Pathology data sharing

United States Military Cancer InstituteWalter Reed Army Medical CenterNovember 16, 2004

Jules J. Berman, Ph.D., M.D.Program Director, Pathology InformaticsCancer Diagnosis Program, NCI, NIHemail: bermanj@mail.nih.gov

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:

NIH Statement on Data Sharinghttp://grants.nih.gov/grants/guide/notice-files/NOT-OD-03-032.html

National Research Council UPSIDE Universal Principle of Sharing Integral Data Expeditiouslyhttp://books.nap.edu/books/0309088593/html/R1.html

NIH Funding for data sharing

Shared Pathology Informatics Networkhttp://grants.nih.gov/grants/guide/rfa-files/RFA-CA-01-006.html

Tools for collaborations that involve data sharinghttp://grants1.nih.gov/grants/guide/pa-files/PAR-03-134.html

Infrastructure for data sharing and archivinghttp://grants.nih.gov/grants/guide/rfa-files/RFA-HD-03-032.html

caBIGhttp://cabig.nci.nih.gov/

Confidentiality methods

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

IRB

Corporate Lawyer

Irate Human Subject

Principle Investigator

Articles:

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:

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).

Standard ways of organizing data (nomenclatures, taxonomies, classifications, data structures)

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."

Impediment: Misunderstanding about the definition of classification

Classifications are not:

Identification systems

Taxonomies and nomenclatures

Ontologies

Achieved by analyzing gene expression array data

What is a [tumor] 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

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

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:

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 2004http://www.biomedcentral.com/1471-2407/4/10

Standard ways of exchanging data

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

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:

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)

Association for Pathology Informatics

Pathology Image Data Exchange Specification

Information available at:

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

end