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

Clinical 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.

Radiogenomics in prostate cancer evaluation.

PURPOSE OF REVIEW: Radiogenomics, fusion between radiomics and genomics, represents a new field of research to improve cancer comprehension and evaluation. In this review, we give an overview of radiogenomics and its most recent and relevant applications in prostate cancer management. RECENT FINDINGS: Literature about radiogenomics in prostate cancer emerged last 5 years but remains scarce. Radiogenomics in prostate cancer mainly rely on MRI-based features. Several imaging biomarkers, mostly based on the identification of radiomic features from deep learning studies, have been studied for the prediction of genomic profiles, such as PTEN Decipher Oncotype DX or Prolaris expression. However, despite promising results, several limitations still preclude any integration of radiogenomics in daily practice. SUMMARY: In the future, the emergence of artificial intelligence in urology, with an increasing use of radiomics and genomics data, may enable radiogenomics to assume a growing role in the evaluation of prostate cancer, with a noninvasive and personal approach in the field of personalized medicine. Further efforts are necessary for integration of this promising approach in prostate cancer decision-making.