Individually-tailored clinical decision support for management of indeterminate pulmonary nodules

摘要(项目描述) 低剂量计算机断层扫描(LDCT)肺部筛查计划在美国正在加速推出 旨在更早地发现肺癌，以提高长期存活率。然而，一个后果是 这样的成像计划是更多地发现不明肺结节(IPN)。重要的问题- 仍然围绕着对屏幕和附带检测到的IPN的有效管理：虽然许多是良性的， 一小部分人将继续癌变。IPN的诊断模型和相关的管理指南 之前已经描述过了，但它们在现实世界中的验证是有限的。此外，大多数型号只有 在单个时间点上使用IPN的“快照”，而没有考虑到渐进的变化。 现在有机会通过包含患者不断发展的医学来推进这种预测模型 病史，结合临床和影像生物标记物以改进预测和个性化管理 随着时间的推移，IPN的数量。 这项影像信息学提案的目标是开发一个临床决策支持工具，用于 屏幕和附带检测到的IPN的管理。我们解决了两个关键挑战：1)发展 用于预测IPN将如何演变的连续时间模型；以及2)使用该预测来确定 随着时间的推移，一系列行动将优化(筛查)个人的结果。我们首先要探索的是-- 连续时间信任网络(CTBN)的发展，一种时间概率模型，用于预测 病人会患上肺癌。与传统方法不同，CTBN不需要固定的采样频率 随着时间的推移(例如，每年进行的所有观察)的数据，因此更符合现实世界的临床 设置和观测数据集。通过CTBN计算的概率随后被输入到 用于指导IPN管理决策的部分可观测马尔可夫决策过程(POMDP)。从 POMDP，可以选择策略(随时间的动作序列)来实现期望的目标(例如，最小化 诊断时间)，给出个人过去和当前的观察/决定。对于CTBN和 POMDP，我们在设计和实现中探索了新的方法，克服了计算挑战 实现将这些模型转化为实践。实现了基于Web的界面，为临床提供了 医生理解模型建议的手术制作工具。评估的重点是评估 CTBN和POMDP相对于已知结果的表现以及与其他常规 方法(例如，Logistic回归、决策树、动态信念网络)；以及 影响决策的制度。这一努力推动了我们过去在概率模型和资本方面的研究- 介绍肺癌筛查方面的专业知识，包括过去领导的全国肺筛查试验(NLST)。 这一努力的结果将是一套信息学驱动的建模工具和新的时态预测模型 通知IPN管理层。

ABSTRACT (PROJECT DESCRIPTION) The rollout of low-dose computed tomography (LDCT) lung screening programs is accelerating in the United States, aiming for earlier detection of lung cancer to improve long-term survival. However, a consequence of such imaging programs is the increased discovery of indeterminate pulmonary nodules (IPNs). Significant ques- tions remain around the effective management of screen- and incidentally-detected IPNs: while many are benign, a fraction will go on to become cancerous. Diagnostic models for IPNs and associated management guidelines have been described previously, but their real-world validation is limited. Moreover, the majority of models only use a “snapshot” of the IPN at a single point in time and fail to take into consideration progressive changes. Opportunities now exist to advance such predictive models by encompassing the patient's evolving medical history, combining clinical and imaging biomarkers to improve prediction and individually-tailor the management of IPNs over time. The objective of this imaging informatics proposal is the development of a clinical decision support tool for the management of screen- and incidentally-detected IPNs. We address two key challenges: 1) the development of a continuous-time model for predicting how the IPN will evolve; and 2) the use of this prediction to determine a series of actions over time that will optimize (screening) outcomes for the individual. We first explore the devel- opment of a continuous time belief network (CTBN), a temporal probabilistic model to predict the likelihood of a patient to develop lung cancer. Unlike traditional approaches, CTBNs do not require fixed sampling frequency of the data over time (e.g., all observations made annually) and are thus more amenable to real-world clinical settings and observational datasets. The probabilities computed through the CTBN are subsequently input into a partially-observable Markov decision process (POMDP) to guide IPN management decisions. From the POMDP, policies (sequences of actions over time) can be chosen to achieve a desired goal (e.g., minimizing time to diagnosis), given past and current observations/decisions for an individual. For both the CTBN and POMDP, we explore novel methods in the design and implementation, overcoming computational challenges to realize translation of these models into practice. A web-based interface is implemented, providing a clinical de- cision making tool for physicians to understand the models' recommendations. Evaluation focuses on assessing the performance of the CTBN and POMDP relative to known outcomes and compared to other conventional methods (e.g., logistic regression, decision trees, dynamic belief networks); as well as the overall impact of the system to influence decision-making. This effort advances our past research in probabilistic models and capital- izes on expertise in lung cancer screening, including past leadership of the National Lung Screening Trial (NLST). The result of this effort will be a set of informatics-driven modeling tools and new temporal predictive models informing IPN management.