The CAP cancer protocols--a case study of caCORE based data standards implementation to integrate with the Cancer Biomedical Informatics Grid

Abstract

Background: The Cancer Biomedical Informatics Grid (caBIG) is a network of individuals and institutions, creating a world wide web of cancer research. An important aspect of this informatics effort is the development of consistent practices for data standards development, using a multi-tier approach that facilitates semantic interoperability of systems. The semantic tiers include (1) information models, (2) common data elements, and (3) controlled terminologies and ontologies. The College of American Pathologists (CAP) cancer protocols and checklists are an important reporting standard in pathology, for which no complete electronic data standard is currently available.

Methods: In this manuscript, we provide a case study of Cancer Common Ontologic Representation Environment (caCORE) data standard implementation of the CAP cancer protocols and checklists model--an existing and complex paper based standard. We illustrate the basic principles, goals and methodology for developing caBIG models.

Results: Using this example, we describe the process required to develop the model, the technologies and data standards on which the process and models are based, and the results of the modeling effort. We address difficulties we encountered and modifications to caCORE that will address these problems. In addition, we describe four ongoing development projects that will use the emerging CAP data standards to achieve integration of tissue banking and laboratory information systems.

Conclusion: The CAP cancer checklists can be used as the basis for an electronic data standard in pathology using the caBIG semantic modeling methodology.

Figure 1

Fragment of CAP cancer checklist for melanoma. Fragment of CAP cancer checklist for cutaneous melanoma showing relationships to ISO/IEC Administered Components. Checklist text reproduced with permission of the College of American Pathologists.

Figure 2

Relationships of semantic model layers. The threelayers of the semantic model include Layer II: information model, Layer III: semantic metadata and Layer IV: controlled terminologies. The figure illustrates two information systems using different class and attribute names, but annotating these UMLentities with the same concept. The resulting CDE will be shared between systems.

Figure 3

Fragment of UML information model for melanoma. Fragment of the CAP checklist UML class model showing association and generalization relationships of CutaneousMelanoma.

Figure 4

Detailed view of structural relationships among information models, ontology, and semantic metadata. CDEs are derived from ontology-annotated UML models. The figure depicts a single class with two attributes generating (a) CDE with a non-enumerated value domain, and (b) CDE with an enumerated value domain. A single NCI Thesaurus annotation is shown for the concept Cutaneous Melanoma. The relationship of the ISO/IEC metadata to UML model and NCI Thesaurus is also shown.

Figure 5

CAE manual annotation interface. The generic CAE user interface utilizes CAP cancer checklist metadata to populate screens, and drop down boxes, and reuses objects from the CAP cancer checklist UML model as part of the CAE information model. In this figure, value domains by reference are indicated with the tree icon. The user can browse and select any value below the root node designated in the metadata.