Imaging based cancer research is in the beginning phases of a transition from analyses based on human observers to the use of advanced computing platforms and software to automatically extract large sets of quantitative image features relevant to prognosis or treatment response. These feature sets can be used to infer phenotypes or correlate with gene-protein signatures. In parallel, radiation oncology is responding to the quantitative requirements of adaptive therapy, where therapy decisions take into account the early response of the tumor to the treatment. Both of these advanced techniques share similar resource requirement, in particular high capacity information repositories co-located with high performance computing capabilities and tools to perform advanced analytics. Drawing on broad experience in imaging informatics we will expand upon the existing Cancer Imaging Archive (TCIA) platform and computational resources of the Washington University Center for High Performance Computing to provide leading edge research services to Quantitative Imaging Network (QIN) researchers. To facilitate this translational research and share our processes and experiences with the academic cancer research community, we propose to: 1. Provide data hosting, management and access capabilities to QIN researchers including longitudinal data collections and advanced Radiation Therapy (RT) data structure support; 2. Develop a QIN portal to support advanced quantitative image processing and biomarker validation through co-located scalable computing and big data infrastructure;3. Support biomarker development and validation with advanced analytics that employ a new generation of statistical tools and data modeling techniques; 4. Provide training and support to permit QIN researchers to effectively utilize this suite of advanced capabilities combined with open source community enablement tools. We have assembled a strong team with a long history of collaboration and extensive experience with open source software development methodologies and advanced imaging informatics. The long-term goal of our team is to develop and deploy software and services to drive advanced quantitative image analysis and biomarker development and provide a gateway for researchers to interrogate multi-modality datasets and mine them to create and validate novel hypotheses in cancer research.
Big data is being seen as the next revolution in cancer research. This project will provide data, computing resources, analytic tools and training to enable the development of novel imaging-based cancer biomarkers. Such comprehensive biomarkers can offer a more personalized profile of the state of the disease, assess the treatment and even predict outcome. With these tools researchers will be better able to use big data resources to improve human health.
|Causey, Jason L; Zhang, Junyu; Ma, Shiqian et al. (2018) Highly accurate model for prediction of lung nodule malignancy with CT scans. Sci Rep 8:9286|
|Bennett, William; Smith, Kirk; Jarosz, Quasar et al. (2018) Reengineering Workflow for Curation of DICOM Datasets. J Digit Imaging 31:783-791|
|Post, Andrew R; Ai, Miao; Kalsanka Pai, Akshatha et al. (2017) Architecting the Data Loading Process for an i2b2 Research Data Warehouse: Full Reload versus Incremental Updating. AMIA Annu Symp Proc 2017:1411-1420|
|Prior, Fred; Smith, Kirk; Sharma, Ashish et al. (2017) The public cancer radiology imaging collections of The Cancer Imaging Archive. Sci Data 4:170124|
|Benedict, Stanley H; Hoffman, Karen; Martel, Mary K et al. (2016) Overview of the American Society for Radiation Oncology-National Institutes of Health-American Association of Physicists in Medicine Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data. Int J Radiat Oncol Biol Phys 95:873-879|
|Post, Andrew R; Pai, Akshatha K; Willard, Richard et al. (2016) Metadata-driven Clinical Data Loading into i2b2 for Clinical and Translational Science Institutes. AMIA Jt Summits Transl Sci Proc 2016:184-93|
|Rosenstein, Barry S; Capala, Jacek; Efstathiou, Jason A et al. (2016) How Will Big Data Improve Clinical and Basic Research in Radiation Therapy? Int J Radiat Oncol Biol Phys 95:895-904|