Affecting observer behavior and performance with a new approach to CAD

使用新的 CAD 方法影响观察者的行为和性能

• 批准号: 9110219
• 负责人: DAVID GUR
• 金额: $ 20.1万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-14 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9110219
• 关键词: Affect; Attention; Behavior; Benign; Breast; Cancer Detection; Clinic; Contralateral Breast; Country; Detection; Diagnostic; Health; Image; Individual; Investigation; Lesion; Location; Malignant Neoplasms; Mammary Gland Parenchyma; Mammography; Outcome; Output; Performance; Positioning Attribute; Process; Reader; Research Design; Schedule; Site; Specificity; Structure; System; Testing; arm; base; breast density; calcification; cancer diagnosis; case-based; computer aided detection; experience; improved; innovation; interest; novel strategies; radiologist; rate of change; screening; soft tissue; tool

 DESCRIPTION (provided by applicant): Computer Aided Detection (CAD) tools are routinely used during the interpretation of mammograms. CAD systems are very good at detecting and highlighting (marking) clusters of micro-calcifications, regardless if a radiologist uses CAD as originally intended, namely as a second reader, or as a marker for what to look at, or not, after initial review. When it comes to identifying soft tissue abnormalities, such as masses, asymmetries, or distortions, CAD systems do not perform as well. Consequently, to maintain some reasonable level of sensitivity, many false positive marks need to be identified on the images. On average, a little less than one mark per image is highlighted by these systems. Hence, for a four view examination, it is very common that more than one mark identifying possible soft tissue abnormalities will be provided. As a result, radiologists discard a large number of CAD "suspected" regions and tend to have little confidence in these marks. It has been well documented that the impact on observer performance is minimal, if any, in detecting additional cancers depicted as soft tissue abnormalities. However, the distribution of these marks is non-uniform and depends on both the breast density and tissue structure. As these marks may range from none to as many as twenty marks per case, companies basically artificially limit the total number of marks provided per examination by setting an image based or a case based threshold, and all marks with lower CAD output scores are automatically discarded (not shown to the interpreter). We know from many studies that false negative interpretation of cancers depicted as soft tissue abnormalities are common. As important, we know that in retrospective reviews of missed and detected cancer cases a large fraction of the cancers are actually depicted on prior mammograms one year (or more) earlier than the examination resulting in the "detection" of the cancer. We also know that quite frequently cases that are hard for the observer to interpret are also "more difficult" for the CAD to analyze in terms of performance, namely, they tend to have a larger number of possible marks and frequently higher CAD output scores. There are no studies on how distributions of the number of marks in false negative (or interval) cases and cases depicting abnormalities but detected at a scheduled subsequent examination differ/compare with that of marks in examinations leading to a cancer "detection" (we combine the two groups, of interval cancers and visible but detected at a subsequent scheduled examination, and term them as "missed" cancer). We propose to use a modified CAD system to highlight specific cases rather than "suspected" regions and test in an observer study whether a totally different CAD that provides "warnings" when a case is likely to belong to the type in which there is higher likelihood to miss cancers affects observers' behaviors and performances. Our study design will enable us to compare observer performance "without" CAD results with the use of a conventional, as well as a new and innovative, CAD system.

 描述（由申请人提供）：在解读乳房 X 光照片时通常使用计算机辅助检测 (CAD) 工具，无论放射科医生是否按最初预期使用 CAD，CAD 系统都非常擅长检测和突出显示（标记）微钙化簇。 ，即作为第二个读者，或作为初步审查后要查看或不查看内容的标记，当涉及到识别软组织异常（例如肿块、不对称或扭曲）时， CAD 系统的性能不佳，为了保持一定程度的灵敏度，需要在图像上识别出许多误报标记，因此，这些系统会突出显示每个图像不到一个标记。在四视图检查中，通常会提供多个标记来识别可能的软组织异常，因此，放射科医生会丢弃大量 CAD“可疑”区域，并且往往对这些标记缺乏信心。已有充分记录表明对观察者绩效的影响在检测被描述为软组织异常的其他癌症时，即使有，也是最小的。然而，这些标记的分布是不均匀的，并且取决于乳房密度和组织结构，因为这些标记的范围可能从没有到多达二十个标记。对于每个案例，公司基本上通过设置基于图像或基于案例的阈值人为地限制每次考试提供的分数总数，并且所有 CAD 输出分数较低的分数都会被自动丢弃（我们从许多研究中得知）。对癌症的假阴性解释被描述为同样重要的是，我们知道，在对漏检和检出的癌症病例进行回顾性审查时，很大一部分癌症实际上是在之前的乳房X光检查中描述的，比导致“检出”的检查早了一年（或更长时间）。我们还知道，对于观察者来说难以解释的病例，对于 CAD 来说，在性能方面也“更难”分析，即，它们往往具有更多的可能标记，并且通常具有更高的 CAD。没有关于如何输出分数的研究。假阴性（或间隔）病例和描述异常但在预定的后续检查中检测到的病例中标记数量的分布与导致癌症“检测”的检查中的标记数量分布不同/比较（我们将间隔的两组结合起来）癌症和可见但在随后的预定检查中检测到，并将其称为“错过的”癌症）。提供当一个病例很可能属于漏诊癌症的可能性较高的类型时，“警告”会影响观察者的行为和表现。我们的研究设计将使我们能够将“没有”CAD 结果的观察者表现与使用传统方法进行比较。 ，以及全新的创新 CAD 系统。