Quantitative Imaging Informatics for Cancer Research

Abstract

Purpose: We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program.

Methods: QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach.

Results: Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation.

Conclusion: Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.

FIG 1.

Download statistics for the 3D Slicer extensions developed with the contribution of the Quantitative Imaging Informatics for Cancer Research (QIICR) project. Note that extensions were released at different times, with some extensions being available longer than others. Some of the extensions are dependent on others (eg, PETDICOMExtension can be installed individually, but is also downloaded every time PET-IndiC or QuantitativeReporting is installed). Statistics reported were collected on December 15, 2019, using the script available publicly at https://github.com/Slicer/SlicerDeveloperToolsForExtensions. These reported download counts do not include downloads of the binary packages directly from GitHub or downloads of Docker images from DockerHub (which were used for disseminating dcmqi packages), or downloads of the source code (due to technical limitations of being able to track such downloads).

FIG 2.

Example demonstration of image analysis results interoperability enabled by DICOM. From bottom left corner clockwise, examples of platforms visualizing the same Digital Imaging and Communications in Medicine (DICOM) positron emission tomography (PET) segmentation dataset from the public QIN-HEADNECK collection^,: 3D Slicer (free, open-source desktop application), OHIF Viewer (free, open-source Web viewer), Brainlab SmartBrush (commercial Food and Drug Administration–approved tumor outlining application). Results were collected as part of the DICOM4QI (DICOM for Quantitative Imaging) demonstration and connectathon organized by Quantitative Imaging Informatics for Cancer Research (QIICR) at the annual Radiologic Society of North America meeting since 2015.^, The histogram shows the significant increase in the The Cancer Imaging Archive–reported usage of the QIN-HEADNECK collection after the publication of the preprint in Nov 2015 (red arrow) and peer-reviewed paper in May 2016 (green arrow), which introduced imaging-related DICOM data (segmentations, measurements, clinical data) to accompany the imaging dataset.