The Quantitative Imaging Project (QIN) [1] is an effort supported by the National Cancer Institute with the goals of i) develop best practices and tools to quantitate medical imaging, ii) develop techniques to assess response to cancer therapies, and iii) use current image-derived and clinical data to perform therapy outcome prediction. Currently there are 13 institutions within the QIN network. The University of Iowa’s role in this project is to capture clinical and imaging data from consented head and neck cancer subjects, develop novel, 3-D image analysis and segmentation annotation, perform clinical trials, develop outcome assessment techniques, and share the resulting data and tools developed to other QIN network institutions and the research-wide community. An interesting aspect for all members of the QIN network is how to share images, image annotation, and data derived from image analyses. One of the tools currently being used by the Iowa group (and other QIN network members) is an open source, 3D imaging tool called 3D slicer [2]. The open source aspect of 3D slicer allows for the development of novel image analysis that can easily be deployed as a 3D slicer plugin. The QIN network has already developed a plugin to convert annotation in 3D slicer into another compatible image annotation format called Annotated Image Markup (AIM) [3].

Other grass-roots efforts to community-based tool development and data sharing have been attempted, include the cancer Biomedical Informatics Grid (caBIG) [4]. The caBIG effort has definitely not been a failure, as products derived from that effort such as caTissue continue to be deployed and used by institutions. However, some caBIG participants believe that caBIG has not been as successful as cancer bioinformatic developers would hope. Compared to caBIG, the QIN effort has the advantage that the problem focus has been narrowed to quantitating imaging, response assessment and outcome prediction. It will be interesting to watch this project evolve and hopefully provide a model for other community-based, science development efforts that can rival success of the human genome project. After all, cancer really sucks.

References:

1. Quantitative Imaging Network
2. 3D Slicer
3. Annotated Image Markup (AIM)
4. Cancer Biomedical Informatics Grid (caBIG)

Recommended readings from the Bio::Neos team:

• Extrachromosomal MicroDNAs and Chromosomal Microdeletions in Normal Tissues. Shibata Y, et. al. Science. 2012 Apr 6;336(6077):82-6. (PMID:22403181)
• High levels of RNA-editing site conservation amongst 15 laboratory mouse strains. Daneck P, et. al.Genome Biol. 2012 Apr 23;13(4):R26. (PMID:22524474)