Evidence map›Paper›PMID 32754420›Full record

ArticleIEEE access : practical innovations, open solutions2019

Using Sequential Decision Making to Improve Lung Cancer Screening Performance.

Panayiotis Petousis, Audrey Winter, William Speier, Denise R Aberle, William Hsu, Alex A T Bui

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE access : practical innovations, open solutions, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
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  3. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Panayiotis PetousisUCLA Bioengineering Department, Los Angeles, CA 90095, USA.
Audrey WinterDepartment of Radiological Sciences, UCLA Medical and Imaging Informatics, Los Angeles, CA 90095, USA.
William SpeierDepartment of Radiological Sciences, UCLA Medical and Imaging Informatics, Los Angeles, CA 90095, USA.
Denise R AberleUCLA Bioengineering Department, Los Angeles, CA 90095, USA.
William HsuUCLA Bioengineering Department, Los Angeles, CA 90095, USA.
Alex A T BuiUCLA Bioengineering Department, Los Angeles, CA 90095, USA.

Funding

Molecular and Imaging Biomarkers for Early Lung Cancer Detection in the Setting of Indeterminate Pulmonary NodulesR01CA210360 · NCI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI ABERLE, DENISE R., LENBURG, MARC ELLIOTT · 2016 to 2021
$3.1M
Individually-tailored clinical decision support for management of indeterminate pulmonary nodulesR01CA226079 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ABERLE, DENISE R., BUI, ALEX · 2019 to 2023
$2.3M
NCI NIH HHS R01 CA210360NCI NIH HHS R01 CA226079
6 · The paper itself

Abstract

Globally, lung cancer is responsible for nearly one in five cancer deaths. The National Lung Screening Trial (NLST) demonstrated the efficacy of low-dose computed tomography (LDCT) to identify early-stage disease, setting the basis for widespread implementation of lung cancer screening programs. However, the specificity of LDCT lung cancer screening is suboptimal, with a significant false positive rate. Representing this imaging-based screening process as a sequential decision making problem, we combined multiple machine learning-based methods to learn a partially-observable Markov decision process that simultaneously optimizes lung cancer detection while enhancing test specificity. Using NLST data, we trained a dynamic Bayesian network as an observational model and used inverse reinforcement learning to discover a rewards function based on experts' decisions. Our resultant predictive model decreased the false positive rate while maintaining a high true positive rate at a level comparable to human experts. Our model also detected a number of lung cancers earlier.

Indexed as

Dynamic Bayesian networksEarly disease predictionLung cancer screeningPartially observable Markov decision processesQMDP algorithm

Identifiers

PMID32754420
PMCPMC7402617

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.