Learning the visual and cognitive bases of lung nodule detection

学习肺结节检测的视觉和认知基础

• 批准号: 10528458
• 负责人: FRANK TONG
• 金额: $ 35.5万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-15 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10528458
• 关键词: 3-Dimensional; Address; Affect; Anatomy; Appearance; Articulation; Attention; Behavioral; Biomedical Engineering; Cancer Etiology; Cessation of life; Chest; Chest imaging; Clinic; Cognitive; Collaborations; Complex; Computer Models; Computing Methodologies; Data; Detection; Development; Diagnostic; Goals; Health; Heart; Human; Image; Incidence; Knowledge; Learning; Lung; Lung nodule; Malignant Neoplasms; Malignant neoplasm of lung; Medical; Medical Imaging; Methods; Modeling; Nature; Nodule; Outcome Measure; Participant; Pathology; Patients; Perception; Performance; Persons; Property; Protocols documentation; Pulmonary Coin Lesion; Pulmonary vessels; Radiology Specialty; Reader; Reporting; Research; Research Personnel; Signal Transduction; Survival Rate; Thoracic Radiography; Training; United States; Variant; Vision; Visual; Woman; X-Ray Medical Imaging; base; clinical training; clinically relevant; cognitive process; computational neuroscience; diagnostic tool; experience; improved; lung basal segment; men; model development; novel; radiologist; rib bone structure; success; visual learning; visual process

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.

项目摘要/摘要 在男性和女性中，肺癌是美国癌症死亡的最常见原因。如果 可以在早期可靠性上检测到肺结节，生存率显着提高 将是可以实现的。胸部X光片是放射学中最常见的诊断工具之一， 可以揭示出意外的肺癌事件。但是，即使是专家放射科医生也可能无法检测到 在A的高对比度解剖背景中存在微妙的低对比度肺结节 胸部X射线，估计遗漏检测率为20-30％。感知机制是什么？ 认知机制和批判性学习经验，这些经验决定了一个人的表现如何 肺结检测的挑战性任务？ PI和共同投资者已经形成了协同的合作 这汇集了人类视觉，计算建模和神经科学（Tong博士）的专业知识 使用胸部成像和生物医学工程（Donnelly博士）来解决这个长期存在的问题 临床相关性。该项目将开发出验证的计算方法，以生成一组多样的 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 以生态有效的方式以逐图为基础的图像性能，2）开发新的图像 - 可计算的模型来说明专家绩效，3）开发一种新颖的基于学习的范式 表征结节检测的感知和认知机制，最初是在非专业参与者中 长期目标是制定一项协议以增强临床培训。该项目将合并 基于2D图像的复杂计算方法以及来自3D CT分段结节的数据 生成各种模拟的结节示例，每个示例都放在独特的胸部X射线中。成功将是 通过以下结果度量进行评估。首先，放射科医生应该发现很难分开真实 来自模拟结节。此外，它们在检测/本地化模拟结节方面的性能准确性 应该可以预测它们对实际结节的准确性。其次，如果模拟结节适当捕获 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 使用模拟结节，应显示模拟和真实结节的性能提高。这个学习 - 基于范式将允许表征感知，认知和基于学习的因素 控制结节检测性能。第三，这种基于学习的范式的发展和完善 应该有可能改善放射学居民中的结节检测性能。最后， 从放射科医生和其他表现最佳参与者那里收集的行为数据将用于开发 结节检测性能的图像计算模型。总体而言，该项目将导致更严格 了解肺结节检测的感知和认知基础，并刺激了 新的基于学习的协议，以增强放射学居民和其他医学专业人员的培训。