Informatics for Integrative Brain Tumor Whole Slide Analysis

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 proposed 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 are developing 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 are also implementing 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 are employing 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.

Collaborating Groups

Biomedical Image Computing and Imaging Informatics (Dr. Lin Yang)

Lin Yang is an associate professor with the J. Crayton Pruitt Family Department of Biomedical Engineering at University of Florida. He leads the Biomedical Image Computing and Imaging Informatics (BICI2) Lab. He was recruited to University of Florida in 2014 as one of the first round preeminence hirings (36 faculty members) through UF Preeminence Hiring Plan.

Department of Biomedical Informatics at Stony Brook

Joel H. Saltz, MD, PhD, Cherith Professor and Founding Chair, Department of Biomedical Informatics; Vice President for Clinical Informatics, Stony Brook Medicine; Associate Director, Stony Brook University Cancer Center.