Radiologist-Centered Artificial Intelligence (RCAI) for Lung Cancer Screening and Diagnosis

项目摘要 肺癌是美国男性和女性癌症死亡的最常见原因。肺 用低剂量计算机断层扫描（CT）进行癌症筛查已显示出降低肺癌死亡率。 然而，目前的放射学实践仍然遭受（1）高比率的漏诊肿瘤和（2）不精确的肺 结节特征（恶性与良性）。基于人工智能的计算机辅助诊断（CAD） 系统已经帮助放射科医生适度地降低了漏诊率，但还没有广泛应用。 采用的主要原因有三个：缺乏效率、缺乏实时协作和缺乏可解释性。的 该提案的总体目标是创建以放射科医生为中心的人工智能算法， 可解释且可协作，并通过肺癌筛查证明其改善的疗效 实验这项工作的核心假设是，创建基于AI的虚拟认知助理， (VCA)将更好地理解认知偏差，同时为放射科医生提供可解释的反馈 以获得更高的诊断准确性、再现性和效率，从而改善筛查体验。 该提案的具体目标有三个方面。目标1：开发眼动追踪平台， 逼真的放射学阅读室体验，同时从放射科医师提取凝视模式。这将有利于 解决放射科医师和CAD之间真正协作的问题。放射科医生将执行他们的 没有任何约束的筛选（例如，戴眼镜），而他们的凝视模式和其他人机 交互事件被真实的跟踪、处理和存储。目标2：开发自动化实时 协作系统，涉及发达的VCA和放射科医生，以协同提高检测和 诊断性能。使用深度学习（DL）算法，VCA将体现强大的视觉注意力 模型用于表示放射科医生的凝视、视觉搜索和注视模式，并将由检测组成 组件和诊断组件。深度强化学习算法将使通信 和放射科医生之间的联系最后，基于DL的分段组件将动态地启用 VCA用于导出和可视化定量测量（HU统计、体积、长/短轴长度等）和 将它们沿着肿瘤分类标签（良性/恶性）及其概率真实的叠加。目标3： 通过6名放射科医生参与的肺癌筛查实验，评价拟议的VCA的有效性 来自两个不同专业水平的研究所（宾夕法尼亚大学和NIH）。 拟议中的VCA是一个首创的系统，利用强大的DL技术和 与被动DL技术不同， 从标记数据中学习。这项研究的成果预计将通过提供深入的 重新设计当前CAD系统的见解，真正与放射科医生合作，而不是充当第二个 为他们提供意见工具或取代他们，并最终进一步减少肺癌相关死亡。

PROJECT SUMMARY Lung cancer is the most common cause of cancer death in both men and women in the United States. Lung cancer screening with low-dose computed tomography (CT) has been shown to reduce lung cancer mortality. However, current radiology practice still suffers from (1) high rates of missed tumors and (2) imprecise lung nodule characterization (malignant vs. benign). Artificial intelligence (AI) based computer aided diagnosis (CAD) systems have helped radiologists to reduce the missed-tumor rates moderately, but have not been widely adopted for three key reasons: lack of efficiency, lack of real-time collaboration, and lack of interpretability. The overall goal of this proposal is to create radiologist-centered artificial intelligence algorithms that are both interpretable and collaborative and to demonstrate their improved efficacy via lung cancer screening experiments. The central hypothesis of this effort is that the creation of an AI based virtual cognitive assistant (VCA) will provide a better understanding of cognitive biases while offering interpretable feedback to radiologists for an improved screening experience with higher diagnostic accuracy, reproducibility, and efficiency. Specific aims of the proposal are three-fold. Aim 1: To develop an eye-tracking platform that offers a realistic radiology reading room experience while extracting gaze patterns from radiologists. This will facilitate addressing the problem of true collaboration between radiologists and CAD. Radiologists will perform their screening without any constraints (e.g., wearing glasses) while their gaze patterns and other human-computer interaction events are tracked, processed, and stored in real time. Aim 2: To develop an automated real-time collaborative system involving a developed VCA and the radiologist to synergistically improve detection and diagnostic performances. Using deep learning (DL) algorithms, the VCA will embody a powerful visual attention model to represent radiologists’ gaze, visual search, and fixation patterns, and will be composed of a detection component and a diagnostic component. A deep reinforcement learning algorithm will enable communication between the VCA and the radiologist. Lastly, a DL-based segmentation component will, on the fly, enable the VCA to derive and visualize quantitative measures (HU statistics, volume, long/short axes lengths, etc.) and overlay them along with the tumor classification label (benign/malignant) and its probability in real time. Aim 3: To evaluate the efficacy of the proposed VCA via lung cancer screening experiments involving six radiologists from two institutes (University of Pennsylvania and NIH) at different expertise levels. The proposed VCA is a first-of-a-kind-system to exploit the synergy between powerful DL technology and experts (humans) to attempt boost clinical diagnostic performance of radiologists, unlike passive DL techniques that learn from labeled data. The outcome of this research are expected to be transformative by providing deep insights for re-designing current CAD systems to truly collaborate with radiologists, instead of acting as second opinion tools for them or replacing them, and by ultimately further reducing lung cancer-related deaths.