Ontology-based classification of radiological procedures for consistent sharing in Clinical Data Warehouses

International audienceClinical data warehouses (CDW) allow the reuse of care data in a research context. Designing and operating CDWs require addressing interoperability, data enrichment and data modeling problems, among others. This work concerns the management of medical imaging data in CDWs. It proposes a data-driven approach for classifying radiological procedures using an ontology-based approach. This approach relies on the RadLex ontology and an imaging procedures terminology called RadLex Playbook, both developed by RSNA. We first created an ontology of the radiological procedures by merging the Playbook with the relevant extract of the RadLex ontology and enriched it with French terms using the UMLS meta thesaurus. Then, we developed a proof of concept of a radiological procedures data classifier that exploits the richness of RadLex ontology and the ontological reasoning and we assessed it using medical imaging data retrieved from two different facilities. Our results demonstrate feasibility and relevance of the approach. They also highlight differences in the methods of filling imaging procedure data in the two institutions, as well as some problems in the RadLex ontology. Based on this experience, this proof of concept will be refined to evolve towards a routinely usable classification tool supporting medical imaging data management in CDWs

How to Optimize Connection Between PACS and Clinical Data Warehouse: A Web Service Approach Based on Full Metadata Integration

International audienceClinical image data analysis is an active area of research. Integrating such data in a Clinical Data Warehouse (CDW) implies to unlock the PACS and RIS and to address interoperability and semantics issues. Based on specific functional and technical requirements, our goal was to propose a web service (I4DW) that allows users to query and access pixel data from a CDW by fully integrating and indexing imaging metadata. Here, we present the technical implementation of this workflow as well as the evaluation we carried out using a prostate cancer cohort use case. The query mechanism relies on a Dicom metadata hierarchy dynamically generated during the ETL Process. We evaluated the Dicom data transfer performance of I4DW, and found mean retrieval times of 5.94 seconds and 0.9 seconds to retrieve a complete DICOM series from the PACS and all metadata of a series. We could retrieve all patients and imaging tests of the prostate cancer cohort with a precision of 0.95 and a recall of 1. By leveraging the CMOVE method, our approach based on the Dicom protocol is scalable and domain-neutral. Future improvement will focus on performance optimization and de identification

Analyse des pratiques d’observation et d’évaluation en classe : vers une prise en compte de l’activité réelle, de la signification et du contexte

International audienc