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

基本信息

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
项目类别：
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

项目摘要

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）美国正在加速推出低剂量计算机断层扫描 (LDCT) 肺部筛查项目各国的目标是早期发现肺癌以提高长期生存率。此类成像程序不断发现不确定的肺结节（IPN）。围绕屏幕和偶然检测到的 IPN 的有效管理仍然存在一些问题：虽然许多是良性的，一小部分将继续发展为 IPN 诊断模型和相关管理指南。前面已经描述过，但它们的现实验证是有限的，而且，大多数模型都是有限的。使用 IPN 在单个时间点的“快照”，而没有考虑渐进的变化。现在有机会通过涵盖患者不断发展的医疗状况来推进此类预测模型历史记录，结合临床和影像生物标志物以改进预测并单独定制管理随着时间的推移，IPN 的数量。该成像信息学提案的目标是开发临床决策支持工具屏幕和偶然检测到的 IPN 的管理我们解决了两个关键挑战：1）开发用于预测 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（筛选）个人的结果。连续时间置信网络 (CTBN) 的优化，这是一种预测某个事件可能性的时间概率模型与传统方法不同，CTBN 不需要固定的采样频率。随着时间的推移数据（例如，每年进行的所有观察），因此更适合现实世界的临床随后将通过 CTBN 计算的概率输入到设置和观测数据集。一个部分可观察的马尔可夫决策过程（POMDP）来指导 IPN 管理决策。 POMDP，可以选择策略（随时间推移的行动序列）来实现期望的目标（例如，最小化诊断时间），考虑到个人过去和当前的观察/决定。 POMDP，我们在设计和实现中探索新颖的方法，克服计算挑战实现将这些模型转化为实践，实施基于网络的界面，提供临床设计。帮助医生理解模型建议的决策工具评估重点是评估。 CTBN 和 POMDP 的性能相对于已知结果以及与其他传统方法的比较方法（例如逻辑回归、决策树、动态信念网络）以及总体影响；这项工作推进了我们过去在概率模型和资本方面的研究。拥有肺癌筛查方面的专业知识，包括过去在国家肺部筛查试验 (NLST) 中的领导地位。这项工作的结果将是一套信息学驱动的建模工具和新的时间预测模型通知 IPN 管理。