An Open Data and Methods Framework for Comparative Evaluation of DICOM De-Identification Tools

Removal of identifying information from data used in clinical studies protects patient privacy and maintains confidentiality. It ensures compliance with laws like data protection regulations, which safeguard personal data. Medical imaging data may contain various sensitive personal data. An image is composed of pixel data and structured metadata. The de-facto standard for medical imaging, the Digital Imaging and Communication in Medicine (DICOM) format, defines more than 5,000 unique DICOM metadata items, including so-called private tags that can contain arbitrary information. Therefore, de-identification of DICOM objects may become a notable challenge, even without consideration of identifying information in pixel data such as burnt-in identifiers or visible series numbers of implants. To decide which tool best to use for defined requirements, this paper aims to define a workflow for comparative evaluations of DICOM de-identification tools.

We assessed requirements and performance indicators for DICOM de-identification tools. We employ open data and open-source tools to assess the different requirements in a comparable way, and apply the workflow on the widely used java-based de-identification tool Clinical Trials Processor (CTP) and the current state of a recently developed python-based anonymisation tool, both provided by the Radiological Society of North America (RSNA).

The proposed workflow proved helpful in systematically comparing de-identification tools and highlighting key differences in functionality and use case. The comparison of CTP and RSNA Anonymizer (RDA) de-identification tools reveals that both tools effectively remove identifying information from datasets, but with distinct differences in their approaches and compliance with the DICOM standard.

The evaluation of two DICOM de-identification tools using open data and methods revealed decisive differences in functionality and practical suitability, highlighting the value of a structured comparison. De-identification tools need to be selected based on specific use cases, especially given ongoing challenges with metadata and image-based identifiers.