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.
Copyright
©2004, Computational Bioinformatics and Bioimaging Laboratory
(CBIL), Alexandria Research Institute, Virginia Tech. Jointly
with The Catholic University of America.
Last
Updated: 03/22/2004. Suggestions/Comments
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