Observer Studies Involving Search: Modeling and Analysis

描述(由申请人提供)：这项研究涉及测量成像系统在检测乳腺癌等任务中的表现。受试者操作特征(ROC)方法是目前的黄金标准，它使用患者级别的信息，即女性怀疑患有乳腺癌。位置特定的自由反应ROC(FROC)方法在放射科医生的报告中使用额外的位置级别信息，例如，癌症位于左乳房并且存在于特定位置。在资助期间取得的进展导致了一种新的基于感知的FROC模型和数据模拟器，以及适用于人类观察者和计算机辅助检测(CAD)算法的几种有效的数据分析方法。使用PI的想法和软件的论文越来越多地在会议和期刊上发表，他的工作引起了激烈的辩论。竞争性更新项目的总体目标是通过解决当前方法的一些局限性，继续推进这一领域的最先进技术。具体目标1：品质因数(FOM)是统计能力和临床相关性的关键决定因素，但目前所有的FOM都是基于病变的，病变较多的病例对FOM的贡献比病变较少的病例更多，临床上不太重要的病变与较重要的病变贡献相同；我们将开发新的基于病例的FOM，以克服这些限制。具体目标2：现实模拟器在使用模拟器进行方法验证时产生信心。我们将通过加入更真实的关联效果来扩展当前的模拟器，并将开发方法来校准模拟器以适应真实数据集，从而允许方法开发人员将模拟器调整到特定的应用程序。该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方法中的几个实际问题：接近标准的任意性，即标记必须离病变多近才能真正检测到观察者；缺乏用于规划前瞻性研究的样本量估计方法；以及缺乏分析临床现实数据采集场景的方法，例如多个视角和乳房以及每个病例的多种病变类型。具体目标4：我们将在乳房X光摄影中使用独立获得的ROC、FROC和结果数据来验证该方法。对于返回筛查的正常病例或送往活检的异常病例，结果被定义为良好，否则为差。我们将检验这一假设，即FROC与结果的相关性更好，并产生比ROC更大的统计能力。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走向特定地点的分析，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，从而有助于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.