Learning the visual and cognitive bases of lung nodule detection

项目摘要/摘要 肺癌是美国男性和女性中最常见的癌症死亡原因。如果 肺结节可以在早期阶段更可靠地发现，存活率显著提高 是可以实现的。胸部X光片是放射学中最常用的诊断工具之一， 可以揭示出乎意料的肺癌发病率。然而，即使是专业放射科医生也可能无法检测到 高对比度解剖背景下的微小低对比肺结节的存在 胸部X光检查，估计漏检率为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？PI和联合调查员已经形成了协同合作 它将人类视觉、计算建模和神经科学的专业知识结合在一起(童博士) 利用胸部成像和生物医学工程(唐纳利博士)来解决这一长期存在的问题 临床相关性。该项目将开发一种经过验证的计算方法，用于生成不同的 视觉上逼真的模拟结核，以实现以下目标。这些是：1)描述放射科医生 以生态有效的方式在逐个图像的基础上执行，2)开发新的图像- 考虑专家绩效的可计算模型，以及3)开发基于学习的新范式，以 描述结核检测的感知和认知机制，最初是在非专家参与者中， 长期目标是制定一项加强临床培训的方案。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样本，每个样本都放置在一个独特的胸部X光片中。成功将是 通过以下结果衡量标准进行评估。首先，放射科医生应该会发现很难区分真实的 从模拟的结核中。此外，它们在检测/定位模拟结核方面的性能精度 应该能够预测它们对真实结节的准确性。其次，如果模拟的结核适当地捕获了 实际结节外观的变化，然后是接受多次培训的非专家参与者 对于模拟结核和真实结核，使用模拟结核应显示出更好的性能。这种学习-- 基于范例将允许表征感知、认知和基于学习的因素 管理结节检测性能。第三，发展和完善这一以学习为基础的范式 应该有可能改善放射科住院医生的结节检测性能。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被用来制定 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 了解肺结节检测的知觉和认知基础，并促进 新的以学习为基础的方案，以加强对放射科住院医师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.