Observer Studies Involving Search: Modeling and Analysis

描述（由申请人提供）：该研究涉及测量成像系统在检测乳腺癌等任务中的性能。受试者工作特征（ROC）方法，目前的黄金标准，使用患者水平的信息，一个女人怀疑乳腺癌。位置特异性自由响应ROC（FROC）方法使用放射科医师报告中的附加位置水平信息，例如，癌症在左乳房中并且存在于特定位置。资助期间取得的进展产生了一种新型的基于感知的FROC模型和数据模拟器，以及几种经过验证的数据分析方法，适用于人类观察者和计算机辅助检测（CAD）算法。使用PI的想法和软件的论文越来越多地出现在会议和期刊上，他的工作引起了健康的辩论。竞争性更新项目的总体目标是通过解决当前方法的一些限制，继续推进该领域的最新技术。具体目标1：优值（FOM）是统计功效和临床相关性的关键决定因素，但目前所有FOM均基于病变，病变较多的病例对FOM的贡献大于病变较少的病例，临床上不太重要的病变与更重要的病变的贡献相同;我们将开发新的基于病例的FOM，以克服这些局限性。具体目标2：一个真实的模拟器产生信心的方法验证使用该模拟器。我们将扩展目前的模拟器，结合更现实的相关性的影响，我们将开发方法来校准模拟器的真实的数据集，从而允许方法开发人员调整模拟器的特定应用。模拟器将用于验证目标1中开发的不同分析方法。具体目标3：我们将解决当前FROC方法的几个实际问题：邻近标准的任意性，即，标记必须与病变多近才能将观察者的真实检测归功于观察者;缺乏用于规划前瞻性研究的样本量估计方法;以及缺乏用于分析临床现实数据采集场景的方法，例如每个病例的多个视图和乳房以及多个病变类型。具体目标4：我们将使用独立获得的ROC、FROC和乳腺X线摄影结果数据来验证该方法。结果定义为正常病例返回筛选或异常病例送活检的良好，否则为不良。我们将检验以下假设：FROC与结果更好地相关，并且比ROC产生更大的统计功效。重要的是，由于其内在的吸引力和临床现实性，该领域正越来越多地转向位置特异性分析，因此迫切需要能够分析复杂数据的方法，远远超出当前黄金标准的范围。患者受益于更好的设计和优化的设备，导致癌症的早期诊断和治疗。医疗保健的好处，因为更有效和更具成本效益的研究成为可能，可以作为昂贵的临床试验的替代品。

DESCRIPTION (provided by applicant): The research involves measuring imaging system performance in tasks such as detecting breast cancer. Receiver operating characteristic (ROC) methodology, the current gold-standard, uses patient-level information that a woman has suspected breast cancer. The location-specific free-response ROC (FROC) method uses additional location-level information in the radiologist's report, e.g., the cancer is in the left breast and is present at a particular location. Progress during the funded period has resulted in a novel perceptually-based FROC model and data simulator and several validated methods for analyzing data which are applicable to human observers and computer aided detection (CAD) algorithms. Papers using the PI's ideas and software are being presented in increasing numbers at conferences and in journals, and his work has generated healthy debate. The overall goal of the competing renewal project is to continue advancing the state-of-the-art in this field by addressing a number of limitations of current methods. Specific Aim 1: The figure-of-merit (FOM) is a critical determinant of statistical power and clinical relevance but all current FOMs are lesion-based and cases with more lesions contribute more to the FOM than cases with fewer lesions, and clinically less important lesions contribute equally as more important ones; we will develop novel case-based FOMs that overcome these limitations. Specific Aim 2: A realistic simulator yields confidence in methodology validation using that simulator. We will extend the current simulator by incorporating more realistic correlation effects and we will develop methodology to calibrate the simulator to real datasets thereby allowing the methodology developer to tune the simulator to specific applications. The simulator will be used to validate the different methods of analysis developed in Aim 1. Specific Aim 3: We will address several practical issues with current FROC methodology: arbitrariness of the proximity criterion, i.e., how close a mark must be to a lesion in order to credit the observer for a true detection; lack of sample-size estimation methodology for planning prospective studies; and lack of methods for analyzing clinically realistic data acquisition scenarios such as multiple views and breasts and multiple lesion types per case. Specific Aim 4: We will validate the methodology using independently acquired ROC, FROC and outcome-data in mammography. Outcome is defined as GOOD for normal cases returned to screening or abnormal cases sent to biopsy and BAD otherwise. We will test the hypothesis that FROC better correlates with outcome and yields greater statistical power than ROC. The significance is that the field is increasingly moving towards location-specific analyses, because of its intrinsic appeal and clinical realism, therefore methodology capable of analyzing the complex data, well outside the scope of the current gold-standard, is urgently needed. Patients benefit from better designed and optimized equipment leading to early diagnosis and treatment of cancers. Health care benefits because more efficient and cost-effective studies become possible which could serve as surrogates for expensive clinical trials.