TIGDx

TIGDx, short for Translational Imaging and Genomics Data Exchange, is a proposed framework intended to facilitate interoperability between biomedical imaging data and genomic data. The term is used in discussions of data integration to support research and clinical decision making by enabling combined analysis of radiographic images, molecular profiles, and associated clinical metadata.

Although not universally adopted, TIGDx has been described in scholarly articles and industry forums as a blueprint for a unified data model that harmonizes imaging formats (for example, DICOM) with genomic data representations (such as VCF/FASTQ-derived resources) and ontologies.

Core components include a shared metadata schema, a mapping layer to translate legacy data into the unified model, and governance mechanisms to manage consent, privacy, and access controls. The framework is designed to be implementation-agnostic and extensible, enabling integration with existing platforms and standards like HL7 FHIR, DICOM, and research data repositories.

Typical applications include radiogenomics studies, the prediction of treatment response, biomarker discovery, and multi-modal clinical decision support. Some projects aim to enable secure data sharing across institutions with privacy-preserving techniques such as de-identification and controlled access.

Criticism and challenges include fragmentation among competing data standards, the complexity of heterogeneous datasets, regulatory considerations, and resource requirements for deployment and maintenance. Proponents argue TIGDx can accelerate translational research by reducing data silos and enabling more comprehensive analyses.

Related topics include radiogenomics, DICOM, FHIR, and data governance.