Computational Bioinformatics & Bio-imaging Laboratory (CBIL)


 
 

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Statistical Visualization of Localized Prostate Cancer (RR12784)
 
 

Multimodality analysis of biological spectrum of specific cell populations characterizing the entire prostate gland hold great promise for studies of prostate disease states. There are significant technical issues specific to utilizing surgical specimens which have yet to be rigorously addressed and completely overcome. The long-term goal is to develop a model for integrating-in three dimensions-probability maps of prostate cell populations (cancer with different grade/stage, PIN lesion, primary tumor, metastatic lesion, gene expression profile) obtained using diagnostic pathology, gene expression, and in-vivo imaging. The central hypotheses are: (1) 3D multimodality diagnostic data are rich in information about mechanisms that underlie prostate cancer progression; and (2) 3D probability maps of prostate cancer fingerprints can effectively organize the histopathology, gene expression profile, and in-vivo imaging feature from multiple samples and many people. The research requires multidisciplinary knowledge of diagnostic pathology, statistical modeling, molecular imaging, and data fusion, which are applied for the first time to the investigation of three specific aims: (1) Precise and probabilistic mapping of prostate cancer distributions characterizing the entire prostate gland in three-dimension; (2) Integration with additional diagnostic data from gene expression profiling and in-vivo functional imaging to determine the anatomic location(s) in which mutations and metastases first occur; and (3) A more complete survey and correlation of the structures of prostate cancer fingerprints in space and time, deep enough to discover the mechanism responsible for tumor behavior and progression. The innovative nature of the project relies on that the 3D model of cancer/normal distributions is an enabling technique for state-of-the-art multi-source data integration into which all available pathoinformatics relevant to the tumor behavior is concurrently incorporated. At the conclusion of the project, we anticipate achieving the following: (1) establish an understanding of the spatial distribution of prostate cancers and the associated pathoinformatics; and (2) provide a model-supported information visualization for the public to study the complete spectrum of prostate cancer progression.

 

 

 

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