Cancer Cluster Morphology

描述（由申请人提供）：该SBIR第一阶段项目将开发用于准确检测和描述癌症集群和热点的方法，软件和教学材料。目前的最新技术水平通常依赖于十多年前定义的方法，不能准确描述集群形态（例如形状，过度风险和边界），并且已知将低风险区域错误分类为癌症集群的一部分。虽然现在已经有可靠的方法可以准确检测集群并量化其形态，但这些技术很少被一线公共卫生专业人员使用，他们正在地方和州卫生部门进行集群调查。这有几个原因。首先，许多这类专业人员不知道新方法，不知道它们相对于现有方法的优势，也不知道如何使用它们。其次，直观易用的商业级软件在很大程度上是不可用的。最后，缺乏针对这些一线应急人员的教育举措，提供易于理解的短期课程、基于网络的教育工具和应用研究。这项研究将提出解决这些需求的方法，软件和教育计划。在第一阶段，我们将开发方法和原型软件，用于准确描述癌症集群形态，并将评估基于分类精度，I型和II型错误的技术，使用模拟数据，其中聚类的真实程度是已知的。在第二阶段，我们将全面开发软件，建立针对公共卫生专业人员的教育课程工具，并举办一系列培训讲习班，以迅速传播知识和工具。相关性：这项研究的科学和技术创新有望彻底改变我们在准确理解癌症集群形态的基础上做出合理的癌症监测和控制决策的能力。

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.