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

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $456,756

## Abstract

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 Tria...

## Key facts

- **NIH application ID:** 9829087
- **Project number:** 5R01CA226079-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** DENISE R. ABERLE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $456,756
- **Award type:** 5
- **Project period:** 2018-12-01 → 2023-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9829087

## Citation

> US National Institutes of Health, RePORTER application 9829087, Individually-tailored clinical decision support for management of indeterminate pulmonary nodules (5R01CA226079-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9829087. Licensed CC0.

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