MedElevator
Войти в исследования International journal of radiation oncology, biology, physics · 2026
Harmonizing Radiotherapy Data Standards: A Comparative Analysis of OMOP, CDISC, FHIR, NAACCR, and OBO Foundry with an O3-Anchored Mapping Framework.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Xiao Ying, Kennedy Georgina, Baker Rebecca L, Savannah Kari J Brewer, Brock Kristy K, Covington Elizabeth L, Gabriel Peter E, Golozar Asieh, Gurley Michael, Hu Chen, Manfredi Denise, Matuszak Martha M, Mayo Katherine, Moughan Jennifer, O'Meara Elizabeth A, Reich Christian, Holloway Lois, Mayo Charles S
Аннотация
PURPOSE: This Special Report analyzes current radiotherapy data standards, determines which radiotherapy data elements should propagate between them, and identifies the barriers to that propagation to improve interoperability and data utilization in clinical care and research.
METHODS: We compared five data standards-OMOP, CDISC SDTM, FHIR, NAACCR, and the Operational Ontology for Oncology (O3)-and one ontology framework, the OBO Foundry, for radiotherapy data representation, strengths, limitations, and implementation maturity, and examined harmonization initiatives including NRG's O3-based conversion frameworks, CodeX mCODE, and ESTRO and IAEA efforts. We then scored a 90-item benchmark drawn from radiotherapy operational and trial-reporting requirements, partitioned the radiotherapy items by whether and where they should propagate, and matched each barrier to a remediation route and owning body.
RESULTS: Counting only unqualified matches, O3 covered 46 of 62 radiotherapy-specific items (74%) versus 15 (24%) for OMOP, 12 (19%) for NAACCR, 11 (18%) for FHIR, and none for OBO Foundry; on the 28 general clinical items the ordering reversed (OMOP 79%, FHIR 71%, NAACCR 46%, O3 25%, OBO Foundry 4%). Each standard is strongest on the items its target function was built to serve. Wholesale propagation is therefore unwarranted: 20 course-summary elements should converge across standards, 26 planning and adaptive elements warrant selective adoption, and 16 voxel- and machine-level artifacts should be referenced by DICOM unique identifier rather than replicated.
CONCLUSIONS: Harmonization requires ontology-driven approaches using O3 as the semantic backbone with mappings to FHIR, OMOP and CDISC, applied to a defined tier of elements rather than the full element set. The principal barriers are process barriers-structural, terminological, burden-related and tooling-related-each with a named owner and an existing route. Standardized formats alone are insufficient; they require robust information systems, validation, and governance. Harmonization improves real-world data utility but cannot resolve biases inherent to nonrandomized data. Coordinated international collaboration remains critical for advancing precision radiation oncology.
METHODS: We compared five data standards-OMOP, CDISC SDTM, FHIR, NAACCR, and the Operational Ontology for Oncology (O3)-and one ontology framework, the OBO Foundry, for radiotherapy data representation, strengths, limitations, and implementation maturity, and examined harmonization initiatives including NRG's O3-based conversion frameworks, CodeX mCODE, and ESTRO and IAEA efforts. We then scored a 90-item benchmark drawn from radiotherapy operational and trial-reporting requirements, partitioned the radiotherapy items by whether and where they should propagate, and matched each barrier to a remediation route and owning body.
RESULTS: Counting only unqualified matches, O3 covered 46 of 62 radiotherapy-specific items (74%) versus 15 (24%) for OMOP, 12 (19%) for NAACCR, 11 (18%) for FHIR, and none for OBO Foundry; on the 28 general clinical items the ordering reversed (OMOP 79%, FHIR 71%, NAACCR 46%, O3 25%, OBO Foundry 4%). Each standard is strongest on the items its target function was built to serve. Wholesale propagation is therefore unwarranted: 20 course-summary elements should converge across standards, 26 planning and adaptive elements warrant selective adoption, and 16 voxel- and machine-level artifacts should be referenced by DICOM unique identifier rather than replicated.
CONCLUSIONS: Harmonization requires ontology-driven approaches using O3 as the semantic backbone with mappings to FHIR, OMOP and CDISC, applied to a defined tier of elements rather than the full element set. The principal barriers are process barriers-structural, terminological, burden-related and tooling-related-each with a named owner and an existing route. Standardized formats alone are insufficient; they require robust information systems, validation, and governance. Harmonization improves real-world data utility but cannot resolve biases inherent to nonrandomized data. Coordinated international collaboration remains critical for advancing precision radiation oncology.