ArticlePloS one2018
Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- Opportunities and Obstacles to the Development of Health Data Warehouses in Hospitals in France: The Recent Experience of Comprehensive Cancer Centers.International journal of environmental research and public health · 2023Review
- Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks.European radiology experimental · 2022Article
- RUBY: Natural Language Processing of French Electronic Medical Records for Breast Cancer Research.JCO clinical cancer informatics · 2022Article
- Application of informatics in cancer research and clinical practice: Opportunities and challenges.Cancer innovation · 2022Review
- Ontologies and Knowledge Graphs in Oncology Research.Cancers · 2022Review
- Biomedical Ontologies to Guide AI Development in Radiology.Journal of digital imaging · 2021Review
- A knowledge graph representation of baseline characteristics for the Dutch proton therapy research registry.Clinical and translational radiation oncology · 2021Article
- Deterioration of Intended Target Volume Radiation Dose Due to Anatomical Changes in Patients with Head-and-Neck Cancer.Cancers · 2021Article
- An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study.Journal of medical Internet research · 2021Article
- Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes.Yearbook of medical informatics · 2019Review
- Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician.Seminars in radiation oncology · 2019Review
- Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer.Scientific reports · 2018Article
- Integrating Multimodal Radiation Therapy Data into i2b2.Applied clinical informatics · 2018Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeLeveraging Electronic Health Records (EHR) and Oncology Information Systems (OIS) has great potential to generate hypotheses for cancer treatment, since they directly provide medical data on a large scale. In order to gather a significant amount of patients with a high level of clinical details, multicenter studies are necessary. A challenge in creating high quality Big Data studies involving several treatment centers is the lack of semantic interoperability between data sources. We present the ontology we developed to address this issue.
methodsRadiation Oncology anatomical and target volumes were categorized in anatomical and treatment planning classes. International delineation guidelines specific to radiation oncology were used for lymph nodes areas and target volumes. Hierarchical classes were created to generate The Radiation Oncology Structures (ROS) Ontology. The ROS was then applied to the data from our institution.
resultsFour hundred and seventeen classes were created with a maximum of 14 children classes (average = 5). The ontology was then converted into a Web Ontology Language (.owl) format and made available online on Bioportal and GitHub under an Apache 2.0 License. We extracted all structures delineated in our department since the opening in 2001. 20,758 structures were exported from our "record-and-verify" system, demonstrating a significant heterogeneity within a single center. All structures were matched to the ROS ontology before integration into our clinical data warehouse (CDW).
conclusionIn this study we describe a new ontology, specific to radiation oncology, that reports all anatomical and treatment planning structures that can be delineated. This ontology will be used to integrate dosimetric data in the Assistance Publique-Hôpitaux de Paris CDW that stores data from 6.5 million patients (as of February 2017).
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