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  • Some nice summary and closing remarks
  • Non-Hodgkin’s lymphomas are cancers of the lymphatic system. The lymphatic system is involved in our body’s immune function.
    The lymphatic system is made up of types of white blood cell called “lymphocytes” (which will be described on the next two slides) and lymphoid organs.
    The organs of the lymphatic system are scattered throughout the body and are responsible for the growth, development, and use (or deployment) of lymphocytes.
    Primary lymphoid organs include the bone marrow (the soft spongy material in the center of all of our bone) and the thymus (a gland in the chest).
    The bone marrow is where blood cells (such as white blood cells, red blood cells and platelets) are produced, grow and develop. The thymus is a special gland in the chest where some lymphocytes (T lymphocytes) mature or develop.
    Secondary lymphoid organs store lymphocytes for use when the body needs them. They include the lymph nodes, spleen, tonsils, adenoids, and specialized lymphoid tissues in the gastrointestinal tract and lungs.
    After lymphocytes develop in the primary lymphoid organs, they enter the blood stream and migrate to the secondary lymphoid tissues, where they congregate and interact with other cells involved in the immune response.
    Lymph is a clear fluid that transports lymphocytes throughout the body by means of lymphatic vessels. This is called lymphatic circulation.
    Lymph nodes are positioned along the lymphatic vessels and filter viruses and bacteria from circulating lymph.
    It is common for lymph nodes to become enlarged when a person is exposed to an infection.
    Lymph nodes are also commonly enlarged in lymphoma.
    American Cancer Society Web site, Non-Hodgkin’s Lymphoma
    Canellos GP, et al., The Lymphomas, 1998, p. 337
    Yarbro CH, et al., Cancer Nursing: Principles and Practice, 2000, p. 1330
  • The NHLs comprise a diverse group of lymphoid neoplasms that collectively ranked 5th-6th in cancer incidence and mortality.1 The majority of patients are first diagnosed at age 65 or over. This age-group accounts for the greatest increase in incidence with a 79% increase observed from 1973 to 1996.2
    About 20% to 30% of the NHLs diagnosed are low-grade or indolent lymphomas. For low-grade NHL, the incidence has increased from 2.6 to 3.2 cases per 100,000 from 1978 to 1996.2
    1. Jemal et al. CA Cancer J Clin. 2002;52:23-47.
    2. Gloeckler Ries et al. SEER Cancer Statistics Review, 1973-1996. Bethesda, Md: National Cancer Institute; 1999.
  • Lymph nodes comprise a network of reticular tissue organized into sinuses and encapsulated by fibrous connective tissue. Phagocytic cells lining the sinuses act to filter lymph flowing through lymph nodes. The cortex of the lymph node contains distinct B- and T-cell lymphoid areas. Primary follicles containing aggregates of B lymphocytes are found in the cortex of the unstimulated lymph node. After antigenic challenge these become secondary follicles, comprising a mantle of resting small B lymphocytes and memory cells encircling a germinal center that contains large B cells and macrophages. There is an adjacent surrounding marginal zone containing mature B cells. In the medulla of the lymph node, antigenic stimulation promotes the differentiation of B lymphoblasts to plasma cells in the medullary cords between the medullary sinuses.
    Various subtypes of indolent B-cell lymphomas originate in distinct microanatomic compartments in the lymph node. Follicular lymphomas may arise from the germinal centers: MCL from the mantle zone and monocytoid B-cell lymphomas and MALTomas from the surrounding marginal zone. SLL, CLL, and LPL can arise preferentially in the medullary cords at the physiologic site of abundant plasma cells.
    Legend:
    Cortex
    Green=T-cell cortex
    Blue=B-cell areas of germinal center
    Red=mantle zone
    Gold=lymphatic sinuses
    Medulla
    Beige=medullary cords
    Gold=medullary sinuses
  • The disease entities described by the WHO classification are described.
    WHO classifications recognize 3 major categories of lymphoid neoplasms based on a combination of morphology and cell lineage:
    B-cell neoplasms
    T/NK-cell neoplasms
    Hodgkin’s disease/Hodgkin’s lymphoma
    Two major differentiation stages are recognized:
    Precursor neoplasms, corresponding to the earliest stages of differentiation
    Peripheral or mature neoplasms, corresponding to more differentiated stages
    Three broad categories of clinical presentation are recognized:
    Predominantly disseminated diseases, which often involve bone marrow and may be leukemic
    Primary extranodal lymphomas
    Predominantly nodal diseases, which are often disseminated and may also involve extranodal sites
  • The disease entities described by the WHO classification are described.
    WHO classifications recognize 3 major categories of lymphoid neoplasms based on a combination of morphology and cell lineage:
    B-cell neoplasms
    T/NK-cell neoplasms
    Hodgkin’s disease/Hodgkin’s lymphoma
    Two major differentiation stages are recognized:
    Precursor neoplasms, corresponding to the earliest stages of differentiation
    Peripheral or mature neoplasms, corresponding to more differentiated stages
    Three broad categories of clinical presentation are recognized:
    Predominantly disseminated diseases, which often involve bone marrow and may be leukemic
    Primary extranodal lymphomas
    Predominantly nodal diseases, which are often disseminated and may also involve extranodal sites
  • Transcript

    • 1. Developing Information Systems forDeveloping Information Systems for Cancer ResearchCancer Research Christopher Flowers, MD, MScChristopher Flowers, MD, MSc Assistant ProfessorAssistant Professor Medical Director, Oncology Data CenterMedical Director, Oncology Data Center Bone Marrow and Stem Cell Transplant CenterBone Marrow and Stem Cell Transplant Center Winship Cancer InstituteWinship Cancer Institute Emory UniversityEmory University
    • 2. Health Care Data Integration Medical Intelligence Applications
    • 3. What Data are available?What Data are available?  Patient GenomicsPatient Genomics – Microarrays and Gene ChipMicroarrays and Gene Chip – Analysis ResultsAnalysis Results – Quality ValuesQuality Values  Hospital Patient ManagementHospital Patient Management – Patient DemographicsPatient Demographics » Inpatient, Outpatient, Patient TypesInpatient, Outpatient, Patient Types » Location, Physician, VisitsLocation, Physician, Visits  Hospital Patient AccountingHospital Patient Accounting – Financial DataFinancial Data » Patient chargesPatient charges » Payments and CollectionsPayments and Collections – Summarized Financial Visit DataSummarized Financial Visit Data – Charge DescriptionCharge Description
    • 4.  PharmacyPharmacy – Orders, Drugs, MedicationOrders, Drugs, Medication – FormularyFormulary – Drug InteractionsDrug Interactions – CostsCosts  Medical RecordsMedical Records – Procedures & Diagnosis (CPT4 & ICD9)Procedures & Diagnosis (CPT4 & ICD9) – Visit, AbstractVisit, Abstract – PhysicianPhysician – Admit Diagnosis, Admit Source and TypeAdmit Diagnosis, Admit Source and Type – RDRG/DRGRDRG/DRG What Data are available?What Data are available?
    • 5.  Clinic Patient AccountingClinic Patient Accounting – Patient Registration; Demographics, Insurance (FSC), Employer, CasePatient Registration; Demographics, Insurance (FSC), Employer, Case – ProviderProvider – General LedgerGeneral Ledger – Financial Data & InvoicesFinancial Data & Invoices • Laboratory Results – Lab Orders, General Results and Micro – Clinic and Hospital Patients What Data are available?What Data are available?
    • 6.  Radiation OncologyRadiation Oncology – Treatment PlansTreatment Plans  Clinical TrialsClinical Trials – StudiesStudies – Patient DemographicsPatient Demographics – PathologyPathology  Cancer RegistryCancer Registry – Patient Demographics and abstractPatient Demographics and abstract – Pathology, Treatment Plans and Discharge SummaryPathology, Treatment Plans and Discharge Summary – Progress Notes, Radiology results, ChargesProgress Notes, Radiology results, Charges What Data are available?What Data are available?
    • 7.  Patient Chart InformationPatient Chart Information – Physician NotesPhysician Notes – Radiology ReportsRadiology Reports – HLAHLA – Cancer Anatomic PathCancer Anatomic Path – Lab Test ResultsLab Test Results  Other (Forms entry)Other (Forms entry) – IBMTR/ABMTR FormIBMTR/ABMTR Form – Acute Myelogenous FormAcute Myelogenous Form – Patient Profile FormPatient Profile Form – Informed ConsentInformed Consent What Data are available?What Data are available?
    • 8. Analysis of Search Algorithms forAnalysis of Search Algorithms for Oncologic Disease IdentificationOncologic Disease Identification Using GeneSys SIUsing GeneSys SI Michael Graiser, PhD1 , Ashley Hilliard1 , Rochelle Victor1 , Ragini Kudchadkar, MD1 , Leroy Hill1 , Michael S. Keehan, PhD2 , Jonathan Simons, MD1 , Christopher Flowers, MD1 1 Winship Cancer Institute, Department of Hematology and Oncology, Emory University School of Medicine, Atlanta, GA (http://www.winshipcancerinstitute.org) 2 NuTec Health Systems, Atlanta, GA* (email: info@nutechealthsystems.com) * Emory University has a financial interest in NuTec Health Systems, which designed and built GeneSys SI. Emory may financially benefit from this interest if NuTec is successful in marketing GeneSys SI. This project may produce income for Emory’s charitable purposes and for NuTec’s commercial purposes.
    • 9. Development of GeneSys SIDevelopment of GeneSys SI ● Collaborative effort between Emory’s Winship CancerCollaborative effort between Emory’s Winship Cancer Institute and NuTec Health SystemsInstitute and NuTec Health Systems ● Web-based query tool and genomic analysis toolsWeb-based query tool and genomic analysis tools designed with a team of Emory oncologists anddesigned with a team of Emory oncologists and research investigatorsresearch investigators ● August, 2002 – 175,000 Emory patients identified byAugust, 2002 – 175,000 Emory patients identified by cancer diagnosis loaded into GeneSys SIcancer diagnosis loaded into GeneSys SI ● New patients added by individual patient consentNew patients added by individual patient consent ● Ongoing efforts to add new sources of dataOngoing efforts to add new sources of data ● Tissue BankingTissue Banking ● Genomic toolsGenomic tools
    • 10. GeneSys SI Modules Health Care Applications
    • 11. GeneSys SI Gene Expression Information Clinical Information Sequence Information External Databases
    • 12. Linked patient-level dataLinked patient-level data ● PathologyPathology ● Cancer RegistryCancer Registry ● Laboratory ResultsLaboratory Results ● Radiology ResultsRadiology Results ● Medication utilizationMedication utilization ● Clinical outcomesClinical outcomes ● GenomicsGenomics Scheduling Medical Records Pharmacy Lab Results 1 1 1 1 Billing 1 Family History Occupational Exposure Cancer Registry Clinical Trials Pyxis 4 4 4 5 Cancer Epidemiology 5 5 Tissue Banking (under construction) 5 Microarrays 2 3 Anatomic Path Cytogenetics Lab Physician Notes Radiology Reports 3 3 3 3 Radiation Oncology GeneSys SI: Architecture
    • 13. Scheduling Medical Records Pharmacy Lab Results 1 1 1 1 Billing 1 Family History Occupational Exposure Cancer Registry Clinical Trials Pyxis 4 4 4 5 Cancer Epidemiology 5 5 Tissue Banking (under construction) 5 Microarrays 2 3 Anatomic Path Cytogenetics Lab Physician Notes Radiology Reports 3 3 3 3 Radiation Oncology GeneSys SI: Architecture
    • 14. Scheduling Medical Records Pharmacy Lab Results 1 1 1 1 Billing 1 Family History Occupational Exposure Cancer Registry Clinical Trials Pyxis 4 4 4 5 Cancer Epidemiology 5 5 Tissue Banking (under construction) 5 Microarrays 2 3 Anatomic Path Cytogenetics Lab Physician Notes Radiology Reports 3 3 3 3 Radiation Oncology Investigator Defined Forms Data Public Databases Genetic Protein
    • 15. GeneSys SI contains information on patients who have visited Emory University Hospital, Crawford Long Hospital, or The Emory Clinic and have received an oncology diagnosis. Benign neoplasms are also included. Database Population
    • 16. Numbers • Total patients 175,748 • Newly consented 551 • By ICD9 & ICD10
    • 17. Data currently available in GeneSys SIData currently available in GeneSys SI DATA SOURCE ENTRY DATE HISTORY (YEARS)DATA SOURCE ENTRY DATE HISTORY (YEARS) Emory Data WarehouseEmory Data Warehouse Hospital administrative (HealthQuest)Hospital administrative (HealthQuest) Clinic administrative (IDX)Clinic administrative (IDX) Medical RecordsMedical Records Clinical LabsClinical Labs Hospital PharmacyHospital Pharmacy Clinic PhamacyClinic Phamacy September, 1995September, 1995 September, 1994September, 1994 19871987 January, 2001January, 2001 January, 1998January, 1998 April, 2002April, 2002 99 1010 1717 33 66 22 Cancer RegistryCancer Registry Emory HositalEmory Hosital Crawford Long HospitalCrawford Long Hospital 19771977 19811981 2727 2323 Clinical TrialsClinical Trials 19811981 2121 Electronic Medical RecordElectronic Medical Record PowerChartPowerChart 19911991 1313 Radiation OncologyRadiation Oncology The Emory ClinicThe Emory Clinic Crawford Long HospitalCrawford Long Hospital 19941994 20012001 1010 33 FormsForms Informed ConsentInformed Consent July, 2003July, 2003 11 GenomicsGenomics TBDTBD N/AN/A
    • 18. Linked Oncology DatabaseLinked Oncology Database Useful for:Useful for: ● Retrospective clinical outcomes researchRetrospective clinical outcomes research ● Clinical trials planningClinical trials planning ● Cost effectiveness analysesCost effectiveness analyses ● Storage of unique clinical dataStorage of unique clinical data ● Linking to public genomic and proteomic databasesLinking to public genomic and proteomic databases ● PharmacogenomicsPharmacogenomics
    • 19. Limitations of linked heterogeneous databasesLimitations of linked heterogeneous databases ● Reliance on patient identifiers such as SSN to linkReliance on patient identifiers such as SSN to link ● data entry errors, missing data, business practicesdata entry errors, missing data, business practices ● Patchwork of different databases not intended forPatchwork of different databases not intended for research purposesresearch purposes ● Reliance upon coded outcomes (e.g. ICD-9 codes)Reliance upon coded outcomes (e.g. ICD-9 codes) ● frequently assigned by personnel unfamiliar with patient,frequently assigned by personnel unfamiliar with patient, disease, or proceduredisease, or procedure ● Multiple sources for the same dataMultiple sources for the same data ● diagnosis, treatment, DOB, DOE, other demographicsdiagnosis, treatment, DOB, DOE, other demographics Breitfeld et.al. J Clin Epi, 2001.Breitfeld et.al. J Clin Epi, 2001. Earle et al. Med Care, 2002.Earle et al. Med Care, 2002. Verstraeten et.al.Verstraeten et.al. Expert Rev. VaccinesExpert Rev. Vaccines, 2003., 2003.
    • 20. Research ObjectivesResearch Objectives ● Develop query algorithms to identify pts with aDevelop query algorithms to identify pts with a histological diagnosishistological diagnosis ● Follicular lymphomaFollicular lymphoma ● Examine sensitivity and specificity of queryExamine sensitivity and specificity of query algorithmsalgorithms ● Develop query strategies for identifying pts withDevelop query strategies for identifying pts with other diseases of interestother diseases of interest
    • 21. 10 Leading Cancer Sites by Gender, US, 200510 Leading Cancer Sites by Gender, US, 2005 32%32% BreastBreast 12%12% Lung & bronchusLung & bronchus 11%11% Colon & rectumColon & rectum 6%6% Uterine corpusUterine corpus 4%4% Non-Hodgkin’s lymphomaNon-Hodgkin’s lymphoma 4%4% Melanoma of skinMelanoma of skin 3%3% OvaryOvary 3%3% ThyroidThyroid 2%2% Urinary bladderUrinary bladder 2%2% PancreasPancreas 20%20% All other sitesAll other sites Men 710,040 Women 662,870 ProstateProstate33%33% Lung & bronchusLung & bronchus13%13% Colon & rectumColon & rectum11%11% Urinary bladderUrinary bladder7%7% Melanoma of skinMelanoma of skin 5%5% Non-Hodgkin’s lymphomaNon-Hodgkin’s lymphoma 4%4% LeukemiaLeukemia 3%3% KidneyKidney 3%3% Oral cavityOral cavity 3%3% PancreasPancreas2%2% All other sitesAll other sites17%17% *Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder. American Cancer Society, 2005.
    • 22. Lymph Node Courtesy of Thomas Grogan, MD.Courtesy of Thomas Grogan, MD. Medula Primary Follicle Marginal Zone Afferent Lymphatic Vessel Mantle Zone Germinal Center Secondary Follicle Postcapillary Venule Artery Efferent Lymphatic Vessel Medullary Sinus Medullary Cord Subcapsular Sinus Cortex
    • 23. WHO NHL Classification B-cell • Precursor B-cell neoplasms − B-acute lymphoblastic leukemia (B-ALL) − Lymphoblastic lymphoma (LBL) • Peripheral B-cell neoplasms − B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma − B-cell prolymphocytic leukemia − Lymphoplasmacytic lymphoma/immunocytoma − Mantle cell lymphoma − Follicular lymphoma − Extranodal marginal zone B-cell lymphoma of MALT type − Nodal marginal zone B-cell lymphoma − Splenic marginal zone lymphoma − Hairy cell leukemia − Plasmacytoma/plasma cell myeloma − Diffuse large B-cell lymphoma − Burkitt’s lymphoma T-cell/NK-cell • Precursor T-cell neoplasm − Precursor T-acute lymphoblastic leukemia (T-ALL) − Lymphoblastic lymphoma (LBL) • Peripheral T-cell/NK-cell neoplasms − T-cell chronic lymphocytic leukemia/prolymphocytic leukemia − T-cell granular lymphocytic leukemia − Mycosis fungoides/Sézary syndrome − Peripheral T-cell lymphoma not otherwise characterized − Hepatosplenic gamma/delta T-cell lymphoma − Angioimmunoblastic T-cell lymphoma − Extranodal T-/NK-cell lymphoma, nasal type − Enteropathy-type intestinal T-cell lymphoma − Adult T-cell lymphoma/leukemia (HTLV1+) − Anaplastic large cell lymphoma, primary systemic type − Anaplastic large cell lymphoma, primary cutaneous type − Aggressive NK-cell leukemia Fisher et al. In: DeVita et al, eds. Cancer: Principles and Practice of Oncology. 2005:1967. Jaffe et al, eds. World Health Organization Classification of Tumours. 2001.
    • 24. WHO NHL Classification B-cell • Precursor B-cell neoplasms − B-acute lymphoblastic leukemia (B-ALL) − Lymphoblastic lymphoma (LBL) • Peripheral B-cell neoplasms − B-cell chronic lymphocytic leukemia/small lymphocytic lymphoma − B-cell prolymphocytic leukemia − Lymphoplasmacytic lymphoma/immunocytoma − Mantle cell lymphoma − Follicular lymphoma − Extranodal marginal zone B-cell lymphoma of MALT type − Nodal marginal zone B-cell lymphoma − Splenic marginal zone lymphoma − Hairy cell leukemia − Plasmacytoma/plasma cell myeloma − Diffuse large B-cell lymphoma − Burkitt’s lymphoma T-cell/NK-cell • Precursor T-cell neoplasm − Precursor T-acute lymphoblastic leukemia (T-ALL) − Lymphoblastic lymphoma (LBL) • Peripheral T-cell/NK-cell neoplasms − T-cell chronic lymphocytic leukemia/prolymphocytic leukemia − T-cell granular lymphocytic leukemia − Mycosis fungoides/Sézary syndrome − Peripheral T-cell lymphoma not otherwise characterized − Hepatosplenic gamma/delta T-cell lymphoma − Angioimmunoblastic T-cell lymphoma − Extranodal T-/NK-cell lymphoma, nasal type − Enteropathy-type intestinal T-cell lymphoma − Adult T-cell lymphoma/leukemia (HTLV1+) − Anaplastic large cell lymphoma, primary systemic type − Anaplastic large cell lymphoma, primary cutaneous type − Aggressive NK-cell leukemia Fisher et al. In: DeVita et al, eds. Cancer: Principles and Practice of Oncology. 2005:1967. Jaffe et al, eds. World Health Organization Classification of Tumours. 2001.
    • 25. MethodsMethods ● Selected disease for initial query algorithm studySelected disease for initial query algorithm study (follicular lymphoma - FL)(follicular lymphoma - FL) ● Developed and ran queries for FL using all availableDeveloped and ran queries for FL using all available sources for diagnosissources for diagnosis ● Clinic & Hospital ICD9 codes, Cancer Registry histologyClinic & Hospital ICD9 codes, Cancer Registry histology codes, Medical record text reports: chart, pathologycodes, Medical record text reports: chart, pathology ● Verified diagnosis for each patientVerified diagnosis for each patient ● pathology reportspathology reports ● other chart reportsother chart reports ● For each query calculated specificity and sensitivityFor each query calculated specificity and sensitivity
    • 26. GeneSys SI queries to find follicular lymphoma patientsGeneSys SI queries to find follicular lymphoma patients QUERYQUERY SOURCESOURCE CRITERIACRITERIA QCQC Cancer Registry NHL patientsCancer Registry NHL patients NHL between 1985-2002NHL between 1985-2002 Q1Q1 Cancer Registry, histology (ICD-0)Cancer Registry, histology (ICD-0) 9690, 9691, 9695, 96989690, 9691, 9695, 9698 Q2Q2 Text search - pathology reportsText search - pathology reports ““follicular” near “lymphoma”follicular” near “lymphoma” Q3Q3 Text search - pathology reportsText search - pathology reports ““follicular lymphoma”follicular lymphoma” Q4Q4 Text search - all medical recordsText search - all medical records ““follicular” near “lymphoma”follicular” near “lymphoma” Q5Q5 Text search - all medical recordsText search - all medical records ““follicular lymphoma”follicular lymphoma” Q6Q6 Clinic ICD-9 diagnosis codesClinic ICD-9 diagnosis codes 202.0, 202.00, 202.01, 202.02,202.0, 202.00, 202.01, 202.02, 202.03, 202.04, 202.05, 202.06,202.03, 202.04, 202.05, 202.06, 202.07, 202.08202.07, 202.08 Q7Q7 Hospital ICD-9 diagnosis codesHospital ICD-9 diagnosis codes (same ICD9 codes)(same ICD9 codes) Q8Q8 Query 2 + 6Query 2 + 6 (criteria for query 2 OR 6)(criteria for query 2 OR 6) Q9Q9 Query 4 + 6Query 4 + 6 (criteria for query 4 OR 6)(criteria for query 4 OR 6) Q10Q10 Query 1 + 2Query 1 + 2 (criteria for query 1 OR 2)(criteria for query 1 OR 2)
    • 27. Patients found with follicular lymphoma queriesPatients found with follicular lymphoma queries QUERYQUERY SOURCESOURCE PATIENTS RESULTSPATIENTS RESULTS QCQC Cancer Registry NHL patientsCancer Registry NHL patients 425425 Q1Q1 Cancer Registry, histology (ICD-0)Cancer Registry, histology (ICD-0) 242242 Q2Q2 Text search 1 – pathology reportsText search 1 – pathology reports 406406 Q3Q3 Text search 2 – pathology reportsText search 2 – pathology reports 126126 Q4Q4 Text search 1 – all medical recordsText search 1 – all medical records 531531 Q5Q5 Text search 2 – all medical recordsText search 2 – all medical records 193193 Q6Q6 Clinic ICD-9 codesClinic ICD-9 codes 901901 Q7Q7 Hospital ICD-9 codesHospital ICD-9 codes 288288 Q8Q8 Query 2 + 6Query 2 + 6 11371137 Q9Q9 Query 4 + 6Query 4 + 6 12331233 Q10Q10 Query 1 + 2Query 1 + 2 498498
    • 28. Schematic Diagram of Query OutcomesSchematic Diagram of Query Outcomes Q6Q6 Q4Q4QCQC Q2Q2 Q7Q7 Q1Q1 Q5Q5 Q3Q3
    • 29. Schematic Diagram of Query OutcomesSchematic Diagram of Query Outcomes n =1520 Other Diagnosis Follicular Lymphoma
    • 30. Schematic Diagram of Query OutcomesSchematic Diagram of Query Outcomes n =1520 Q1 Other Diagnosis Follicular Lymphoma
    • 31. RESULTS – Analysis of follicular lymphoma casesRESULTS – Analysis of follicular lymphoma cases Purple=Path verifiedPurple=Path verified Red =Chart verifiedRed =Chart verified White=Total verifiedWhite=Total verified Query# #Pat #True Pos #False Pos #True Neg #False NegQuery# #Pat #True Pos #False Pos #True Neg #False Neg Q1Q1 242242 151151++4444=195=195 2323++2424=47=47 765765++303303=1068=1068 145145++6565=210=210 Q2Q2 406406 269269++1919=288=288 102102++1616=118=118 686686++311311=997=997 2727++9090=117=117 Q3Q3 126126 9696++66=102=102 2121++33=24=24 767767++324324=1091=1091 200200++103103=303=303 Q4Q4 531531 279279++9494=373=373 131131++2727=158=158 657657++300300=957=957 1717++1515=32=32 Q5Q5 193193 123123++3636=159=159 2828++66=34=34 760760++321321=1081=1081 173173++7373=246=246 Q6Q6 901901 143143++3535=178=178 490490++233233=723=723 298298++9494=392=392 153153++7474=227=227 Q7Q7 288288 106106++3131=137=137 101101++5050=151=151 687687++277277=964=964 190190++7878=268=268 Q8Q8 11371137 280280++4343=323=323 569569++245245=814=814 219219++8282=301=301 1616++6666=82=82 Q9Q9 12331233 286286++102102=388=388 591591++254254=845=845 197197++7373=270=270 1010++77=17=17 Q10Q10 498498 285285++5252=337=337 123123++3838=161=161 665665++289289=954=954 1111++77=68=68
    • 32. Query#Query# # Case Identified Sensitivity Path Specificity Path Sensitivity All Notes Specificity All Notes Q1Q1 195 51% 97% 48% 96% Q2Q2 288 91% 87% 71% 89% Q3Q3 102 32% 97% 25% 98% Q4Q4 373 94% 83% 92% 86% Q5Q5 159 42% 96% 39% 97% Q6Q6 178 48% 38% 44% 35% Q7Q7 137 36% 87% 34% 86% Q8Q8 323 95% 28% 80% 27% Q9Q9 388 97% 25% 96% 24% Q10Q10 337 96% 84% 48% 86% * For sensitivity and specificity calculations, numbers of true and false negatives were based on the total population of patients unique to these queries (1520 pts; 1084 pt w/ path) and not the entire patient population in GeneSys SI. Results: Algorithms Sensitivity & SpecificityResults: Algorithms Sensitivity & Specificity
    • 33. Query#Query# # Case Identified Sensitivity Path Specificity Path Sensitivity All Notes Specificity All Notes Q1Q1 195 51% 97% 48% 96% Q2Q2 288 91% 87% 71% 89% Q3Q3 102 32% 97% 25% 98% Q4Q4 373 94% 83% 92% 86% Q5Q5 159 42% 96% 39% 97% Q6Q6 178 48% 38% 44% 35% Q7Q7 137 36% 87% 34% 86% Q8Q8 323 95% 28% 80% 27% Q9Q9 388 97% 25% 96% 24% Q10Q10 337 96% 84% 48% 86% * For sensitivity and specificity calculations, numbers of true and false negatives were based on the total population of patients unique to these queries (1520 pts; 1084 pt w/ path) and not the entire patient population in GeneSys SI. Results: Algorithms Sensitivity & SpecificityResults: Algorithms Sensitivity & Specificity
    • 34. Query#Query# # Case Identified Sensitivity Path Specificity Path Sensitivity All Notes Specificity All Notes Q1Q1 195 51% 97% 48% 96% Q2Q2 288 91% 87% 71% 89% Q3Q3 102 32% 97% 25% 98% Q4Q4 373 94% 83% 92% 86% Q5Q5 159 42% 96% 39% 97% Q6Q6 178 48% 38% 44% 35% Q7Q7 137 36% 87% 34% 86% Q8Q8 323 95% 28% 80% 27% Q9Q9 388 97% 25% 96% 24% Q10Q10 337 96% 84% 48% 86% * For sensitivity and specificity calculations, numbers of true and false negatives were based on the total population of patients unique to these queries (1520 pts; 1084 pt w/ path) and not the entire patient population in GeneSys SI. Results: Algorithms Sensitivity & SpecificityResults: Algorithms Sensitivity & Specificity
    • 35. Query#Query# # Case Identified Sensitivity Path Specificity Path Sensitivity All Notes Specificity All Notes Q1Q1 195 51% 97% 48% 96% Q2Q2 288 91% 87% 71% 89% Q3Q3 102 32% 97% 25% 98% Q4Q4 373 94% 83% 92% 86% Q5Q5 159 42% 96% 39% 97% Q6Q6 178 48% 38% 44% 35% Q7Q7 137 36% 87% 34% 86% Q8Q8 323 95% 28% 80% 27% Q9Q9 388 97% 25% 96% 24% Q10Q10 337 96% 84% 48% 86% * For sensitivity and specificity calculations, numbers of true and false negatives were based on the total population of patients unique to these queries (1520 pts; 1084 pt w/ path) and not the entire patient population in GeneSys SI. Results: Algorithms Sensitivity & SpecificityResults: Algorithms Sensitivity & Specificity
    • 36. ROC Plot for Search AlgorithmsROC Plot for Search Algorithms Q6 Q7 Q8 Q9 Q4Q2 Q3 Q10 Q5 Q1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 20% 40% 60% 80% 100% 1 - Specificity Sensitivity
    • 37. ● Highest SensitivityHighest Sensitivity ● Free Text search w/ near algorithmFree Text search w/ near algorithm ● Combination queriesCombination queries ● Highest SpecificityHighest Specificity ● Cancer Registry code, Free Text query “follicularCancer Registry code, Free Text query “follicular lymphoma”lymphoma” ● Limiting search to pathology reports improvesLimiting search to pathology reports improves specificityspecificity ● Best Overall PerformanceBest Overall Performance ● Free Text query “follicular lymphoma” +/- CancerFree Text query “follicular lymphoma” +/- Cancer Registry codeRegistry code ConclusionsConclusions
    • 38. ● Use query results for outcomes researchUse query results for outcomes research on FL (n=405)on FL (n=405) ● Test query algorithms for:Test query algorithms for: ● other Non-Hodgkin’s lymphomaother Non-Hodgkin’s lymphoma ● Breast ca., prostate ca., colorectal ca.Breast ca., prostate ca., colorectal ca. ● Develop and test query algorithms forDevelop and test query algorithms for treatments and outcomestreatments and outcomes ● Modify the query engine and interface toModify the query engine and interface to automate algorithmsautomate algorithms Future DirectionsFuture Directions
    • 39. Winship Cancer InstituteWinship Cancer Institute Oncology InformaticsOncology Informatics ● Leroy HillLeroy Hill ● Michael Graiser, PhDMichael Graiser, PhD ● Rochelle VictorRochelle Victor ● Ragini Kudchadkar, MDRagini Kudchadkar, MD ● Susan Moore MD, MPHSusan Moore MD, MPH ●Bonita Feinstein RNBonita Feinstein RN ●Ashley HilliardAshley Hilliard ●James YangJames Yang ●John TumehJohn Tumeh ●Simone ParkerSimone Parker
    • 40. Potential ProjectsPotential Projects ● Cancer Outcomes ResearchCancer Outcomes Research ● Genomic Discovery / PharmacogenomicsGenomic Discovery / Pharmacogenomics ● Clinical Trials SupportClinical Trials Support ● Medical InformaticsMedical Informatics
    • 41. Cancer Outcomes ResearchCancer Outcomes Research ● Examining Treatment Strategies & Outcomes forExamining Treatment Strategies & Outcomes for Fludarabine Refractory CLLFludarabine Refractory CLL ● The influence of Comorbidity on Outcome in patientsThe influence of Comorbidity on Outcome in patients undergoing Allogeneic Transplantationundergoing Allogeneic Transplantation Other Cancer TreatmentsOther Cancer Treatments ● Examining Treatment Strategies & Outcomes forExamining Treatment Strategies & Outcomes for Relapsed Follicular LymphomaRelapsed Follicular Lymphoma ● Management of Squamous Cell Cancer of the AnusManagement of Squamous Cell Cancer of the Anus (Reducing Surgical Morbidity)(Reducing Surgical Morbidity) ● Examining Regimen-Related ToxicityExamining Regimen-Related Toxicity
    • 42. PharmacogenomicsPharmacogenomics ● Provide utilization data for cost-effectivenessProvide utilization data for cost-effectiveness studiesstudies ● Provide resources to support observationalProvide resources to support observational studies and clinical trials instudies and clinical trials in pharmacogenomicspharmacogenomics ● Resource for developing algorithms forResource for developing algorithms for pattern recognitionpattern recognition
    • 43. Clinical Trials SupportClinical Trials Support ● Screening algorithms for identifying patientsScreening algorithms for identifying patients eligible for clinical trialseligible for clinical trials ● Identify populations that would permit clinicalIdentify populations that would permit clinical trial investigationtrial investigation ● Data resource for monitoring trial outcomesData resource for monitoring trial outcomes Regimen-related toxicityRegimen-related toxicity Treatment ResponseTreatment Response SurvivalSurvival
    • 44. Medical InformaticsMedical Informatics ● Advanced database search algorithmsAdvanced database search algorithms Pattern RecognitionPattern Recognition Neural NetworksNeural Networks Bayesian NetworksBayesian Networks Hierarchical Statistical ModelsHierarchical Statistical Models
    • 45. caCORE Enterprise Vocabulary Common Data Elements Biomedical Objects Scientific ApplicationsScientific Applications
    • 46. Common Data Elements (CDEs) Data descriptors or “metadata” for cancer research Precisely defining the questions and answers  What question are you asking, exactly?  What are the possible answers, and what do they mean? Ongoing projects covering various domains  Clinical Trials  Imaging  Biomarkers  Genomics
    • 47. caBIO Overview Software industry design paradigms  Unified Modeling Language (UML) representations of biomedical “objects”  Java 2 Enterprise Edition “n-tier” system architecture Broad coverage of biomedicine (but not comprehensive yet):  Genomics  Gene expression  Model systems for cancer  Human clinical trials Data “on-tap” via application programming interfaces
    • 48. Cancer Clinical Database Application SystemCancer Clinical Database Application System Web Form GenerationWeb Form Generation
    • 49. Web form input fields for Cancer Chemotherapy
    • 50. Configurable column attributes for the Cancer Chemotherapy form