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）：流式细胞仪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，高通量筛查，临床试验和大规模研究实验的瓶颈。该过程当前要求训练有素的技术人员通过手动绘制区域在数据图上识别数字图。随着数据的复杂性的增加，这项门控任务变得更加漫长和费力，越来越清楚的是，将人类处理最小化对于增加吞吐量和一致性至关重要。在临床测试和诊断环境中，自动化的门控将消除标准操作程序（SOP）中一组复杂的人类指示和决策，从而减少了医生的错误和超速结果。在许多情况下，该软件将能够在医生有机会查看第一个报告之前认识到需要进行其他测试的必要性。当前，尚无软件以自动化和严格验证的方式进行复杂的多参数分析。 FlowDX将填补技术发展的重要空白，并为更大的表型研究和将该研究过程转化为临床环境的方式铺平道路。具体目的1）充分定义了实验方案，研究人员可以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，偏见和有效性。 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，说明了该技术影响临床分析的潜力。 4）迭代实施这些工具来自动化这些实验，提高实验能力并在新用例中进行协作。这些目标将在保持软件质量的定量标准，建立系统正常运行时间，吞吐量和稳健性中的测量值，以设定后续迭代的基线。 公共卫生相关性：FlowDX是一个临床细胞仪分析软件项目，旨在使用完全自动化的软件系统创建一种新的，更高效，更有效的方法，用于分析癌症，艾滋病毒/艾滋病和其他疾病的细胞。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软件将以比现在使用的手动方法更快地分析患者的细胞样本，误报和负效率更少。 FlowDX项目1）适合NIH路线图的“转化医学”模型2）减少癌症诊断和其他疾病的错误3）速度结果给医师。患者更快地学习结果。治疗干预速度更快。 4）通过允许更快地检查，比较和量化的更快的复杂数据来适应大规模研究5）将细胞分析的费用降低多达50％6）符合21CFR Part 11指南