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The medical imaging extension proposal represents imaging
+characteristics through two new tables—Image_occurrence and
+Image_features.
Table Description
The Image_occurrence table describes imaging events and provides data
+lineage to the imaging study stored in DICOM format.
User Guide
Each row in Image_occurrence represents a collection of images +acquired on an imaging modality using a contiguous imaging technique. +This is referred to as a DICOM series. Each DICOM series can be an +independent modality and acquisition technique grouped within a DICOM +study (e.g., PET/CT scan).
+cdmTableName | +cdmFieldName | +isRequired | +cdmDatatype | +userGuidance | +
---|---|---|---|---|
image_occurrence | +image_occurrence_id | +Yes | +integer | +The unique key is given to an imaging study record +(often referred to as the accession number or imaging order number) | +
image_occurrence | +person_id | +Yes | +integer | +The person_id of the Person for whom the procedure is +recorded. This can be a system-generated code or adopted from original +source. | +
image_occurrence | +procedure_occurrence_id | +Yes | +integer | +The unique key is given to a procedure record for a +person. Link to the Procedure_occurrence table. | +
image_occurrence | +visit_occurrence_id | +No | +integer | +The unique key is given to the visit record for a +person. Link to the Visit_occurrence table. | +
image_occurrence | +anatomic_site_concept_id | +No | +integer | +Anatomical location of the imaging procedure by the +medical acquisition device (gross anatomy). It maps the +ANATOMIC_SITE_SOURCE_VALUE to a Standard Concept in the Spec Anatomic +Site domain. This should be coded at the lowest level of +granularity. | +
image_occurrence | +wadors_uri | +No | +varchar (max) | +A Web Access to DICOM Objects via Restful Web Services +Uniform Resource Identifier on study level. | +
image_occurrence | +local_path | +Yes | +varchar (max) | +“Universal Naming Convention (UNC) path to the folder +containing the image object file access via a storage block access +protocol. (e.g., \Server)” | +
image_occurrence | +image_occurrence_date | +Yes | +date | +The date the imaging procedure occurred. | +
image_occurrence | +image_study_UID | +Yes | +varchar (250) | +DICOM Study UID | +
image_occurrence | +image_series_UID | +Yes | +varchar (250) | +DICOM Series UID | +
image_occurrence | +modality | +Yes | +varchar (250) | +“DICOM-defined value (e.g., US, CT, MR, PT, DR, CR, +NM)” | +
Table Description
Imaging features are comprised of segmentation algorithm results +executed on the images, image acquisition parameters, and structured +radiology reports. The Image_feature table describes the characteristics +of the images and their provenance.
+User Guide
+Each row will contain a uniquely identified feature with links to the +source imaging as well as the clinical domain table the feature is +located in. The image_feature_type_concept_id will describe the method +used to create that feature (e.g., machine learning algorithm, DICOM +structured report). Image acquisition parameters will also be stored in +the clinical domain tables and linked to the source images through the +image feature table. This allows the cardinality to include multiple +acquisition parameters as well as the benefit of being able to query +them with existing tooling in the OHDSI platform.
+cdmTableName | +cdmFieldName | +isRequired | +cdmDatatype | +userGuidance | +
---|---|---|---|---|
image_feature | +image_feature_id | +Yes | +integer | +The unique key is given to an imaging feature. | +
image_feature | +person_id | +Yes | +integer | +The person_id of the Person table for whom the the +procedure is recorded. This can be a system-generated code or adopted +from original source. | +
image_feature | +image_occurrence_id | +Yes | +integer | +The unique key of the Image_occurrence table. | +
image_feature | +table_concept_id | +Yes | +integer | +“The concept_id of the domain table that feature is +stored in Measurement, Observation, etc. This concept should be used +with the table_row_id.” | +
image_feature | +table_row_id | +Yes | +integer | +The row_id of the domain table that feature is +stored. | +
image_feature | +image_feature_concept_id | +Yes | +integer | +Concept_id of standard vocabulary—often a LOINC or +RadLex of image features | +
image_feature | +image_feature_type_concept_id | +Yes | +integer | +“This field can be used to determine the provenance of +the imaging features (e.g., DICOM SR, algorithms used on images)” | +
image_feature | +image_finding_concept_id | +No | +integer | +“RadLex or other terms of the groupings of image +feature (e.g., nodule)” | +
image_feature | +image_finding_id | +No | +integer | +Integer for linking related image features. It should +not be interpreted as an order of clinical relevance. | +
image_feature | +anatomic_site_concept_id | +No | +integer | +This is the site on the body where the feature was +found. It maps the ANATOMIC_SITE_SOURCE_VALUE to a Standard Concept in +the Spec Anatomic Site domain. | +
image_feature | +alg_system | +No | +varchar(max) | +“URI of the algorithm that extracted features, +including version information” | +
image_feature | +alg_datetime | +No | +datetime | +The date and time of the algorithm processing. | +
test
+The Medical Imaging Working Group (MI WG) for the OHDSI community was +formed in 2021, comprised of imaging research scientists and +observational health researchers familiar with OMOP CDM. The working +group evaluated standard vocabularies, defined fields containing key +imaging events, and identified limitations of the model. The working +group started with the R-CDM in the development of the medical imaging +extension. Imaging researchers across the field were consulted to gather +requirements and gain insights into the structure and usability of the +proposed model. The principal clinical use case focused on longitudinal +tracking of multiple lung nodules. Important attributes included CT +acquisition parameters, nodule diameter, location, density, shape, and +other phenotypes. A prototype using CT lung nodules was developed and +demonstrated at the 2023 Society of Imaging Informatics in Medicine +(SIIM) conference Hackathon.
+
Citation: Park C, You SC, Jeon H, Jeong CW, Choi JW, Park RW. +Development and Validation of the Radiology Common Data Model (R-CDM) +for the International Standardization of Medical Imaging Data. Yonsei +Med J. 2022;63(Suppl):S74. doi:10.3349/ymj.2022.63.S74
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