JCO clinical cancer informatics · 2026
Bridging Clinical Observational Research and Data Standards in Head and Neck Cancer Care: Scoping Review and Mapping of Clinical Data Elements.
СтатьяОбзорНазвание 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.
Valerie Ivy Cerelia, Aly Farhannah, Niknam Shirvan Ghazaleh, Alam Fahim Irfan, Johnston Meredith, Kong Benjamin Y, Findlay Merran, Holloway Lois, Kennedy Georgina
Аннотация
PURPOSE: A scoping review and mapping of clinical data elements in observational studies were conducted to assess head and neck cancer (HNC) clinical data, alignment with existing observational oncology data standards, and the demonstrated opportunities for using these data standards in observational studies.
METHODS: The scoping review was performed to systematically find and extract data elements from real-world studies emphasizing HNC treatment patterns. Text-embedding-assisted and manual mapping was performed to map data elements to their representations in HNC Ontology, Operational Ontology for Oncology, Minimal Common Oncology Data Elements, and the Observational Medical Outcomes Partnership Common Data Model. Study characteristics, data elements and their mapping, and proportion of clinical domains represented were analyzed. Demonstrated opportunities for using standard data elements were reviewed based on the mapping results.
RESULTS: A total of 98 observational studies were included. National databases were the most common data source (59.2%), and disease trajectory outcomes were the most frequently reported study end point (95.9%). Overall, 1,613 data elements were reported and classified into five domains: diagnosis (1.2%), health system (5.7%), tumor (27.2%), person (32.7%), and treatment (33.2%). Mapping coverage varied across domains, with 85%-100% coverage in diagnosis, 83.6%-95.2% in treatment, 85.3%-97.8% in tumor, 50.7%-89% in person, and 43.8%-81.2% in health system domains. Demonstrated opportunities for applying data standardization include facilitating geographical links to personal data elements, streamlining value derivation of longitudinally tracked data elements, and deriving complex treatment constructs.
CONCLUSION: The existing data standards and associated terminology systems covered most of the clinical observational data elements, but gaps remain in structuring complex treatment factors that may affect patient outcomes. Resulting mapping offers generalizable representations across standards, laying the groundwork for further refinement through collaborative data element curation from local conventions under the guidance of data standard experts.
METHODS: The scoping review was performed to systematically find and extract data elements from real-world studies emphasizing HNC treatment patterns. Text-embedding-assisted and manual mapping was performed to map data elements to their representations in HNC Ontology, Operational Ontology for Oncology, Minimal Common Oncology Data Elements, and the Observational Medical Outcomes Partnership Common Data Model. Study characteristics, data elements and their mapping, and proportion of clinical domains represented were analyzed. Demonstrated opportunities for using standard data elements were reviewed based on the mapping results.
RESULTS: A total of 98 observational studies were included. National databases were the most common data source (59.2%), and disease trajectory outcomes were the most frequently reported study end point (95.9%). Overall, 1,613 data elements were reported and classified into five domains: diagnosis (1.2%), health system (5.7%), tumor (27.2%), person (32.7%), and treatment (33.2%). Mapping coverage varied across domains, with 85%-100% coverage in diagnosis, 83.6%-95.2% in treatment, 85.3%-97.8% in tumor, 50.7%-89% in person, and 43.8%-81.2% in health system domains. Demonstrated opportunities for applying data standardization include facilitating geographical links to personal data elements, streamlining value derivation of longitudinally tracked data elements, and deriving complex treatment constructs.
CONCLUSION: The existing data standards and associated terminology systems covered most of the clinical observational data elements, but gaps remain in structuring complex treatment factors that may affect patient outcomes. Resulting mapping offers generalizable representations across standards, laying the groundwork for further refinement through collaborative data element curation from local conventions under the guidance of data standard experts.