10. Annex: SPARQL Queries
Based on the information acquired from the prostate cancer use cases (Section 7), SPARQL queries are applied to request the hyper-ontology regarding diagnosis details. In the following, we give some examples of SPARQL queries to question the cancer patients (COM1001051) who:
had a PSA (CLIN1000227) lab test and to return the PSA levels (Query1);
underwent a prostatectomy (CLIN1000248) and to return the associated pathological interpretation results (Query2);
were subject to imaging procedures and to return the associated imaging interpretation results (Query3).
PREFIX ho: <https://cancerimage.eu/ontology/EUCAIM#>
Query1: SELECT ?p ?r WHERE {
?p rdf:type ho:COM1001051 .
?p ho:Is_Subject_For ?a .
?a rdf:type ho:CLIN1000227 .
?a ho:Has_Value ?r . }
For Query1, both patients of the ProCAncer-i (uc1) and INCISIVE (uc2) use cases have done the PSA lab test. Thus, by executing Query1, we obtain the following results:
ProCAncer-I_patient : PSA level = 7.16
INCISIVE_patient : PSA level = 0.04
INCISIVE_patient : PSA level = 0.07
INCISIVE_patient : PSA level = 5.6
Query2: SELECT ?p ?a ?r WHERE {
?p rdf:type ho:COM1001051 .
?p ho:HasUndergone ?a .
?a rdf:type ho:CLIN1000248 .
?a ho:Has_pathologic_interpretation_result ?r . }
For Query2, only the ProCancer-i patient (uc1) underwent a prostatectomy with different pathologic interpretation results. Thus, the response of this query is obtained as follows:
ProCAncer-I_patient : prostatectomy -> Result: intraductal_carcinoma
ProCAncer-I_patient : prostatectomy -> Result: pN0
ProCAncer-I_patient : prostatectomy -> Result: pT3b
ProCAncer-I_patient : prostatectomy -> Result: 4+3_Gleason_score
Query3: SELECT ?p ?a ?r WHERE {
?p rdf:type ho:COM1001051 .
?p ho:Is_Subject_For ?a .
?a ho:Has_imaging_interpretation_result ?r . }
For Query3, the ProCancer-i patient (uc1) was subject to multiparametric MRI and fusion biopsy with the following interpretation results: PI-RADS 5, cT2b, cN0, and pT2. Meanwhile, the INCISIVE patient (uc2) was subject to MRI scan and biopsy with the following results: PI-RADS score 4, Gleason score 6, and ISUP grade 5. By executing Query3, we obtain the following results:
INCISIVE_patient : MRI_scan -> Result: PIRADS_score_of_4
INCISIVE_patient : biopsy -> Result: Gleason_score_of_6
INCISIVE_patient : biopsy -> Result: ISUP_grade_5
ProCAncer-I_patient : fusion_biopsy -> Result: pT2
ProCAncer-I_patient : multiparametric_MRI -> Result: PI-RADS_5
ProCAncer-I_patient : multiparametric_MRI -> Result: cN0
ProCAncer-I_patient : multiparametric_MRI -> Result: cT2b
EUCAIM D5.1. Early release of the Data Federation Framework, 2023 https://cancerimage.eu/wp-content/uploads/2023/10/D5.1_Early-release-of-the-Data-Federation-Framework_vf.pdf ↑
EUCAIM D5.1. Early release of the Data Federation Framework, 2023 https://cancerimage.eu/wp-content/uploads/2023/10/D5.1_Early-release-of-the-Data-Federation-Framework_vf.pdf ↑
https://healthdcat-ap.github.io/ ↑
https://www.fairdatapoint.org/ ↑
Adopt OMOP conceptual model (terminologies), ↑
https://catalogue.eucaim.cancerimage.eu/ ↑
https://github.com/OHDSI/Athena ↑
https://data.bioportal.lirmm.fr/documentation ↑
https://documentation.uts.nlm.nih.gov/rest/home.html ↑
https://build.fhir.org/ig/HL7/fhir-mCODE-ig/ ↑
https://nemo.inf.ufes.br/en/projetos/ufo/ ↑
https://ontouml.org/ ↑
https://www.w3.org/OWL/ ↑
https://protege.stanford.edu/ ↑
https://github.com/stardog-union/pellet ↑
https://tehdas.eu/app/uploads/2023/09/tehdas-recommendations-on-a-data-quality-framework.pdf ↑
https://build.fhir.org/ig/HL7/fhir-mCODE-ig/ ↑
Guérin, J., Laizet, Y., Le Texier, V., Chanas, L., Rance, B., Koeppel, F., Lion, F., Gourgou, S., Martin, A. L., Tejeda, M., Toulmonde, M., Cox, S., Hess, E., Rousseau-Tsangaris, M., Jouhet, V., & Saintigny, P. (2021). OSIRIS: A Minimum Data Set for Data Sharing and Interoperability in Oncology. JCO clinical cancer informatics, 5, 256–265. https://doi.org/10.1200/CCI.20.00094 ↑
https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems ↑
Varvara Kalokyri et al., MI-Common Data Model: Extending Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) for Registering Medical Imaging Metadata and Subsequent Curation Processes. JCO Clin Cancer Inform 7, e2300101(2023). DOI:10.1200/CCI.23.00101 ↑
https://build.fhir.org/ig/HL7/fhir-mCODE-ig/StructureDefinition-mcode-primary-cancer-condition.html ↑
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