Clinical Cytometry Analysis Software with Automated Gating

具有自动门控功能的临床细胞计数分析软件

• 批准号: 8139155
• 负责人: ADAM S TREISTER
• 金额: $ 44.97万
• 依托单位: TREE STAR, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-12-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8139155
• 关键词: AIDS/HIV problem; Affect; Algorithms; Architecture; Authorization documentation; Automation; Biological Assay; Biological Neural Networks; Biomedical Research; Cell physiology; Cells; Characteristics; Classification; Client; Clinical; Clinical Trials; Cluster Analysis; Code; Complex; Computer software; Computers; Consensus; Cytometry; Data; Data Analyses; Data Set; Databases; Diagnosis; Diagnostic; Diagnostic tests; Disease; Documentation; Effectiveness; Environment; Evolution; Flow Cytometry; Foundations; Graph; Grouping; Hospitals; Human; Institution; Instruction; Label; Language; Learning; Machine Learning; Magnetism; Malignant Neoplasms; Manuals; Measurement; Medical center; Methods; Metric; Modeling; Outcome; Patients; Performance; Physicians; Population; Probability; Procedures; Process; Protocols documentation; Quality Control; Records; Reporting; Research; Research Personnel; Sampling; Scientist; Security; Services; Speed; Structure; System; Techniques; Technology; Test Result; Testing; Therapeutic Intervention; Training; Translational Research; Trees; United States National Institutes of Health; Universities; Work; abstracting; cancer diagnosis; cell type; commercial application; data integrity; design; digital; encryption; high throughput screening; improved; operation; patient privacy; public health relevance; repository; research study; response; software systems; technological innovation; tool; translational medicine

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

描述（由申请人提供）：流式细胞术用于快速收集有关细胞类型和功能的大量数据。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。目前，该过程需要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表上的人群。随着数据复杂性的增加，这种门控任务变得更加漫长和费力，而且越来越清楚的是，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获得结果的速度。在许多情况下，在医生有机会查看第一份报告之前，软件将能够识别出是否需要进行额外的测试。目前还没有软件能够以自动化且经过严格验证的方式执行复杂的多参数分析。 FlowDx 将填补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) 完全定义实验方案，研究人员可以通过该方案比较相同数据集的两个或多个分类，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，说明该技术影响临床分析的潜力。 4）迭代地实现这些实验的自动化工具，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定量标准，建立系统正常运行时间、吞吐量和稳健性的测量，为后续迭代设定基线。 公共健康相关性：FlowDx 是一个临床细胞计数分析软件项目，旨在创建一种新的、更高效的方法，使用全自动软件系统分析细胞中是否存在癌症、艾滋病毒/艾滋病和其他疾病。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)，Tree Star 软件将以比现在手动方法更快的速度分析患者的细胞样本，并且误报和漏报更少在使用中。 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) 加快向医生提供结果的速度。患者更快地了解结果。治疗干预速度更快。 4) 通过更快地检查、比较和量化更大量的复杂数据，适应大规模研究 5) 将细胞分析费用降低多达 50% 6) 符合 21CFR 第 11 部分指南