Learning the visual and cognitive bases of lung nodule detection

项目概要/摘要 肺癌是美国男性和女性癌症死亡的最常见原因。如果 肺结节可以在早期检测到更高的可靠性，显著提高生存率 是可以实现的。胸片是放射学中最常用的诊断工具， 可以揭示肺癌的意外发病率。然而，即使是专业的放射科医生也可能无法检测到 存在一个微妙的低对比度肺结节对高对比度的解剖背景， 胸部X光检查，估计漏诊率为20- 30%。感知机制是什么， 认知机制和关键的学习经验，决定了一个人能做得多好， 肺结节检测的挑战性任务？主要研究者和合作研究者已形成协同合作 汇集了人类视觉、计算建模和神经科学方面的专业知识（Tong博士） 与胸部成像和生物医学工程（唐纳利博士），以解决这一长期存在的问题，高 临床相关性本项目将开发一种有效的计算方法，用于生成一组不同的 视觉上逼真的模拟结节，以实现以下目标。这些是：1）表征放射科医生 以生态有效的方式逐个图像地执行，2）开发新的图像- 可计算模型，占专家的表现，和3）开发一种新的学习为基础的范例， 表征结节检测的感知和认知机制，最初在非专家参与者中， 其长期目标是制定一项协议，以加强临床培训。该项目将包括 复杂的基于2D图像的计算方法以及来自3D CT分割结节的数据， 生成一组不同的模拟结节示例，每个示例都放置在一个独特的胸部X光片中。成功将是 通过以下结果测量进行评估。首先，放射科医生会发现很难区分真实的 从模拟的结节。此外，它们在检测/定位模拟结节方面的性能准确性 应该预测它们对于真实的结节的准确性。第二，如果模拟结节适当地捕获 真实的结节外观的变化，然后是接受多次培训的非专家参与者 对于模拟的和真实的结节都应该显示出改进的性能。这种学习- 基于范例的方法将允许表征感知、认知和基于学习的因素， 控制结节检测性能。第三，发展和完善这种以学习为基础的范式 应该有潜力提高放射科住院医师的结节检测性能。最后 从放射科医生和其他表现最好的参与者那里收集的行为数据将用于制定一个 结节检测性能的图像可计算模型。作为一个整体，这个项目将导致一个更严格的 了解肺结节检测的感知和认知基础，并促进 新的以学习为基础的协议，以加强放射科住院医生和其他医疗专业人员的培训。

Project Summary/Abstract Lung cancer is the most frequent cause of cancer death in the United States among both men and women. If lung nodules can be detected with greater reliability at an early stage, significant improvements in survival rate would be achievable. Chest radiographs are among the most common diagnostic tool used in radiology, and can reveal unexpected incidences of lung cancer. However, even expert radiologists may fail to detect the presence of a subtle low-contrast pulmonary nodule against the high-contrast anatomical background of a chest X-ray, with estimated rates of missed detection of 20-30%. What are the perceptual mechanisms, cognitive mechanisms, and critical learning experiences that determine how well a person can perform this challenging task of lung nodule detection? The PI and Co-Investigator have formed a synergistic collaboration that brings together expertise in human vision, computational modeling and neuroscience (Dr. Tong) in concert with thoracic imaging and biomedical engineering (Dr. Donnelly) to address this longstanding problem with high clinical relevance. This project will develop a validated computational approach for generating a diverse set of visually realistic simulated nodules to achieve the following goals. These are: 1) to characterize radiologist performance on an image-by-image basis in an ecologically valid manner, 2) to develop a novel image- computable model that accounts for expert performance, and 3) to develop a novel learning-based paradigm to characterize the perceptual and cognitive mechanisms of nodule detection, initially in non-expert participants, with the long-term goal of developing a protocol to enhance clinical training. The project will incorporate sophisticated 2D image-based computational methods as well as data from 3D CT segmented nodules to generate a diverse set of simulated nodule examples, each placed in a unique chest X-ray. Success will be evaluated by the following outcome measures. First, radiologists should find it very difficult to tell apart real from simulated nodules. Moreover, their performance accuracy at detecting/localizing simulated nodules should be predictive of their accuracy for real nodules. Second, if the simulated nodules suitably capture the variations of real nodule appearance, then non-expert participants who receive multiple sessions of training with simulated nodules should show improved performance for both simulated and real nodules. This learning- based paradigm will allow for characterization of the perceptual, cognitive, and learning-based factors that govern nodule detection performance. Third, development and refinement of this learning-based paradigm should have the potential to improve nodule detection performance in radiology residents. Finally, the behavioral data gathered from radiologists and other top-performing participants will be used to develop an image-computable model of nodule detection performance. As a whole, this project will lead to a more rigorous understanding of the perceptual and cognitive bases of lung nodule detection, and spur the development of a new learning-based protocol to enhance the training of radiology residents and other medical professionals.