Quantitative Imaging Informatics for Cancer Research

Author:

Fedorov Andrey1,Beichel Reinhard2,Kalpathy-Cramer Jayashree3,Clunie David4,Onken Michael5,Riesmeier Jörg6,Herz Christian1,Bauer Christian2,Beers Andrew3,Fillion-Robin Jean-Christophe7,Lasso Andras8,Pinter Csaba8,Pieper Steve9,Nolden Marco10,Maier-Hein Klaus10,Herrmann Markus D.11,Saltz Joel12,Prior Fred13,Fennessy Fiona1,Buatti John2,Kikinis Ron1

Affiliation:

1. Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA

2. University of Iowa, Iowa City, IA

3. Massachusetts General Hospital, Boston, MA

4. PixelMed Publishing, Bangor, PA

5. OpenConnections, Oldenburg, Germany

6. Freelancer, Oldenburg, Germany

7. Kitware, Clifton Park, NY

8. Queen’s University, Kingston, ON, Canada

9. Isomics, Cambridge, MA

10. German Cancer Research Center, Heidelberg, Germany

11. Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA

12. Stony Brook University, Stony Brook, NY

13. University of Arkansas for Medical Sciences, Little Rock, AR

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.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

General Medicine

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