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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：
10539247
负责人：
DENISE R. ABERLE
金额：
$ 44.76万
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 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10539247
关键词：
Acceleration Address Belief Benign Cancerous Clinical Clinical Data Computer software 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 United States Update Validation X-Ray Computed Tomography arm clinical biomarkers clinical decision support clinical decision-making computed tomography screening cost database structure design discrete time empowerment 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 translational model 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（项目说明）

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Factors Associated With Nonadherence to Lung Cancer Screening Across Multiple Screening Time Points.

DOI：
10.1001/jamanetworkopen.2023.15250
发表时间：
2023-05-01
期刊：
JAMA NETWORK OPEN
影响因子：
13.8
作者：
Lin, Yannan;Liang, Li-Jung;Ding, Ruiwen;Prosper, Ashley Elizabeth;Aberle, Denise R.;Hsu, William
通讯作者：
Hsu, William

Generalizability and Transportability of the National Lung Screening Trial Data: Extending Trial Results to Different Populations.

DOI：
10.1158/1055-9965.epi-21-0585
发表时间：
2021-12
期刊：
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
影响因子：
0
作者：
Inoue K;Hsu W;Arah OA;Prosper AE;Aberle DR;Bui AAT
通讯作者：
Bui AAT

Association of Intensive Blood Pressure Control and Living Arrangement on Cardiovascular Outcomes by Race: Post Hoc Analysis of SPRINT Randomized Clinical Trial.

DOI：
10.1001/jamanetworkopen.2022.2037
发表时间：
2022-03-01
期刊：
JAMA NETWORK OPEN
影响因子：
13.8
作者：
Inoue, Kosuke;Watson, Karol E.;Kondo, Naoki;Horwich, Tamara;Hsu, William;Bui, Alex A. T.;Duru, O. Kenrik
通讯作者：
Duru, O. Kenrik

AdaDiag: Adversarial Domain Adaptation of Diagnostic Prediction with Clinical Event Sequences.