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Pathology data sharing United States Military Cancer Institute Walter Reed Army Medical Center November 16, 2004 Jules J. Berman, Ph.D., M.D. Program Director, Pathology Informatics Cancer Diagnosis Program, NCI, NIH email: [email protected] UFO Abductees Lots of them

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Pathology data sharing

United States Military Cancer Institute

Walter Reed Army Medical Center

November 16, 2004

Jules J. Berman, Ph.D., M.D.

Program Director, Pathology Informatics

Cancer Diagnosis Program, NCI, NIH

email: [email protected]


UFO Abductees

Lots of them

They often say about the same thing (independent confirmations)

All walks of life

Minority are a little crazy

Mostly honest and rational people

One problem: no evidence


Researchers who don’t publish their primary data

Lots of them

They often say about the same thing (independent confirmations)

All walks of life

Minority are a little crazy

Mostly honest and rational people

One problem: no evidence


After your research data reaches a certain size, the data becomes the publication, and the journal articles become tiny editorials that describe or interpret the data


In a data-intensive world, the data is the center of the universe. Manuscripts are satellites revolving around a central large BLOB of data.


Sticks and carrots: universe. Manuscripts are satellites revolving around a central large BLOB of data.

NIH Statement on Data Sharing

http://grants.nih.gov/grants/guide/notice-files/NOT-OD-03-032.html

National Research Council UPSIDE Universal Principle of Sharing Integral Data Expeditiously

http://books.nap.edu/books/0309088593/html/R1.html


NIH Funding for data sharing universe. Manuscripts are satellites revolving around a central large BLOB of data.

Shared Pathology Informatics Network

http://grants.nih.gov/grants/guide/rfa-files/RFA-CA-01-006.html

Tools for collaborations that involve data sharing

http://grants1.nih.gov/grants/guide/pa-files/PAR-03-134.html

Infrastructure for data sharing and archiving

http://grants.nih.gov/grants/guide/rfa-files/RFA-HD-03-032.html

caBIG

http://cabig.nci.nih.gov/


Confidentiality methods universe. Manuscripts are satellites revolving around a central large BLOB of data.


Two U.S. regulations that tell us how we can use medical records in research:

Common Rule & HIPAA Privacy Rule

In pathology informatics, the most ambitious research typically involves hundreds of thousands or millions of patient records. Getting informed consent for these studies is not feasible.

HIPAA and Common Rule both work under the principle that medical research is good, and it can be conducted without getting patient consent if you can come up with a way to avoid harming patients (no harm, no consent for harm).


Hipaa
hipaa records in research:


IRB records in research:


Corporate lawyer
Corporate Lawyer records in research:


Irate human subject
Irate Human Subject records in research:


Principle investigator
Principle Investigator records in research:


Articles: records in research:

Berman JJ. Confidentiality for Medical Data Miners. Artificial Intelligence in Medicine. 26(1-2):25-36, 2002.

Berman JJ. Concept-Match Medical Data Scrubbing: How pathology datasets can be used in research. Arch Pathol Lab Med. 2003 Jun;127(6):680-6.

Berman JJ. Threshold protocol for the exchange of confidential medical data. BMC Medical Research Methodology, 2002, 2:12.


More: records in research:

Berman JJ. A tool for sharing annotated research data: the "Category 0" UMLS (Unified Medical Language System) vocabularies. BMC Med Inform Decis Mak. 2003 Jun 16;3(1):6.

Berman JJ. Zero-check: a zero-knowledge protocol for reconciling patient identities across institutions.Arch Pathol Lab Med. 2004 Mar;128(3):344-6.

Berman JJ. Racing to share pathology data. Am J Clin Pathol. 2004 Feb;121(2):169-71 (editorial).



Director’s Challenge: Toward a molecular classification of tumors

In January 1999, the U.S. National Cancer Institute (NCI) issued a challenge to the scientific community "to harness the power of comprehensive molecular analysis technologies to make the classification of tumors vastly more informative. This challenge is intended to lay the groundwork for changing the basis of tumor classification from morphological to molecular characteristics."



Classifications are not: classification

Identification systems

Taxonomies and nomenclatures

Ontologies

Achieved by analyzing gene expression array data


What is a [tumor] classification? classification

A grouped taxonomy [listing of all tumors] with the following properties:

Inheritance: Hierarchical structure, with each class of tumors inheriting properties of its ancestors

Uniqueness: Each tumor occurs in only one place in the classification

Comprehensive: All tumors are included

Intransitive: A tumor from one class does not change into a tumor from another class (e.g. an adenocarcinoma does not become a lymphoma)


Problems with current tumor classifications classification

1. Created piecemeal

2. Often based on medical disciplines

3. A given tumor can appear redundantly when subclassifications are merged

4. No tumor classification has been prepared in a standard format designed to exchange, merge or analyze heterogeneous biological data


New Tumor Classification classification

Comprehensive ~122,000 terms (9 Megabytes)

Based on developmental and molecular biologic features of tumors

Heritable class structure with a unique class location for each tumor

XML document that can be cross-annotated with molecular biology databases

Preserves current tumor names, while abandoning purely morphologic categories (e.g. epithelial/stromal)


Latest version of the nomenclature: classification

http://www.pathologyinformatics.org/informatics_r.htm

122,000+ terms

Copy of paper:

Berman JJ. Tumor classification: molecular analysis meets Aristotle. BMC Cancer 2004 4:10, 17 March 2004

http://www.biomedcentral.com/1471-2407/4/10



XML is the greatest information organizing tool since the invention of the book.

Much more important than HTML

Takes advantage of:

Metadata

Namespaces

Internet

External links


Example: Tissue Microarray Data Exchange Specification invention of the book.

The TMA Specification is an open access document that can be used without any restriction.

Its development was sponsored by the NCI and by the Association for Pathology Informatics


Basics of the specification: invention of the book.

Jules J Berman, Mary Edgerton and Bruce Friedman. The tissue microarray data exchange specification: a community-based, open source tool for sharing tissue microarray data. BMC Med Inform Decis Mak. 2003 May 23;3:5

Real-world implementation example:

Jules J Berman, Milton Datta, Andre Kajdacsy-Balla, Jonathan Melamed, Jan Orenstein, Kevin Dobbin, Ashok Patel, Rajiv Dhir, Michael J Becich. The tissue microarray data exchange specification: implementation by the Cooperative Prostate Cancer Tissue Resource. BMC Bioinformatics 2004 Feb 27, 5:19


LDIP (Laboratory Digital Imaging Project) invention of the book.

Association for Pathology Informatics

Pathology Image Data Exchange Specification

Information available at:

http://www.pathologyinformatics.org/ldip.htm


end invention of the book.


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