Clinical Cytometry Analysis Software with Automated Gating

描述（由申请人提供）：流式细胞术用于快速收集大量关于细胞类型和功能的数据。对数十万个细胞进行分类的手动过程形成了诊断、高通量筛选、临床试验和大规模研究实验的瓶颈。该过程目前需要一名训练有素的技术人员通过手动绘制区域来识别数据数字图上的人群。随着数据复杂性的增加，这种门控任务变得更加冗长和费力，并且越来越清楚的是，最大限度地减少人工处理对于提高吞吐量和一致性至关重要。在临床测试和诊断环境中，自动门控将消除标准操作程序（SOP）中的一组复杂的人工指令和决策，从而减少错误并加快医生的结果。在许多情况下，该软件将能够在医生有机会查看第一份报告之前识别出需要进行额外测试。目前，没有软件可用于以自动化和严格验证的方式执行复杂的多参数分析。FlowDx将填补该技术发展中的一个重要空白，并为更大规模的表型研究以及将该研究过程转化为临床环境铺平道路。具体目标1）完全定义实验协议，研究人员可以比较相同数据集的两个或更多分类，以研究人类和算法分类器的差异，偏差和有效性。2)描述和评估比较分类算法性能的指标。3)对我们确定的用例进行分析实验，说明这种技术影响临床分析的潜力。4)迭代地实现这些工具来自动化这些实验，提高实验能力，并在新的用例中进行协作。这些目标将得到满足，同时保持软件质量的量化标准，建立系统可靠性、吞吐量和稳健性的衡量标准，为随后的迭代设定基线。 公共卫生相关性：FlowDx是一个临床细胞计数分析软件项目，旨在使用全自动软件系统创建一种新的，更高效，更有效的方法来分析细胞中是否存在癌症，HIV/ AIDS和其他疾病。使用磁门控，概率聚类，减法聚类分析，人工神经网络和支持向量机（SVM），树星星软件将分析患者的细胞样本在一个更快的速度和更少的假阳性和阴性比现在使用的手动方法。FlowDx项目1）符合NIH路线图的“转化医学”模型2）减少癌症和其他疾病诊断中的错误3）加快医生的结果。患者可以更快地了解结果。治疗干预更快。4)通过允许更快速地检查、比较和量化更大量的复杂数据来简化大规模研究5）将细胞分析的费用降低多达50% 6）符合21 CFR Part 11指南

DESCRIPTION (provided by applicant): Flow cytometry is used to rapidly gather large quantities of data on cell type and function. The manual process of classifying hundreds of thousands of cells forms a bottleneck in diagnostics, high-throughput screening, clinical trials, and large-scale research experiments. The process currently requires a trained technician to identify populations on a digital graph of the data by manually drawing regions. As the complexity of the data increases, this gating task becomes more lengthy and laborious, and it is increasingly clear that minimizing human processing is essential to increasing both throughput and consistency. In clinical tests and diagnostic environments, automated gating would eliminate a complex set of human instructions and decisions in the Standard Operating Procedure (SOP), thereby reducing error and speeding results to the doctor. In many cases, the software will be able to recognize the need for additional tests before the doctor has an opportunity to look at the first report. Currently no software is available to perform complex multi-parameter analyses in an automated and rigorously validated manner. FlowDx will fill an important gap in the evolution of the technology and pave the way for ever larger phenotypic studies and for the translation of this research process to a clinical environment. Specific Aims 1) Fully define the experimental protocol, whereby a researcher can compare two or more classifications of identical data sets to study the differences, biases and effectiveness of human and algorithmic classifiers. 2) Describe and evaluate metrics that compare the performance of classification algorithms. 3) Conduct analytical experiments on our identified use cases, illustrating the potential of this technique to affect clinical analysis. 4) Iteratively implement the tools to automate these experiments, improve the experimental capabilities, and collaborate in new use cases. These aims will be satisfied while maintaining quantitative standards of software quality, establishing measurements in system uptime, throughput and robustness to set the baseline for subsequent iterations. PUBLIC HEALTH RELEVANCE: FlowDx, a Clinical Cytometry Analysis Software Project is designed to create a new, more efficient, and more effective way of analyzing cells for the presence of cancer, HIV/ AIDS, and other diseases, using a fully automated software system. Using Magnetic Gating, Probability Clustering, Subtractive Cluster Analysis, Artificial Neural Networks, and Support Vector Machines (SVM), Tree Star software will analyze the cell samples from patients at a much faster rate and with fewer false positives and negatives than the manual method now in use. The FlowDx Project 1) Fits the "translational medicine" model of the NIH Roadmap 2) Reduces error in the diagnosis of cancer and other diseases 3) Speeds results to physicians. Patients learn the outcome more quickly. Therapeutic intervention is faster. 4) Accommodates large-scale research by allowing greater volumes of complex data to be much more quickly examined, compared, and quantified 5) Reduces the expense of cell analysis by as much as 50% 6) Conforms to 21CFR Part 11 guidance