huang T" first="Taoying" last="Huang">Taoying Huang</name><name sortKey="Shenoy, Pareen J" sort="Shenoy, Pareen J" uniqKey="Shenoy P" first="Pareen J." last="Shenoy">Pareen J. Shenoy</name><name sortKey="Sinha, Rajni" sort="Sinha, Rajni" uniqKey="Sinha R" first="Rajni" last="Sinha">Rajni Sinha</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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