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.
|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|
|Cooper, Lee Ad; Demicco, Elizabeth G; Saltz, Joel H et al. (2018) PanCancer insights from The Cancer Genome Atlas: the pathologist's perspective. J Pathol 244:512-524|
|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|
|Wilkinson, S; Hou, Y; Zoine, J T et al. (2017) Coordinated cell motility is regulated by a combination of LKB1 farnesylation and kinase activity. Sci Rep 7:40929|
|Zhou, Naiyun; Yu, Xiaxia; Zhao, Tianhao et al. (2017) Evaluation of nucleus segmentation in digital pathology images through large scale image synthesis. Proc SPIE Int Soc Opt Eng 10140:|
|Murthy, Veda; Hou, Le; Samaras, Dimitris et al. (2017) Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images. IEEE Winter Conf Appl Comput Vis 2017:834-841|
|Dmitriev, Konstantin; Kaufman, Arie E; Javed, Ammar A et al. (2017) Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble. Med Image Comput Comput Assist Interv 10435:150-158|
|Saltz, Joel; Sharma, Ashish; Iyer, Ganesh et al. (2017) A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images. Cancer Res 77:e79-e82|
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