High-resolution image analysis of digitized pathology slides coupled with molecular data has enormous potential to provide additional information for stratifying patients in terms of prognosis and therapy. We propose to develop methods, analytic pipelines, and data management tools that will make it feasible to systematically carry out large-scale comparative analyses of brain tumor histological features and of patterns of protein and gene expression. We will develop information models to manage information associated with analysis of brain tumor whole virtual slide data. These models will capture information about context relating to patient data, specimen preparation, and special stains, human observations involving histological classification and characteristics, algorithmic composition, parameterization and input data corresponding to analysis pipelines, and algorithm and human-described segmentations, features, and classifications. We will implement middleware for high-performance database and query support for queries that selects subsets of image data and results based on metadata on images and provenance information;that compare features, spatial structures, and classifications obtained from multiple algorithms as well as human markups;and that compare statistical and summary information on features and classifications across multiple image datasets. Using the information models and middleware, we will carry out analysis studies needed to determine the relationship between image analysis derived tumor information and clinical outcome, gene expression category, genetic gains and losses, and methylation status. We will employ a novel automated multiplex quantum dot immunohistochemistry with peptide controls and quantitative image analysis methodology to map the activity of signal transduction pathways and transcriptional networks relative to the tumor microenvironment using histology feature descriptions. We will leverage multivariate data fusion techniques to simultaneously take into account potential correlations and relationships among the measured image features, molecular signatures to predict patient outcomes. We will deploy a data repository populated with images, features, analysis pipelines, provenance information, and analytic results from our project. This repository will provide a publicly available resource for brain tumor research. All software and information models developed in this project will be open source and free for research use.
High-resolution image analysis of digitized pathology slides coupled with molecular data has enormous potential to provide additional information for stratifying patients in terms of prognosis and therapy. We propose to develop methods, analytic pipelines, and data management tools that will make it feasible to systematically carry out large-scale comparative analyses of brain tumor histological features and of patterns of protein and gene expression. We will deploy a data repository populated with images, features, and analytic results from our project that will provide a publicly available resource for brain tumor research.
|Teodoro, George; Kurc, Tahsin; Kong, Jun et al. (2014) Comparative Performance Analysis of Intel Xeon Phi, GPU, and CPU: A Case Study from Microscopy Image Analysis. IEEE Trans Parallel Distrib Syst 2014:1063-1072|
|Yang, Lin; Qi, Xin; Xing, Fuyong et al. (2014) Parallel content-based sub-image retrieval using hierarchical searching. Bioinformatics 30:996-1002|
|Boregowda, Rajeev K; Olabisi, Oyenike O; Abushahba, Walid et al. (2014) RUNX2 is overexpressed in melanoma cells and mediates their migration and invasion. Cancer Lett 348:61-70|
|Roy, Rajarshi; Chen, Wenjin; Cong, Lei et al. (2014) Probabilistic estimation of mechanical properties of biomaterials using atomic force microscopy. IEEE Trans Biomed Eng 61:547-56|
|Qi, Xin; Wang, Daihou; Rodero, Ivan et al. (2014) Content-based histopathology image retrieval using CometCloud. BMC Bioinformatics 15:287|
|Aji, Ablimit; Wang, Fusheng; Vo, Hoang et al. (2013) Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce. Proceedings VLDB Endowment 6:|
|Roy, Rajarshi; Chen, Wenjin; Cong, Lei et al. (2013) A Semi-Automated Positioning System for contact-mode Atomic Force Microscopy (AFM). IEEE Trans Autom Sci Eng 10:|
|Teodoro, George; Pan, Tony; Kurc, Tahsin et al. (2013) Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines. Parallel Comput 39:189-211|
|Gutman, David A; Cobb, Jake; Somanna, Dhananjaya et al. (2013) Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data. J Am Med Inform Assoc 20:1091-8|
|Cooper, Lee A D; Kong, Jun; Gutman, David A et al. (2012) Integrated morphologic analysis for the identification and characterization of disease subtypes. J Am Med Inform Assoc 19:317-23|
Showing the most recent 10 out of 18 publications