|Grant Number:||1R43CA112743-01A1 Interpret this number|
|Primary Investigator:||Jacquez, Geoffrey|
|Project Title:||Cancer Cluster Morphology|
DESCRIPTION (provided by applicant): This SBIR Phase I project will develop methods, software and instructional materials for the accurate detection and description of cancer clusters and hotspots. Present state-of-the-art often relies on methods that were defined over a decade ago, do not accurately describe cluster morphology (e.g. shape, excess risk, and boundaries) and that are known to misclassify areas of low risk as part of a cancer cluster. While proven methods are now available for accurately detecting clusters and for quantifying their morphology, these techniques are rarely used by front-line public health professionals who are undertaking cluster investigations in local and state health departments. This arises for several reasons. First, many such professionals are not aware of the new methods, their advantages over present approaches, nor of how to use them. Second, enabling commercial-grade software that is intuitive and easy to use is largely unavailable. Finally, educational initiatives are lacking that target these front-line responders with easy to understand short courses, web-based educational tools, and applied studies. This research will put in place methods, software, and educational programs that address these needs. In Phase I, we will develop methods and prototype software for accurately describing cancer cluster morphology, and will evaluate the techniques based on classification accuracy, type I and type II error using simulated data for which the true extent of clustering is known. In Phase II, we will fully develop the software, establish educational course tools targeting public health professionals, and conduct a series of training workshops to quickly disseminate knowledge and tools. Relevance: The scientific and technological innovations from this research are expected to revolutionize our ability to make sound cancer surveillance and control decisions based on an accurate understanding of the morphology of cancer clusters.