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

针对不确定肺结节管理的个性化临床决策支持

• 批准号: 10055957
• 负责人: DENISE R. ABERLE
• 金额: $ 46.48万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-12-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10055957
• 关键词: Address; Belief; Benign; Cancerous; Capital; Clinical; Clinical Data; Computer software; Custom; Data; Data Set; Databases; Decision Making; Decision Trees; Dependence; Development; Diagnosis; Diagnostic; Disease; Disease model; Early Diagnosis; Electronic Health Record; Evaluation; Event; Evolution; Frequencies; Future; Goals; Guidelines; Image; Individual; Informatics; Leadership; Life Expectancy; Logistic Regressions; Lung; Lung nodule; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Medical History; Medical center; Methodology; Methods; Modeling; Nodule; Online Systems; Outcome; Patients; Performance; Physicians; Policies; Population; Practice Guidelines; Probability; Process; Recommendation; Recording of previous events; Research; Research Personnel; Resources; Sampling; Series; Smoker; Statistical Models; System; Time; Translating; Translations; United States; Update; Validation; X-Ray Computed Tomography; arm; base; clinical biomarkers; clinical decision support; computed tomography screening; cost; database structure; design; discrete time; high risk; imaging biomarker; imaging informatics; imaging program; improved; individual patient; innovation; low dose computed tomography; lung cancer screening; machine learning method; mortality; novel; predictive modeling; prospective; screening; screening guidelines; screening policy; screening program; statistical and machine learning; support tools; time use; tool; web based interface

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.

摘要（项目描述） 低剂量计算机断层扫描（LDCT）肺部筛查程序的推出正在加速 国家，旨在早期检测肺癌以改善长期生存。但是，这是 这样的成像程序是增加不确定肺结核（IPN）的发现。重大任务 - 保留在屏幕和偶然发现的IPN的有效管理周围：虽然许多是良性的，但 分数将继续被取消。 IPN和关联管理指南的诊断模型 以前已经描述过，但是他们的现实验证是有限的。而且，大多数模型 在一个时间点使用IPN的“快照”，并且无法考虑进行渐进的更改。 现在存在机会通过涵盖患者不断发展的医学来推动此类预测模型 历史记录，结合临床和成像生物标志物以改善预测并单独地管理管理者 随着时间的流逝。 此成像信息提案的目的是开发用于临床决策支持工具 屏幕和偶然发现的IPN的管理。我们解决了两个关键挑战：1） 一个连续的时间模型，用于预测IPN的发展方式； 2）使用此预测来确定 随着时间的推移，一系列动作将为个人优化（筛选）结果。我们首先探索Devel- 连续时间信念网络（CTBN）的选项，这是一种临时概率模型，可预测 患者患肺癌。与传统方法不同，CTBN不需要固定采样频率 随着时间的推移数据（例如，每年进行的所有观察结果），因此更适合现实世界的临床 设置和观察数据集。通过CTBN计算的可能性随后输入到 马尔可夫决策过程（POMDP）指导IPN管理决策。来自 可以选择POMDP，政策（随着时间的推移动作序列）以实现理想的目标（例如，最小化最小化目标 诊断的时间），给定个人观察/决策。对于CTBN和 POMDP，我们探索了设计和实施中的新方法，克服了计算挑战 将这些模型翻译成实践。实现了基于Web的界面，提供了临床界面 Cision制作工具供医生理解模型的建议。评估重点是评估 CTBN和POMDP相对于已知结果的性能，并与其他常规的结果进行比较 方法（例如，逻辑回归，决策树，动态信念网络）；以及 影响决策的系统。这项努力推动了我们过去在概率模型和资本中的研究 - ISE关于肺癌筛查专业知识，包括过去的国家肺筛查试验（NLST）的领导。 这项工作的结果将是一组信息驱动的建模工具和新的临时预测模型 告知IPN管理。