Three-Class ROC Analysis for Task-Based Medical Image Quality Assessment

基于任务的医学图像质量评估的三类 ROC 分析

• 批准号: 7298516
• 负责人: xin he
• 金额: $ 8.85万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-09 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7298516
• 关键词: Area; Benign; Biomedical Engineering; Biometry; Class; Classification; Clinical; Communities; Data; Decision Making; Decision Modeling; Decision Theory; Defect; Detection; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Discriminant Analysis; Disease; Doctor of Philosophy; Educational process of instructing; Foundations; Goals; Grant; Heart Diseases; Human; Image; Image Analysis; Imaging Techniques; Investigation; Knowledge; Machine Learning; Malignant Neoplasms; Mammography; Measures; Medical; Medical Imaging; Mentors; Methodology; Methods; Modeling; Myocardial perfusion; Patients; Performance; Phase; Physical assessment; Pilot Projects; Public Health; ROC Curve; Research; Research Personnel; Resolution; Series; Solutions; Standards of Weights and Measures; Statistical Methods; Surface; Task Performances; Techniques; Technology; Technology Assessment; Testing; Training; Work; base; breast cancer diagnosis; career; improved; interest; programs; response; single photon emission computed tomography; statistics

DESCRIPTION (provided by applicant): Conventional ROC analysis has been widely accepted as a standard in the assessment of diagnostic techniques for binary diagnoses. Many medical diagnoses, however, involve multiple diagnostic alternatives. Examples are breast cancer diagnosis using mammography, where the diagnostic classes are normal, benign, or malignant tumor, and cardiac disease diagnosis using myocardial perfusion SPECT (MRS), where the classes are normal, reversible or fixed defect. To assess multi-class diagnostic techniques, multi-class ROC is required, but has remained an unsolved problem ever since the introduction of binary ROC analysis in the 1950s. Sparked by a practical challenge raised by MPS optimization, the candidate proposed a three- class ROC analysis method that extends and unifies the decision theoretic, linear discriminant analysis and probabilistic foundations of binary ROC in a three-class paradigm. She has conducted five preliminary studies on three-class ROC analysis: (1) deriving its decision model [He, Metz, et.al IEEE Trans Med Imag (TMI) vol. 25(5), 2006]; (2) investigating its decision theoretic foundation [He and Frey, TMI, vol. 25(8), 2006]; (3) exploring its linear discriminant analysis (LDA) foundation [He and Frey, TMI, in press, 2006]; (4) establishing its probabilistic foundation; and (5) comparing it with conventional three-class LDA and revealing the limitations of conventional three-class LDA. The candidate obtained a PhD in Biomedical Engineering in December 2005 and had intensive training on medical imaging. She increased her interest in medical image quality assessment during the development of three-class ROC analysis; her knowledge of the statistics and decision theory principals used in this research is self-taught. Further exploring new areas opened by three- class ROC analysis requires systematic understanding of the statistical principles in decision theory, statistical learning, and Bayesian modeling, etc. Thus, she requests a two-year mentored phase focusing on formal biostatistics training. The training phase will substantially enhance the candidate's career development as an interdisciplinary investigator and contribute to her independent research to accomplish the following specific aims: 1) to establish the theoretical foundations of three-class ROC analysis; (2) to develop general statistical methods for three-class ROC analysis; (3) to apply the three-class methodologies to task-based medical image quality assessment. The significance of the proposed work is two-fold. First, it provides a rigorous solution to an open theoretical problem and will open new areas of theoretical research in ROC analysis and medical decision making. Second, it enables applications of task-based assessment techniques for multi-class diagnosis. These techniques have the potential to fundamentally improve current imaging techniques for disease detection and characterization, and thus to enhance doctors' performance in disease diagnosis, which will broadly benefit public health.

描述(由申请人提供)： 传统的ROC分析已被广泛接受为评估二元诊断诊断技术的标准。然而，许多医学诊断涉及多种诊断选择。例如，使用乳房X光摄影诊断乳腺癌，诊断类别为正常、良性或恶性肿瘤，以及使用心肌灌注SPECT(MRS)诊断心脏疾病，类别为正常、可逆或固定缺陷。要评估多类诊断技术，需要多类ROC，但自从20世纪50年代引入二进制ROC分析以来一直是一个悬而未决的问题。受MPS优化带来的实际挑战的启发，候选人提出了一种三类ROC分析方法，该方法在三类范式中扩展和统一了二进制ROC的决策理论、线性判别分析和概率基础。她对三级ROC分析进行了五项初步研究：(1)推导其决策模型[He，Metz等，IEEE Trans Med Imag(TMI)Vol.25(5)，2006]；(2)考察其决策理论基础[He and Frey，TMI，Vol.25(8)，2006]；(3)探索其线性判别分析(LDA)基础[He and Frey，TMI，Press，2006]；(4)建立其概率基础；(5)将其与传统的三级LDA进行比较，揭示传统三级LDA的局限性。应聘者于2005年12月获得生物医学工程博士学位，并接受过医学成像方面的强化培训。在开发三级ROC分析的过程中，她增加了对医学图像质量评估的兴趣；她对本研究中使用的统计和决策理论原理的知识是自学的。进一步探索三级ROC分析开辟的新领域需要系统地理解决策理论、统计学习和贝叶斯建模等方面的统计学原理。因此，她要求进行为期两年的指导阶段，重点是正规的生物统计学培训。培训阶段将极大地促进应聘者作为一名跨学科研究者的职业发展，并有助于她的独立研究，以实现以下具体目标：1)建立三级ROC分析的理论基础；(2)开发三级ROC分析的一般统计方法；(3)将三级方法应用于基于任务的医学图像质量评估。这项拟议工作的意义是双重的。首先，它为一个开放的理论问题提供了一个严谨的解决方案，并将为ROC分析和医疗决策开辟新的理论研究领域。其次，它使基于任务的评估技术能够应用于多类诊断。这些技术有可能从根本上改进目前用于疾病检测和表征的成像技术，从而提高医生在疾病诊断中的表现，这将广泛惠及公众健康。