The Integrated Proactive Surveillance System for Prostate Cancer

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Abstract

In this paper, we present the design and implementation of the integrated proactive surveillance system for prostate cancer (PASS-PC). The integrated PASS-PC is a multi-institutional web-based system aimed at collecting a variety of data on prostate cancer patients in a standardized and efficient way. The integrated PASS-PC was commissioned by the Prostate Cancer Foundation (PCF) and built through the joint of efforts by a group of experts in medical oncology, genetics, pathology, nutrition, and cancer research informatics. Their main goal is facilitating the efficient and uniform collection of critical demographic, lifestyle, nutritional, dietary and clinical information to be used in developing new strategies in diagnosing, preventing and treating prostate cancer.

The integrated PASS-PC is designed based on common industry standards – a three tiered architecture and a Service- Oriented Architecture (SOA). It utilizes open source software and programming languages such as HTML, PHP, CSS, JQuery, Drupal and MySQL. We also use a commercial database management system – Oracle 11g. The integrated PASS-PC project uses a “confederation model” that encourages participation of any interested center, irrespective of its size or location. The integrated PASS-PC utilizes a standardized approach to data collection and reporting, and uses extensive validation procedures to prevent entering erroneous data. The integrated PASS-PC controlled vocabulary is harmonized with the National Cancer Institute (NCI) Thesaurus. Currently, two cancer centers in the USA are participating in the integrated PASS-PC project.

The final system has three main components: 1. National Prostate Surveillance Network (NPSN) website; 2. NPSN myConnect portal; 3. Proactive Surveillance System for Prostate Cancer (PASS-PC). PASS-PC is a cancer Biomedical Informatics Grid (caBIG) compatible product. The integrated PASS-PC provides a foundation for collaborative prostate cancer research. It has been built to meet the short term goal of gathering prostate cancer related data, but also with the prerequisites in place for future evolution into a cancer research informatics platform. In the future this will be vital for successful prostate cancer studies, care and treatment.

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Active surveillance program for prostate cancer: an update of the Johns Hopkins experience.

Jeffrey Tosoian, B J Trock, Yuan Feng … (2011)

We assessed outcomes of men with prostate cancer enrolled in active surveillance. Since 1995, a total of 769 men diagnosed with prostate cancer have been followed prospectively (median follow-up, 2.7 years; range, 0.01 to 15.0 years) on active surveillance. Enrollment criteria were for very-low-risk cancers, defined by clinical stage (T1c), prostate-specific antigen density < 0.15 ng/mL, and prostate biopsy findings (Gleason score ≤ 6, two or fewer cores with cancer, and ≤ 50% cancer involvement of any core). Curative intervention was recommended on disease reclassification on the basis of biopsy criteria. The primary outcome was survival free of intervention, and secondary outcomes were rates of disease reclassification and exit from the program. Outcomes were compared between men who did and did not meet very-low-risk criteria. The median survival free of intervention was 6.5 years (range, 0.0 to 15.0 years) after diagnosis, and the proportions of men remaining free of intervention after 2, 5, and 10 years of follow-up were 81%, 59%, and 41%, respectively. Overall, 255 men (33.2%) underwent intervention at a median of 2.2 years (range, 0.6 to 10.2 years) after diagnosis; 188 men (73.7%) underwent intervention on the basis of disease reclassification on biopsy. The proportions of men who underwent curative intervention (P = .026) or had biopsy reclassification (P < .001) were significantly lower in men who met enrollment criteria than in those who did not. There were no prostate cancer deaths. For carefully selected men, active surveillance with curative intent appears to be a safe alternative to immediate intervention. Limiting surveillance to very-low-risk patients may reduce the frequency of adverse outcomes.

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Toward an ontology-based framework for clinical research databases.

Y. Megan Kong, Carl Dahlke, Qun Xiang … (2011)

Clinical research includes a wide range of study designs from focused observational studies to complex interventional studies with multiple study arms, treatment and assessment events, and specimen procurement procedures. Participant characteristics from case report forms need to be integrated with molecular characteristics from mechanistic experiments on procured specimens. In order to capture and manage this diverse array of data, we have developed the Ontology-Based eXtensible data model (OBX) to serve as a framework for clinical research data in the Immunology Database and Analysis Portal (ImmPort). By designing OBX around the logical structure of the Basic Formal Ontology (BFO) and the Ontology for Biomedical Investigations (OBI), we have found that a relatively simple conceptual model can represent the relatively complex domain of clinical research. In addition, the common framework provided by BFO makes it straightforward to develop data dictionaries based on reference and application ontologies from the OBO Foundry. Copyright © 2010 Elsevier Inc. All rights reserved.

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Is Open Access

Web-based data collection: detailed methods of a questionnaire and data gathering tool

Charles J Cooper, Sharon Cooper, Deborah Del Junco … (2006)

There have been dramatic advances in the development of web-based data collection instruments. This paper outlines a systematic web-based approach to facilitate this process through locally developed code and to describe the results of using this process after two years of data collection. We provide a detailed example of a web-based method that we developed for a study in Starr County, Texas, assessing high school students' work and health status. This web-based application includes data instrument design, data entry and management, and data tables needed to store the results that attempt to maximize the advantages of this data collection method. The software also efficiently produces a coding manual, web-based statistical summary and crosstab reports, as well as input templates for use by statistical packages. Overall, web-based data entry using a dynamic approach proved to be a very efficient and effective data collection system. This data collection method expedited data processing and analysis and eliminated the need for cumbersome and expensive transfer and tracking of forms, data entry, and verification. The code has been made available for non-profit use only to the public health research community as a free download [1].

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Author and article information

Journal

Journal ID (nlm-ta): Open Med Inform J

Journal ID (iso-abbrev): Open Med Inform J

Journal ID (publisher-id): TOMINFOJ

Title: The Open Medical Informatics Journal

Publisher: Bentham Open

ISSN (Electronic): 1874-4311

Publication date (Electronic): 2 March 2012

Publication date Collection: 2012

Volume: 6

Pages: 1-8

Affiliations

[1 ]Research Informatics Core, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA

[2 ]Clinical Research Office, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA

[3 ]Louis Warschaw Prostate Cancer Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA

[4 ]Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA

Author notes

[* ]Address correspondence to this author at the Research Informatics Core, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA; Tel: (01)310-423-3315; Fax: (01)310-423-4020; E-mail: haibin.wang@ 123456cshs.org

Article

Publisher ID: TOMINFOJ-6-1

DOI: 10.2174/1874431101206010001

PMC ID: 3322433

PubMed ID: 22505956

SO-VID: 58e1cf98-f47e-499b-a923-9e2dc9ed918f

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This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

History

Date received : 5 December 2011

Date revision received : 31 January 2012

Date accepted : 16 February 2012

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Most cited references 17

Active surveillance program for prostate cancer: an update of the Johns Hopkins experience.

Toward an ontology-based framework for clinical research databases.

Web-based data collection: detailed methods of a questionnaire and data gathering tool

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