PDCP: A Set of Tools for Extracting, Transforming, and Loading Radiotherapy Data from the Orthanc Research PACS

Abstract

The Orthanc server is a light-weight open-source picture imaging and archiving system (PACS) used to store digital imaging and communications in medicine (DICOM) data. It is widely used in research environments as it is free, open-source and scalable. To enable the use of Orthanc stored radiotherapy (RT) data in data mining and machine learning tasks, the records need to be extracted, validated, linked, and presented in a usable format. This paper reports patient data collection and processing (PDCP), a set of tools created using python for extracting, transforming, and loading RT data from Orthanc PACs. PDCP enables querying, retrieving, and validating patient imaging summaries; analysing associations between patient DICOM data; retrieving patient imaging data into a local directory; preparing the records for use in various research questions; tracking the patient’s data collection process and identifying reasons behind excluding patient’s data. PDCP targeted simplifying the data preparation process in such applications, and it was made expandable to facilitate additional data preparation tasks.