Skip to Content
COVID-19 Resources
Cancer Imaging Program (CIP)
Contact CIP
Show menu
Search this site
Last Updated: 11/21/19
Quantitative Imaging Network (QIN)

Gordon Harris

Massachusetts General Hospital

Precision Imaging Metrics

Precision Imaging Metrics (PIM) is a cloud-hosted clinical trial imaging assessment and workflow management system developed by the Dana-Farber/Harvard Cancer Center and currently in use through an academic licensing model as an NCI shared resource at eight NCI-designated Cancer Centers around the country with several other sites considering implementing the solution (www.precisionmetrics.org). The PIM platform improves protocol adherence by eliminating common response criteria discrepancies and accelerates tumor measurements turnaround time to support same-day reporting. The system has built-in flexibility to allow for site customization to better meet the specific imaging review needs of each cancer center and eliminates paper measurement forms and other outdated, error-prone tools and practices. The local clinical treatment team can request scan analyses, specify the time when results are needed, and view imaging results through a secure, password-protected website. Built-in response criteria conformance checks ensure that the imaging reviewers assess the scans according to the trial-specific protocol. Upon saving image measurements, quantitative metrics and annotated images are automatically uploaded to the website. After electronic sign-off by the reviewing radiologist, the imaging time point is locked and the clinical team is automatically alerted that the assessment is ready for viewing. Results are provided on-line and on-time, before the patient is seen in the clinic for treatment decisions. The clinical team can access measurement tables, graphs, and annotated images in a single structured report and can print a copy of the report to serve as the source document for trial audits. Response assessments can also be stored back to the hospital PACS in DICOM format.

Precision Imaging Metrics includes an integrated web viewer that is customized from the open-source OHIF Viewer framework. PIM could support the goals of QIN by providing an avenue for QIN developed tools to be implemented into a cloud-hosted clinical trials informatics framework that is deployed for use in thousands of active clinical trials at NCI-designated Cancer Centers around the country. This could enable efficient deployment of QIN quantitative imaging tools for use in multi-site clinical trials without requiring each QIN development team to create a separate clinical trials informatics and workflow management solution specific to each new QIN tool.

Open Health Imaging Foundation Viewer and Framework

The Open Health Imaging Foundation (OHIF) provides a vendor-neutral, open-source (MIT license), extensible, zero-footprint web imaging viewer and framework for analysis and display of medical images (www.ohif.org). The system is built upon libraries such as dicomParser and Cornerstone (https://cornerstonejs.org/), and can easily be integrated or embedded into third-party web applications. It provides extension points for adding custom functionality, supports DICOMWeb (QIDO-RS, WADO-URI, WADO-RS) connectivity to imaging archives, and can be configured to connect to identity providers that support OpenID Connect or OAuth. Support for custom formats for images and/or metadata can also be added. The application operates as a single-page web application and does not require any server-side component. All DICOM transfer syntaxes are supported through client-side decompression.

There are hundreds of projects around the world using the OHIF/Cornerstone framework, both in academia and industry. Developers can easily prototype their own third party tools and incorporate them into the OHIF Viewer by writing custom Cornerstone Tools and including them in OHIF using the extension mechanism. This provides a simple route for researchers to bring their quantitative imaging tools into use inside a user-friendly web imaging application. The OHIF framework could support QIN goals by providing a common web imaging environment within which QIN tool developers could establish interoperability and incorporate tools through the plug-in extension architecture. In addition, the OHIF platform can allow QIN tool developers to focus on the creation of novel quantitative imaging solutions without requiring each team to develop and support a separate and proprietary web imaging solution.