This project is to develop, deploy, and disseminate a suite of open source tools and integrated informatics platform that will facilitate multi-scale, correlative analyses of high resolution whole slide tissue image data, spatially mapped genetics and molecular data for cancer research. This platform will play an essential role in supporting studies of tumor initiation, development, heterogeneity, invasion, and metastasis. These tools will allow quantitative analyses of the interplay between morphology and spatially mapped genetics and molecular data and will be used in studies that predict outcome and response to treatment, in radiogenomic and quantitative radiology imaging studies and in studies to identify cancer targets. The software and methods will enable researchers to assemble and visualize detailed, multi-scale descriptions of tissue morphologic changes originating from a wide range of microscopy instruments and make it possible to efficiently manage, interrogate, and explore microscopy imaging data at multiple scales and to identify and analyze features across individuals and cohorts. The project will build on and extend the software and methods we have developed in microscopy imaging, integrative image analysis, high performance computing, databases, and visualization over the past fifteen years and will also leverage, integrate and adapt the Harvard Slicer platform. The design and implementation of the informatics platform will be driven by four well funded, leading edge cancer focused studies along with many additional collaborative efforts including the Cancer Imaging Archive (TCIA), the Mayo Clinic Quantitative Imaging Network site, the Colon Cancer Family Registry and the Polyp Prevention Study.
Cancer is a disease that involves complex interactions between cancer cells, surrounding and distant tissue. Within a given cancer, cancer cells can differ from one another in many ways and cancer cells can exert a variety of types of influence on other tissue. In order to develop effective diagnostic and treatment methods for cancer, we need to understand these complex patterns of interaction. This project will develop and deploy a suite of informatics tools that will enable researchers to study tumors - their structure, their genetics and protein expression - at microscopic scales. Our tools will be employed by basic cancer researchers who study cancer mechanisms, by researchers who seek to discover new therapies and by researchers who employ quantitative imaging methods to assess results of clinical cancer trials.
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|Gomes, Jeremias; de Melo, Alba C M A; Kong, Jun et al. (2018) Cooperative and out-of-core execution of the irregular wavefront propagation pattern on hybrid machines with Intel? Xeon Phi™. Concurr Comput 30:|
|Pantanowitz, Liron; Sharma, Ashish; Carter, Alexis B et al. (2018) Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives. J Pathol Inform 9:40|
|Saltz, Joel; Gupta, Rajarsi; Hou, Le et al. (2018) Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep 23:181-193.e7|
|Thorsson, Vésteinn; Gibbs, David L; Brown, Scott D et al. (2018) The Immune Landscape of Cancer. Immunity 48:812-830.e14|
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|Saltz, Joel; Almeida, Jonas; Gao, Yi et al. (2017) Towards Generation, Management, and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research. AMIA Jt Summits Transl Sci Proc 2017:85-94|
|Teodoro, George; Kurç, Tahsin M; Taveira, Luís F R et al. (2017) Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines. Bioinformatics 33:1064-1072|
|Liang, Yanhui; Wang, Fusheng; Zhang, Pengyue et al. (2017) Development of a Framework for Large Scale Three-Dimensional Pathology and Biomarker Imaging and Spatial Analytics. AMIA Jt Summits Transl Sci Proc 2017:75-84|
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