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Sherman S.,University of Nebraska Medical Center | Sherman S.,Progenomix Inc | Shats O.,University of Nebraska Medical Center | Shats O.,Progenomix Inc | And 15 more authors.
Cancer Informatics | Year: 2011

The Pancreatic Cancer Collaborative Registry (PCCR) is a multi-institutional web-based system aimed to collect a variety ofdata on pancreaticcancer patients and high-risk subjects in a standard and efficient way. The PCCR was initiated by a group of experts in medical oncology, gastroenterology, genetics, pathology, epidemiology, nutrition, and computer science with the goal of facilitating rapid and uniform collection of critical information and biological samples to be used in developing diagnostic, prevention and treatment strategies against pancreatic cancer. The PCCR is a multi-tier web application that utilizes Java/JSP technology and has Oracle 10 g database as a back-end. The PCCR uses a "confederation model" that encourages participation of any interested center, irrespective of its size or location. The PCCR utilizes a standardized approach to data collection and reporting, and uses extensive validation procedures to prevent entering erroneous data. The PCCR controlled vocabulary is harmonized with the NCI Thesaurus (NCIt) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT). The PCCR questionnaire has accommodated standards accepted in cancer research and healthcare. Currently, seven cancer centers in the USA, as well as one center in Italy are participating in the PCCR. At present, the PCCR database contains data on more than 2,700 subjects (PC patients and individuals at high risk of getting this disease). The PCCR has been certified by the NCI Center for Biomedical Informatics and Information Technology as a cancer Biomedical Informatics Grid (caBIG®) Bronze Compatible product. The PCCR provides a foundation for collaborative PC research. It has all the necessary prerequisites for subsequent evolution of the developed infrastructure from simply gathering PC-related data into a biomedical computing platform vital for successful PC studies, care and treatment. Studies utilizing data collected in the PCCR may engender new approaches to disease prognosis, risk factor assessment, and therapeutic interventions. © the author(s), publisher and licensee Libertas Academica Ltd.


Sherman S.,University of Nebraska Medical Center | Sherman S.,Progenomix Inc | Shats O.,University of Nebraska Medical Center | Shats O.,Progenomix Inc | And 18 more authors.
Cancer Informatics | Year: 2011

The Breast Cancer Collaborative Registry (BCCR) is a multicenter web-based system that efficiently collects and manages a variety of data on breast cancer (BC) patients and BC survivors. This registry is designed as a multi-tier web application that utilizes Java Servlet/JSP technology and has an Oracle 11g database as a back-end. The BCCR questionnaire has accommodated standards accepted in breast cancer research and healthcare. By harmonizing the controlled vocabulary with the NCI Thesaurus (NCIt) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), the BCCR provides a standardized approach to data collection and reporting. The BCCR has been recently certified by the National Cancer Institute's Center for Biomedical Informatics and Information Technology (NCI CBIIT) as a cancer Biomedical Informatics Grid (caBIG®) Bronze Compatible product. The BCCR is aimed at facilitating rapid and uniform collection of critical information and biological samples to be used in developing diagnostic, prevention, treatment, and survivorship strategies against breast cancer. Currently, seven cancer institutions are participating in the BCCR that contains data on almost 900 subjects (BC patients and survivors, as well as individuals at high risk of getting BC). © the author(s), publisher and licensee Libertas Academica Ltd.


Mdzinarishvili T.,University of Nebraska Medical Center | Sherman A.,University of Nebraska Medical Center | Shats O.,University of Nebraska Medical Center | Shats O.,Progenomix Inc | And 2 more authors.
Cancer Informatics | Year: 2014

A computational approach for estimating the overall, population, and individual cancer hazard rates was developed. The population rates characterize a risk of getting cancer of a specific site/type, occurring within an age-specific group of individuals from a specified population during a distinct time period. The individual rates characterize an analogous risk but only for the individuals susceptible to cancer. The approach uses a novel regulariza-tion and anchoring technique to solve an identifiability problem that occurs while determining the age, period, and cohort (APC) effects. These effects are used to estimate the overall rate, and to estimate the population and individual cancer hazard rates. To estimate the APC effects, as well as the population and individual rates, a new web-based computing tool, called the CancerHazard@Age, was developed. The tool uses data on the past and current history of cancer incidences collected during a long time period from the surveillance databases. The utility of the tool was demonstrated using data on the female lung cancers diagnosed during 1975–2009 in nine geographic areas within the USA. The developed tool can be applied equally well to process data on other cancer sites. The data obtained by this tool can be used to develop novel carcinogenic models and strategies for cancer prevention and treatment, as well as to project future cancer burden. © the authors, publisher and licensee Libertas Academica Limited.


Mdzinarishvili T.,Eppley Cancer Institute | Gleason M.X.,Eppley Cancer Institute | Sherman S.,Progenomix Inc.
Proceedings of the Annual Hawaii International Conference on System Sciences | Year: 2010

The influence of time period and birth cohort effects on age-specific incidence rates of pancreatic cancer (PC) and kidney cancer (KC) in white males and females was analyzed using SEER 9 data collected during 1975-2004. To estimate these effects, we used our novel approach that is applicable for any arbitrary hazard function. By utilizing the incidence rates of first primary, microscopically confirmed cases observed in six five-year time periods and 17 cohorts, we found turnovers in the PC and KC incidence rate distributions at old ages. For PC, no systematic changes were revealed in both time period and cohort coefficients during the considered time observation. For KC, however, we found systematically increasing trends for time period coefficients, while trends of birth cohort effect coefficients remain unchangeable during 1975-2004. The proposed approach can be used in the mathematical modeling of different types of carcinogenesis. © 2010 IEEE.


Shats O.,University of Nebraska Medical Center | Shats O.,Progenomix Inc | Goldner W.,University of Nebraska Medical Center | Feng J.,University of Nebraska Medical Center | And 5 more authors.
Cancer Informatics | Year: 2016

A multicenter, web-based Thyroid Cancer and Tumor Collaborative Registry (TCCR, http://tccr.unmc.edu) allows for the collection and management of various data on thyroid cancer (TC) and thyroid nodule (TN) patients. The TCCR is coupled with OpenSpecimen, an open-source biobank management system, to annotate biospecimens obtained from the TCCR subjects. The demographic, lifestyle, physical activity, dietary habits, family history, medical history, and quality of life data are provided and may be entered into the registry by subjects. Information on diagnosis, treatment, and outcome is entered by the clinical personnel. The TCCR uses advanced technical and organizational practices, such as (i) metadata-driven software architecture (design); (ii) modern standards and best practices for data sharing and interoperability (standardization); (iii) Agile methodology (project management); (iv) Software as a Service (SaaS) as a software distribution model (operation); and (v) the confederation principle as a business model (governance). This allowed us to create a secure, reliable, user-friendly, and self-sustainable system for TC and TN data collection and management that is compatible with various end-user devices and easily adaptable to a rapidly changing environment. Currently, the TCCR contains data on 2,261 subjects and data on more than 28,000 biospecimens. Data and biological samples collected by the TCCR are used in developing diagnostic, prevention, treatment, and survivorship strategies against TC. © the authors.


Gleason M.X.,University of Nebraska Medical Center | Mdzinarishvili T.,University of Nebraska Medical Center | Are C.,University of Nebraska Medical Center | Sasson A.,University of Nebraska Medical Center | And 5 more authors.
Cancer Informatics | Year: 2013

The 18,352 pancreatic ductal adenocarcinoma (PDAC) cases from the Surveillance Epidemiology and End Results (SEER) database were analyzed using the Kaplan-Meier method for the following variables: race, gender, marital status, year of diagnosis, age at diagnosis, pancreatic subsite, T-stage, N-stage, M-stage, tumor size, tumor grade, performed surgery, and radiation therapy. Because the T-stage variable did not satisfy the proportional hazards assumption, the cases were divided into cases with T1-and T2-stages (localized tumor) and cases with T3-and T4-stages (extended tumor). For estimating survival and conditional survival probabilities in each group, a multivariate Cox regression model adjusted for the remaining covariates was developed. Testing the reproducibility of model parameters and generalizability of these models showed that the models are well calibrated and have concordance indexes equal to 0.702 and 0.712, respectively. Based on these models, a prognostic estimator of survival for patients diagnosed with PDAC was developed and implemented as a computerized web-based tool. © the author(s), publisher and licensee Libertas Academica Ltd.

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