Increasing Nodule Detection in Lung Cancer by Non-Conscious Detection of "Missed" Nodules and Machine Learning

肺癌的5年存活率为21%，超过80%的新患者是在 高级阶段。发现代表肺癌早期阶段的小结节是至关重要的，但 在难以发现的肺结节中，诊断错误可能高达50%。结节检测是一种 困难的搜索任务，使用放射科医生花费的有意识和无意识的脑过程 多年的训练不断提高。目前，检测仅限于那些变得有意识的过程。一个 迫切需要的是表征和利用这些无意识的过程来改进结核检测 有意识的检测极限。这个提议的RO1将使用一种创新的新范式来隔离无意识 在CT图像中搜索肺结节的过程。使用眼球跟踪，该项目将显示清晰和 在没有任何有意识检测的情况下，无意识检测“遗漏”结节的可靠生物标志物 或考虑到结节。这些生物标志物将用于训练机器学习(ML)以检测 “遗漏”的结节。该应用程序的创新之处在于充分利用了放射科医生的专业知识 利用这些无意识检测的生物标志物来发展ML，以读取放射科医生而不是图像； 颠覆ML在放射学上的现状，创造出可以检测到“遗漏”结节的ML。中环 假说将在三个具体目标中进行检验：1：评估已识别的生物标记物在多大程度上 遗漏结节的无意识检测可用于训练和改进ML模型以增加结节 检测；2：量化ML模型显示的位置的反馈是遗漏结节的程度 可以提高结节检测；以及3：指定训练的最大似然模型可以推广到新集合的程度 放射科医生使用一套新的胸部CT来发现漏诊的肺结节，并增加结节的发现。这些 AIMS将通过测试放射科医生使用高速眼球在CT图像中搜索肺结节来实现 跟踪和我们的创新模式，允许我们在未命中和未命中期间隔离无意识过程 证明无意识的过程正在成功地检测到“遗漏的”结节。ML模特将会是 对这些无意识的生物标记物进行训练，以检测“遗漏的”肺结节。ML模型将提供 对放射科医生的重要反馈，以减少意识限制所遗漏的结节数量 侦测。这项拟议的研究意义重大，因为它有望提供强有力的科学依据。 用于在诊断视觉搜索中使用无意识过程，并创建能够 因此，改变临床实践，减少结节漏诊， 改善早期发现，提高肺癌的5年生存率。

Lung cancer has a 5-year survival rate of 21% and more than 80% of all new patients are diagnosed at an advanced stage. Finding small lung nodules representing the early stages of lung cancer are critical, but diagnostic error can be as high as 50% in harder-to-detect lung nodules. Nodule detection is the outcome of a difficult search task which employs both conscious and non-conscious brain processes that radiologists spend years of training enhancing. Currently, detection is constrained to those processes that become conscious. A critical need is characterizing and utilizing these non-conscious processes to improve nodule detection beyond conscious detection limits. This proposed RO1 will use an innovative new paradigm to isolate non-conscious processes during lung nodule searches in CT images. Using eye-tracking, the project will show clear and reliable biomarkers of non-conscious detection for “missed” nodules in the absence of any conscious detection or consideration of the nodule. These biomarkers will be used to train Machine Learning (ML) to detect “missed” nodules. The innovation of this application is capitalizing on the full expertise of the radiologist by utilizing these biomarkers of non-conscious detection to develop ML to read the radiologist and not the image; disrupting the status quo of ML in radiology, and creating ML that can detect “missed” nodules. The central hypothesis will be tested in three Specific Aims: 1: To evaluate the extent to which identified biomarkers of non-conscious detection of missed nodules can be used to train and refine ML models to increase nodule detection; 2: To quantify the extent that feedback of the locations that ML models indicate are missed nodules can increase nodule detection; and 3: To specify the extent that trained ML models can generalize to a novel set of radiologists on a novel set of chest CTs to detect missed lung nodules and increase nodule detection. These Aims will be carried out by testing radiologist on lung nodule searches in CT images using high-speed eye- tracking and our innovative paradigm that allow us to isolate non-conscious processes during misses and demonstrate that non-conscious processes are successfully detecting the “missed” nodules. ML models will be trained on these non-conscious biomarkers to detect “missed” lung nodules. The ML models will provide significant feedback to the radiologists to reduce the number of nodules missed by the limits of conscious detection. The proposed research is significant because it is expected to provide strong scientific justification for the use of non-conscious processes in diagnostic visual search, and to create ML models capable of detecting otherwise “missed” lung nodules; hence, changing clinical practice, reducing nodule misses, improving early detection, and increasing lung cancer's 5-year survival rate.