Clinical Cytometry Analysis Software with Automated Gating

描述（由申请人提供）：本拨款申请中描述的拟议临床细胞分析软件项目旨在使用全自动软件系统创建一种新的，更高效和有效的方法来分析癌症，艾滋病毒/艾滋病和其他疾病的存在。使用现代数据挖掘技术（模式识别，特征识别，图像分析），我们将设计软件，以更快的速度分析数据（来自患者的细胞样本），比现在使用的人工方法更少的假阳性和阴性。目的：在软件中组装和验证算法，可以自动对流式细胞术数据中的感兴趣区域进行分类。我们将演示用例所需的特定人群可以被有效地、严格地和可重复地自动识别。开发和验证图形和统计结果，满足FDA对医疗器械软件的要求，简化临床用户的法规遵从性，并自动将分析结果交付给诊断专家系统和/或LIMS系统。满足NIH路线图中概述的转化医学目标。该软件将为临床医生带来目前仅在研究实验室中可用的简化测试。方法：选取4个用例，1个采用综合资料，3个采用临床资料；白血病/淋巴瘤试验，骨髓移植标本纵向移植物抗宿主病（GvHD）预测标志物分析和HIV/AIDS - gag特异性T细胞细胞因子反应谱分析。对于每一项，我们都可以访问由专家分析的大量现有数据。从我们自己的FlowJo软件中的自动门控例程开始，我们将测试和扩展磁门控，概率聚类，减法聚类分析，人工神经和支持向量机（SVM）的应用。使用人类操作员的样本来建立一个控制范围，我们将与我们的合作者合作，针对四种用例测试这五种技术中的每一种。人工分类样本中的事件根据它们被所有操作符包含的频率给予加权得分。将单个操作员的得分或门控算法的得分与专家组的累积得分进行比较，并计算匹配等级。其他验证技术包括关于帕累托最优性的内部测量的组合验证，以及使用外部指标（如调整的Rand指数和信息变化指数）测量的重新采样或扰动数据的预测能力/稳定性自一致性检查。公共卫生相关性：通过消除操作人员的时间，我们估计临床流式细胞术分析的成本可以减少到目前的一半，同时更快地提供结果。通过消除人工创建区域的主观性和人为错误，并减少所创建区域的可变性范围，将会产生更少的假阳性和假阴性，从而改善那些需要治疗但目前方法无法检测到的患者的临床结果。速度的一个数量级增加意味着更快的治疗干预。一种较便宜的检测方法可以使更多的患者获得检测，从而改善结果。

DESCRIPTION (provided by applicant): The proposed Clinical Cytometry Analysis Software Project described in this grant application is designed to create a new, more efficient and effective way of analyzing cells for the presence of cancer, HIV/AIDS and other disease, using a fully automated software system. Using modern data mining techniques (pattern recognition, feature recognition, image analysis) we will design software which will analyze data (the cell samples from patients) at a much faster rate and with fewer false positives and negatives than the manual method now in use. Objectives: Assemble and validate algorithms in software that can automatically classify regions of interest in flow cytometry data. We will demonstrate that the particular populations required by our use cases can be validly, rigorously and repeatably identified automatically. Develop and validate graphical and statistical results that satisfy FDA requirements for medical device software, simplify regulatory compliance by the clinical user, and automatically deliver analysis results to diagnostic expert systems and/or LIMS systems. Satisfy the translational medicine goals outlined in the NIH Roadmap. This software will bring the clinician streamlined testing currently only available in research labs. Methods: Four use cases have been selected, one employing synthetic data and three clinical data; Leukemia/Lymphoma test, Analysis of longitudinal Graft vs. Host Disease (GvHD) in bone marrow transplant specimens for predictive markers and HIV/AIDS - Gag-specific T cell cytokine response profile assay. For each we have access to a substantial body of existing data, analyzed by experts. Beginning with the autogating routines in our own FlowJo software, we will test and expand the application of Magnetic gating, Probability Clustering, Subtractive Cluster Analysis, Artificial Neural and Support Vector Machines (SVM). Using a sampling of human operators to establish a control range, we will test each of these five techniques against the four use cases in cooperation with our collaborators. Events in the manually classified samples are given a weighted score based on the frequency with which they are included by all the operators. A single operator's score or the gating algorithm's score is compared with the cumulative score of the expert group and a match rating is computed. Additional validation techniques include combinatory validation on internal measures with respect to Pareto optimality, and Predictive Power/Stability self consistency checks using resampled or perturbed data measured with external indices such as the adjusted Rand index and the Variation of Information index. PUBLIC HEALTH RELEVANCE: By eliminating the operator's time, we estimate that the cost of clinical flow cytometry analysis can be reduced to half the current figure while delivering the results much faster. By eliminating the subjectivity and human error of manually created regions and reducing the range of variability of the so created, there would result fewer false positives and false negatives, improving the clinical outcome for those patients needing therapy but undetected by current methods. An order of magnitude increase in speed means faster therapeutic intervention. A less expensive test improves outcome by making the test accessible to more patients.