Exchanging radiation oncology data for national registries

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1 Exchanging radiation oncology data for national registries Tim Lustberg PhD Student Department of Radiation Oncology (MAASTRO) GROW - Maastricht University Medical Centre + Maastricht, The Netherlands

2 Index Dutch Lung Radiotherapy Audit automatic data exchange pilot FAIR data & APIs FHIR & DLCA-R registration

3 Dutch Lung Radiotherapy Audit automatic data exchange pilot

4 NVRO assignment (2013) Requested data are stored locally in: PACS TPS (Eclipse, Pinnacle, Raystation, XiO etc.) R&V (Mosaiq, Aria) EMRs (Chipsoft, Epic, SAP, etc.) Can we transfer (radiation oncology) data to DICA instead of manual input?

5 Initial design

6 First software release (2015) Every care provider is different, no uniform solution can be reached DICOM solution not mature enough After 2 years still no usable HL7 message (CSV batch exchange) Not all data can be linked by default E.g. prescribed and delivered treatment

7 FAIR data & APIs

8 Building a single source of truth Curated Data Clinical Systems Registries MIA Dashboards DICOM

9 FAIR principles Findable: easy to find for both humans and computers, with metadata that facilitate searching for specific datasets, Accessible: stored for long term so that they can easily be accessed and/or downloaded with well-defined license and access conditions (open access when possible), whether at the level of metadata, or at the level of the actual data, Interoperable: ready to be combined with other datasets by humans or computers, Reusable: ready to be used for future research and to be further processed using computational methods. Source:

10 FAIR data Not only data: Description of data (metadata) Process of data analysis/conversion Prediction models learned on data Results of analysis Computer should also understand these things, to be able to innovate

11 Semantics

12 Need standardized terminologies ICD ICD10-CM SNOMED SNOMED-CT NCI Thesaurus LOINC RadLex

13 Terminologies -> ontologies Patient (NCI Thesaurus C16960) Disease (NCI Thesaurus C2991) SNOMED: LOINC: LP ICD: All codes! We need relationships between codes Patient has one or multiple disease(s) Need to code relationships (predicates)

14 Semantic Web technologies Resource description framework (RDF) Describe all terms as URLs (roo:100020) Understandable for a computer Graph database Subject (node) Predicate (arc) Object (node) mst:patient_111 rdf:type ncit:c16960 mst:patient_123 rdf:type ncit:c16960 mst:patient_123 roo: ncit:c16576 mst:patient_123 roo: mst:disease_123 mst:disease_123 rdf:type ncit:c2926

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16 Semantic Web technologies Resource description framework (RDF) Describe all terms as URLs (roo:100020) Understandable for a computer Graph database Subject (node) Predicate (arc) Object (node) mst:patient_111 rdf:type ncit:c16960 mst:patient_123 rdf:type ncit:c16960 mst:patient_123 roo: ncit:c16576 mst:patient_123 roo: mst:disease_123 mst:disease_123 rdf:type ncit:c2926

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18 Semantic Web technologies Resource description framework (RDF) Describe all terms as URLs (roo:100020) Understandable for a computer Graph database Subject (node) Predicate (arc) Object (node) mst:patient_111 rdf:type ncit:c16960 mst:patient_123 rdf:type ncit:c16960 mst:patient_123 roo: ncit:c16576 mst:patient_123 roo: mst:disease_123 mst:disease_123 rdf:type ncit:c2926

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20 Semantic web merge on-the-fly Janssen Patient Person Database 1 Janssen Gender Gender Male Janssen Tumor Volume 53.6 Unit: CC Database 2

21 View of the world Non-small cell lung carcinoma (ncit:c2926) has_neoplasm roo: Patient (ncit:c16960) has_biological_sex roo: Sex (nci:c20197 or nci:c16576) treats (roo:100402) has_radiation_therapy roo: has_age_at_start_rt roo: Radition Therapy (ncit:c15313) OR SBRT (ncit:c118286) has_fractionation_part (roo:100269) First radiotherapy fraction (roo:100058) Age at start RT (roo:100003) roo:is_when (roo:100038)

22 Value Rib (fma:fma7574) has_body_part roo: has_body_part (roo:100221) Pneumonitis (ctcae:pneumonitis) Fracture (ctcae:fracture) Reaction (ctcae:radiation_re call_reaction_derm atologic) Fatigue (ctcae:fatigue) Percent (uo:uo_ ) Value Liter (uo:uo_ ) Value Liter (uo:uo_ ) has_value roo: has_unit roo: has_value roo: has_unit roo: has_value roo: has_unit roo: Percentage DLCO (roo:100368) DLCO (nci:c38083) FEV1 (nci:c38084) Value Value Count (uo:uo_ ) has_measurement roo: has_volume roo: has_volume roo: has_value roo: has_unit roo: Hospital (ncit:c19326) (uri= PrincessMargaret) rdfs:label Positive Lymph Node Stations (roo:100049) Histology (nci:2926 nci:2852 nci:3780 nci:2929 nci:2852 nci:3915) treated_in Roo: MIA Version (mia:<version>) Patient (dcterms:hasversion) has_finding roo: has_histology (roo:100021) has_neoplasm roo: RT Structure Set (sedi:rtstructureset) measurement_of (roo:100213) Volume of primary tumor (roo:100054) has_volume (roo:100315) Non-small cell lung carcinoma (ncit:c2926) has_value roo: has_unit roo: has_performance_status roo: Value Cubic centimeter (uo:uo_ ) is_when (roo:100038) is_when (roo:100038) has_clinical_stage Roo: ECOG performance status (nci: nci: nci: nci: nci: nci:105728) Clinical TNM Finding (ncit:c48881) has_clinical_t_stage roo: Generic T-stage 0-4 (ncit:48719) (ncit:48720) (ncit:48732) is_staged_with Roo: has_ajcc_version (roo:100219) Diagnostic Procedure (ncit:c18020) AJCC Edition (roo:100052) (roo:100053) Dyspnea (ctcae:dyspnea) Couch (ctcae:couch) Anorexia (ctcae:anorexia) Dysphagia (ctcae:dysphagia) Value Percent (uo:uo_ ) has_value roo: has_unit roo: Percentage FEV1 (nci:c112376) has_patient_finding (roo:100291) has_fev1_percentage roo: (ncit:c16960) has_radiation_therapy roo: treats has_biological_sex roo: has_age_at_start_rt roo: (roo:100402) has_age_at_diagnosis roo: Sex (nci:c20197 and nci:c16576) has_survival roo: has_vital_status roo: Age at diagnosis (roo:100002) has_value roo: has_unit roo: Value Year (uo:uo_ ) Survival (roo:100063) has_unit roo: has_value roo: Month (uo:uo_ ) Hemoptysis (nci:c3094) Esophagitis (ctcae:esophagitis) Pulmonary Fibrosis (ctcae:pulmonary_fi brosis) Brachial plexopathy (ctcae:brachial_plex opathy) Solid Tumor Metastases (ncit:c3261) Severe liver disease (icd:k74.6) Lymphoma (ncit:c3208) Myocardial Infarct (ncit:c27996) Congenital Heart Failure (icd:i50.0) AIDS (ncit:c2851) Leukemia (ncit:c3161) Cancer (ncit:c3262) has_disease (roo:100020) Gray (uo:uo_ ) has_unit roo: Value (xsd:double) has_value roo: Radition Therapy (ncit:c15313) OR SBRT (ncit:c118286) roo: has_prescribed_radiation_dose has_fractionation_part (roo:100269) has_rt_dose roo: has_fractionation_part (roo:100269) Delivered Radiotherapy Dose (roo:100012) First radiotherapy fraction (roo:100058) Last radiotherapy fraction (roo:100059) Age at start RT (roo:100003) roo:is_when (roo:100038) after (time:after) has_unit roo: has_value roo: Year (uo:uo_ ) Value has_survival roo: Vital Status (ncit:c37987 or ncit:28554) Value Peripheral Vascular Disease (icd:i73.9) DM End Organ Disease (ncit:c113717) Prescribed Radiotherapy Dose (roo:100013) has_value roo: Value (xsd:double) has_unit roo: Gray (uo:uo_ ) Cerebrovascular Accident (ncit:c2938) Dementia (ncit:c4786) Chronic Lung Disorder (ncit:c98541) Renal disease (icdcm:n18.3) Hemiplegia (ncit:c64329) Diabetes (ncit:c2985) has_fraction_count (roo:100224) No. RT Fractions Per Treatment (roo:100356) has_count (roo:100223) has_count (roo:100223) No. RT Fractions Per Day (roo:100355) has_count (roo:100223) Value (xsd:integer) Connective Tissue Disease (ncit:c27204) Ulcer (ncit:c3426) Mild Liver Disease (ncit:c27204) Value (xsd:integer)

23 Creating structured data from DICOM images: Medical Image Analysis

24 FHIR & DLCA-R registration

25 FHIR for Radiation Oncology Standard domain-specific implementation is missing! Radiotherapy domain model by discussing process- and data flow: FHIR scenario capturing various RT-procedures Detailed resources for future use Workable model implemented to support registries now In hindsight: scenario in alignment with solutions considered in FHIR community

26 MRDM-model for Radiation Therapy

27 FHIR processing at MRDM transaction-based submission, batch-processing

28 RDF & FHIR Curated Data Clinical Systems Registries Dashboards DICOM Create linked data FHIR resources!

29 We are not there yet! FHIR messages will always have some receiver specific implementation the capture challenges for the data exchange are bigger for other disciplines other approaches are point to point There is no magic bullet, data capture / exchange is hard work!

30 Thank you for your attention!

31 Acknowledgements NVRO, Netherlands DICA, Leiden, Netherlands MRDM, Deventer, Netherlands Radboudumc, Nijmegen, Netherlands MAASTRO, Maastricht, Netherlands De Praktijk Index, Bilthoven, Netherlands Special thanks to MRDM & MAASTRO development teams! More info on: dcapackage-template

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