Quantitative Image Informatics for Cancer Research (QIICR)

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DESCRIPTION (provided by applicant): Imaging has enormous untapped potential to improve cancer research through software to extract and process morphometric and functional biomarkers. In the era of non-cytotoxic treatment agents, multi- modality image-guided ablative therapies and rapidly evolving computational resources, quantitative imaging software can be transformative in enabling minimally invasive, objective and reproducible evaluation of cancer treatment response. Post-processing algorithms are integral to high-throughput analysis and fine- grained differentiation of multiple molecular targets. Software tools used for such analyses must be robust and validated across a range of datasets collected for multiple subjects, timepoints and institutions. Ensuring the validity of this software requires unambiguous specification of analysis protocols, documentation of the analysis results, and clear guidelines for their interpretation. Yet cancer research data does not exist in formats that facilitate advancement of quantitative analysis and there is lack of an infrastructure to support common data exchange and method sharing. We therefore propose to develop and disseminate interoperable image informatics platform for development of software tools for quantitative imaging biomarker discovery. This platform will enable archival, organization, retrieval, dissemination of the data produced by the novel analysis tools and performance evaluation of quantitative analysis methods. Its functionality will be defined by the needs of the active QIN research projects in quantitative imaging biomarker development for prostate adenocarcinoma, head and neck cancer and glioblastoma multiforme. The infrastructure will be based on 3D Slicer, an NIH funded open source platform for image analysis and visualization, and will be accompanied by sample data and step-by-step documentation. We will (1) develop software tools encapsulating analysis and data organization workflows for the specific cancer imaging research applications; (2) implement support for interoperable open formats accepted in the community to enable dissemination and sharing of the analysis results; (3) develop interfaces to community cancer imaging repositories to enable archival and dissemination of the analysis results. RELEVANCE: This project will develop the informatics infrastructure for dissemination of image analysis technology and sharing of the analysis results and validation data. This will lead to improved traceability of the analysis and streamlined multi-site evaluation of imaging biomarkers, ultimately reducing the development time and facilitating the approval process.