Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology

<div><p>Purpose</p><p>Leveraging 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.</p><p>Methods</p><p>Radiation 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.</p><p>Results</p><p>Four 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).</p><p>Conclusion</p><p>In 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).</p></div

Map of the ROS ontology from the first superclass to the cervical lymph nodes area I class.

<p>Map of the ROS ontology from the first superclass to the cervical lymph nodes area I class.</p

Dose Volume Histogram for 84 patients treated with neo-adjuvant chemoradiation for rectal cancer generated from the dosimetric data extracted from the treatment planning system and integrated into our i2b2 CDW.

<p>Dose Volume Histogram for 84 patients treated with neo-adjuvant chemoradiation for rectal cancer generated from the dosimetric data extracted from the treatment planning system and integrated into our i2b2 CDW.</p

Visualization of heterogeneity reduction for cervical lymph nodes area I.

<p>A: all area I labels. B: Label LN_C1_L (red). C: Label LN_C1_R (green). D: Label LN_C1 (blue).</p

Additional file 1: of A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease

List of ATC codes used. Lists of Anatomical Therapeutic Chemical Classification System (ATC) codes used for autoimmune thyroiditis (levothy*) and for diabetes mellitus, Type 1 (insulin). (DOCX 13 kb

Additional file 2: of A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease

Evolution of the numbers of co-occurrences in time. The 15 first ranked autoimmune diseases (in red) which would have been included based on the literature available at various time points. Numbers of co-occurrences until the specified year, ranks in prevalence estimates from this study, ranks in number of MeSH terms co-occurrence with term ‘Celiac Disease’ in MEDLINE at specified years. First version of the clinical vignette related on a new analgesic to control pain in mild trauma injuries with the four experimental factors tested. Description of first clinical vignette and list of response options. (DOCX 17 kb

Additional file 2: of Leveraging the EHR4CR platform to support patient inclusion in academic studies: challenges and lessons learned

Normalized Criteria for the DERENEDIAB, aXa and EWING 2008 studies (DOCX 49 kb

Additional file 1: of Leveraging the EHR4CR platform to support patient inclusion in academic studies: challenges and lessons learned

Free Text Eligibility Criteria for DERENEDIAB, aXa and EWING 2008 studies (DOCX 202 kb

Additional file 1: of Next generation phenotyping using narrative reports in a rare disease clinical data warehouse

Extracted phenotypical concepts per cohort. For each cohort, we list the top50 concepts ranked by Frequency and TF-IDF. The first column is the UMLS code of the phenotypical concepts, the second column is the French preferred terms, the third column is the English preferred terms, the fourth column is the frequencies score (FREQ), the fifth column is the TF-IDF score, the sixth column is the rank of the concept sorted by the frequency score, the seventh column is the rank of the concept sorted by the TF-IDF score and the eighth column is the expert evaluation (1: relevant concept, 0: none relevant concept). (XLS 93 kb