Mapping of ICD-O Tuples to OncoTree Codes Using SNOMED CT Post-Coordination

Area of Science:

• Oncology
• Medical Informatics
• Bioinformatics

Background:

• Approximately 500,000 new oncological diseases are diagnosed annually in Germany.
• Cancer data is documented using the International Classification of Diseases for Oncology (ICD-O).
• OncoTree is utilized for integrating novel research findings in oncology.

Purpose of the Study:

• To develop a semi-automatic mapping method for translating ICD-O codes to OncoTree.
• To assess the feasibility of using SNOMED CT as an intermediate mapping step.
• To evaluate the accuracy and applicability of the developed mapping approach in clinical settings.

Main Methods:

• Implementation of a FHIR terminology server.
• Utilization of pre-coordinated and post-coordinated SNOMED CT expressions.
• Application of subsumption testing and various validation techniques.
• Comparison of results with scientific literature and manual evaluation by a senior pathologist.

Main Results:

• The developed semi-automatic approach achieved an 84% agreement with manual mapping.
• SNOMED CT, particularly its post-coordinated expressions, proved viable as an intermediate mapping step.
• Validation confirmed the general applicability of SNOMED CT in oncological data classification.

Conclusions:

• The developed semi-automatic mapping method demonstrates significant potential for routine medical practice.
• This approach facilitates better integration of cancer research findings into clinical documentation.
• The use of SNOMED CT enhances the interoperability and standardization of oncological data.