Clinical Memory Layer for Radiation Therapy
Find. Plan. Share.
Sources
CIS / HIS
HL7/FHIR
OIS
RIS / PACS
DICOM CT · MR · PET
TPS
RTPLAN · RTDOSE · RTSTRUCT
QA / Dose
Measurement and Test Data
Cureator Foundation
Clinical Memory Layer
Semantic
Semantic
Data Hub
Harmonize
Index
Link
Calculate
DICOM Web Viewer
DVH
ROI Volume
RTOG CI
Share Link
Everyday Use
Case Research
Find similar cases
Plan Review
Compare dose, DVH, CI
Tumor Board
Share cases via link
Cohorts
Research and Benchmarking
Selected AI
Site-specific context
Sources
KIS/HIS
PACS
OIS
TPS
QA
Cureator Foundation
Clinical Memory Layer
Semantic Data Hub
Harmonize
Index
Link
Calculate
Use
Case Research
Plan Review
Tumor Board
Cohorts
Selected AI
Cureator does not improve the medical decision, but rather the availability, documentation, and economic usability of your own clinical information. Teams find historical cases faster, review RT objects, DVH, and RTOG CI in context, and share relevant information in a controlled manner – regardless of manufacturer or system.
The everyday moment: finding the right case, seeing the relevant dose, and grasping the clinical context at a glance.
Request a 15-minute demo
Performance Record
The leverage lies in specialist time.
In addition to increased everyday efficiency, Cureator Foundation relieves the clinical process in many areas: less searching, less exporting, less manual case compilation, and more usable time for planning, review, and treatment.
We create the greatest relief in tasks that place an economic burden on clinical operations.
Relative model values from internal process analysis. No Euro amounts, no guarantee of results; actual effects depend on the site, data situation, and workflows.
Data layer in daily clinical routine
Existing IT remains. Clinical context becomes easier.
Cureator Foundation connects data from PACS, OIS, TPS, RIS, and HIS to a semantic data layer. RT plans, RT dose, RT structures, image data, and clinical information are harmonized, indexed, and made usable in context.
Public demo
Try Cureator Foundation directly here.
The fastest way to start is our open demo: set search criteria, open cases, check RT objects, and see how easily data becomes browser-accessible in a clinical context.
To test with your own data, request a personal cloud version.
Features
Built for everyday clinical practice.
Semantic Data Hub
Connects imaging, RT objects, and clinical documentation in a queryable knowledge structure.
DICOM RT + HL7/FHIR
Links RTSTRUCT, RTDOSE, RTPLAN, imaging data, and clinical context from existing systems.
RDF/OWL knowledge graph
Models relationships between patient, study, structure, plan, dose, findings, and course.
ROO + SPARQL
Radiation oncology terminology and precise queries enable complex case research, reference case search, and benchmarks.
DVH · ROI · RTOG CI
Makes dose, structure, and planning metrics visible and comparable faster.
DICOM Web Viewer
Displays images, RT objects, and dose information browser-based.
Cohorts and FAIR
Supports research with easily discoverable, accessible, interoperable, and reusable data structures.
Share & Discuss
Provides relevant patient records and case information via link.
Application
The entry wedge is case research.
Helpful for many work steps: finding historical and critical cases, checking RT objects, comparing DVH and CI, sharing links, and building cohorts.
Case Research
Find similar cases by diagnosis, findings, structure, dose, or planning characteristics.
Plan Review
Review historical plans, dose distributions, DVHs, and metrics in context.
Critical cases
For rare or particularly critical cases, see how comparable situations were handled at your own site.
Interoperability
Provide selected patient records and case information in a controlled way via link.
Insights
Prepare research and benchmarking datasets in a more structured way.
From storage to application
How Cureator simplifies existing IT
Patient data from legacy systems becomes usable in a clinical data layer, enabling fewer system changes, faster case research, traceable quality, and structured data for research and selected AI applications.
Data structure
DICOM metadata and clinical information are often kept separate.
RDF triples, ROO, and the knowledge graph connect imaging data, RT objects, and clinical context.
Queryability
Case information must be pieced together across multiple systems.
SPARQL-powered queries enable context-based case research.
AI readiness
Manual data preparation is required.
Structured FAIR data can enable selected AI workflows and foundation model applications.
Interoperability
Systems remain functionally and organizationally separate.
DICOM, HL7/FHIR, and open standards connect data sources without system replacement.
Scaling across multiple centers
Comparability across multiple centers is complex and often manual.
Harmonization and benchmarking make quality comparable across sites.
Find · Plan · Share
Receive information faster. More time for patient treatment.
Specialists are supported in daily clinical routine so that relevant information is available significantly faster and much more conveniently. This leaves more professional time for planning, review, QA, and patient consultations.
Faster Access to Information
Relevant image, plan, dose, and context data are available where teams need them in their daily work.
More Specialist Time
Less searching, exporting, and coordinating. More time for value-adding and billable clinical services.
One place instead of system changes
All information is provided vendor-independently.
Fail-safe
Resilient data management ensures persistent access to critical information in daily clinical practice.
For Whom
A data layer for three user teams.
Medical Physics
Find plans, RT objects, DVH, ROI volumes, and RTOG CI faster within the clinical context. More time for plan quality, QA, and particularly critical cases.
Radiation Therapy
Compare similar cases from your own treatment history and better prepare for planning, review, approval, and follow-up.
Medical Teams
Share relevant image, finding, plan, and dose information in a structured way without losing clinical context.
The Actual Problem
The data is there. The context is missing.
In radiation therapy, CTs, contours, plans, dose distributions, DVHs, diagnoses, findings, and follow-up information are generated daily. However, in everyday practice, this information is often scattered across PACS, OIS, TPS, RIS, and CIS.
The problem is not a lack of software. The problem is quick access to excellent treatment experience.
DICOM ImagingCT · MR · PET
DICOM RTRTSTRUCT · RTDOSE · RTPLAN
HL7/FHIRDiagnoses · Findings · Context
Semantic LayerRDF/OWL · ROO · SPARQL
OutputCases · DVH · RTOG CI · Cohorts · Links
Technical Logic
Data integration becomes clinically usable.
Existing systems are on the left. In the middle is Cureator Foundation as a layer on top: a Semantic Data Hub with a knowledge graph, ROO/RDF structure, DICOM Web Viewer, RTOG CI, SPARQL-supported search, Share Link, and Tumor Board release. The benefits emerge on the right: faster access to historical data, more specialist time, better communication, and more control over your clinic's data.
Sources
CIS / HIS
HL7/FHIR
OIS
RIS / PACS
DICOM CT · MR · PET
TPS
RTPLAN · RTDOSE · RTSTRUCT
QA / Dose
Measurement and Test Data
Cureator Foundation
Clinical Memory Layer
Semantic
Semantic
Data Hub
Harmonize
Index
Link
Calculate
DICOM Web Viewer
DVH
ROI Volume
RTOG CI
Share Link
Everyday Use
Case Research
Find similar cases
Plan Review
Compare dose, DVH, CI
Tumor Board
Share cases via link
Cohorts
Research and Benchmarking
Selected AI
Site-specific context
Technical Core
From DICOM object to knowledge graph.
Imaging and clinical documentation are brought together and semantically linked for the first time. RDF triples, OWL structures, Radiation Oncology Ontology, and SPARQL turn isolated objects into a queryable experience base for case research, benchmarking, research, and the preparation of selected AI applications.
Usage becomes easier for users: find cases faster, check RT objects in context, compare DVH and RTOG CI, use reference cases, build cohorts, and share relevant information in a controlled manner.
Data sources
DICOM CT / MR / PET
DICOM metadata
RTSTRUCT
RTDOSE
RTPLAN
HL7/FHIR
Findings
Semantic Data Hub
Knowledge graph for radiation oncology
RDF triples and OWL structures link imaging data, RT objects, and clinical documentation. The Radiation Oncology Ontology (ROO) organizes technical terms and relationships. SPARQL enables precise queries for complex case examples, reference cases, and benchmarks; FAIR structures support research and reusability.
Clinical use
Reference case search
DVH / RTOG CI
Dose wash
Cohorts
Benchmarking
FAIR data
Share & Discuss
Selected AI
Examples of semantic search
Patients > 65 years · lung carcinoma · defined tumor/ROI context · CT available
similar site · comparable dose distribution · existing RTSTRUCT / RTDOSE / RTPLAN
DVH · ROI volume · RTOG CI · dose wash in case context
Diagnosis · imaging data · RT objects · findings · follow-up data in one query
Active experience base
Not an archive. An active wealth of experience
Cureator is constantly updated with your site's own cases. This creates a dynamically verifiable data treasure from real treatment cases, real planning standards, real treatment details, and your teams' routine, rather than a static archive.
Put local treatment experience to better use in daily practice: for planning, review, particularly critical cases, research, and selected AI applications.
Treatment plans
Contours
Dose distributions
DVH values
ROI volumes
Diagnoses
Findings
Follow-up data
Outcome data
Side effects
Recurrences
Local protocols
Features in daily practice
Semantic search instead of data search.
Cureator untangles DICOM metadata and connects it with clinical context. This enables queries that really matter in everyday work for the first time: similar cases, comparable plans, dose characteristics, tumor volumes, DVH, RTOG CI, and relevant findings.
The starting point remains case research. The technology behind it is RDF/OWL, ROO, SPARQL, and a knowledge graph for radiation oncology.
Query age, diagnosis, tumor volume, RT objects, planning characteristics, and clinical findings in combination.
Find historical cases from your own site faster and use them as a basis for comparison.
Review DVH, ROI volume, RTOG CI, and dose-wash views in the case context.
Provide relevant case information in a controlled way via link for tumor board and coordination.
AI context
Drive innovation. Maintain control.
No anonymous AI. Your experience matters.
Cureator Foundation makes the knowledge and skills of your teams digitally usable – for humans and for selected AI applications. The knowledge graph provides site-specific context; the team selects the cases that fit their own planning reality and clinical standards.
1Cureator Foundation
unlocks local treatment experience as a semantic knowledge graph.
2Team
selects relevant cases, standards, and treatment details.
3Selected AI
works with site-specific context instead of just generic assumptions.
4Treatment team
reviews, interprets, and approves.
Case study
Developed from clinical requirements.
Case studyHistorical cases become clinically usable again.
Historical cases
RTPLAN · RTDOSE · RTSTRUCT · imaging data · findings
Linking
DICOM C-MOVE · FHIR REST · RDF/OWL · ROO · SPARQL
Calculation
RTOG CI · DVH · ROI volume · Dose wash visibility
Clinical use
Planning · review · critical cases · research
Cross-system links enable the identification of highly specialized cohorts.
Scientific and technical evidence
Evidence you can verify.
Cureator Foundation is designed for existing clinical IT environments: DICOM, RT DICOM, HL7/FHIR, knowledge graph, RDF/SPARQL, RTOG CI calculation, and browser-based use without replacing systems. In addition, published work shows the scientific origins behind semantic linking of imaging data and metadata.
PDF
DICOM Conformance Statement 6.5.x
Storage, Query/Retrieve, C-STORE, C-FIND, and C-MOVE as technical proof of integration.
Paper
Research Foundation: Semantic DICOM Data Modeling
Semantic Web technology for the representation of image data and calculated results. Stud Health Technol Inform, 2014.
Paper
FAIR-compliant clinical, radiomics and DICOM metadata
FAIR, RDF, Radiomics, DICOM Metadata and Semantic DICOM. Medical Physics, 2020.
Vendor Independence
Built for heterogeneous system landscapes.
Radiation therapy rarely operates in a single-vendor world. Cureator Foundation is developed for data from established CIS/HIS, RIS, PACS, OIS, TPS, QA, and AI systems – without replacing these systems.
CIS / HIS / EHR
Dedalus
CIS / HIS
CGM
EHR
Epic
EHR
Oracle Health
RIS
Dedalus Radiology IT
RIS / PACS
GE HealthCare
RIS / PACS
Philips
OIS / TPS
Varian
OIS / TPS
Elekta
OIS / TPS
RaySearch
PACS
Sectra
PACS
AGFA HealthCare
PACS
Fujifilm Synapse
Delivery / TPS
Accuray
Radiosurgery
Brainlab
AI Software
MVision AI
QA
Sun Nuclear
QA / Dosimetry
IBA Dosimetry
Proton Therapy
IBA
SGRT
C-RAD
Positioning
LAP
Dosimetry
PTW
CIS / HIS / EHR
Dedalus
CIS / HIS
CGM
EHR
Epic
EHR
Oracle Health
RIS
Dedalus Radiology IT
RIS / PACS
GE HealthCare
RIS / PACS
Philips
OIS / TPS
Varian
OIS / TPS
Elekta
OIS / TPS
RaySearch
PACS
Sectra
PACS
AGFA HealthCare
PACS
Fujifilm Synapse
Delivery / TPS
Accuray
Radiosurgery
Brainlab
AI Software
MVision AI
QA
Sun Nuclear
QA / Dosimetry
IBA Dosimetry
Proton Therapy
IBA
SGRT
C-RAD
Positioning
LAP
Dosimetry
PTW
Logos and product names are trademarks of their respective owners. The depiction shows relevant clinical system environments and interoperability contexts. Partnership or endorsement is only claimed where it explicitly exists.
Request a 15-minute demo
See what your own data already knows.
In 30 minutes, we will show you how Cureator Foundation brings together cases, plans, contours, dose data, and clinical context. Your existing systems remain in place – your knowledge becomes usable faster.