Grants and Contracts Details
Description
A persistent difficulty in evaluating the pathologic specimens is its subjective nature, leading to
high inter-observer variability. Recent literature has shown that, when morphologic
classification is assisted with computer-aided analysis, objectivity and reproducibility improves
significantly. Subtle changes in measurable parameters can potentially provide mechanistic
and diagnostic clues which are not always apparent by human visual inspection. Lung cancer
is one of the most serious and life threatening cancers in Kentucky in which successful
prognosis and treatment planning varies greatly depending on consistent, reproducible, and
accurate pathologic diagnosis and prognosis. Yet even differentiating among different types of
lung cancer, such as adenocarcinomas and squamous cell carcinomas, could already be a
challenging task for some cases because it depends on subjective assessment of morphologic
variables of the digitized pathologic specimen. Until now, there is no such an objective,
reproducible, and quantitative imaging informatics system exists in Kentucky, which can help
doctors on computer aided diagnosis/prognosis, treatment outcome prediction, and therapy
planning for lung cancer. In this study, population, informatics and clinical researchers in
University of Kentucky and Markey Cancer Center (MCC) propose to collaborate together to
design, develop, deploy and evaluate a quantitative imaging informatics system to score,
classify and analyze digitized pathology lung tumor specimens for better personalized lung
cancer treatment and control. The main objectives are: 1) Develop robust, high throughput
medical image analysis component to analyze digitized lung tumors specimens; 2) Build a
quantitative imaging informatics (QII) system and make it freely available to the clinical and
research communities, and establish a large scale image database to link digitized pathology
images with clinical metadata stored in Kentucky Cancer Registry and clinical data
warehousing; 3) By discovering statistical relationships among image signatures, protein
expression profiles, stage of disease, response to therapy, survival rate, etc., to achieve the
ultimate goal to discover novel image markers which will help to tailor individualized treatment
for better personalized lung cancer treatment and targeted therapeutics.
Status | Finished |
---|---|
Effective start/end date | 7/1/12 → 6/30/14 |
Funding
- KY Lung Cancer Research Fund: $75,000.00
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