Grant Number:	1R01CA284646-01 Interpret this number
Primary Investigator:	Braithwaite, Dejana
Organization:	University Of Florida
Project Title:	Advancing Precision Lung Cancer Surveillance and Outcomes in Diverse Populations (PLUS2)
Fiscal Year:	2023

Due in part to recent advances in screening and treatment, the 5-year relative survival rate for patients with early-stage non-small cell lung cancer (NSCLC), the leading cause of cancer death worldwide, continues to increase each year. The uptake of guideline-recommended computed tomography (CT) imaging surveillance semiannually for 2 years and annually for up to 5 years following curative-intent therapy is increasing rapidly in the U.S., despite unclear evidence regarding its benefit in reducing mortality. Furthermore, clinical trials of CT imaging surveillance have not been reported among U.S. populations with NSCLC. This gap in research is alarming and portends a low quality of evidence in clinical guidelines. Currently, no comprehensive lung cancer surveillance data source exists that catalogs real-world lung cancer surveillance utilization patterns and downstream outcomes, both of which are necessary to develop evidence-based recommendations for surveillance following curative-intent therapy. This project, Advancing Precision Lung Cancer Surveillance and Outcomes in Diverse Populations (PLUS2), will create this unique data source to study, understand, and optimize lung cancer surveillance and downstream outcomes. Building on the extant infrastructure and preliminary data from the lung cancer screening registry of the PCORI- and NCI-funded OneFlorida+ Clinical Research Consortium, a network of community practices that serve Florida, Georgia, and Alabama, PLUS2 will leverage multilevel data from electronic health records, claims, and system-level factors for patients with early-stage NSCLC who have completed curative-intent therapy (n~27,217; median age 70) from 2012-2022 (retrospective cohort) and 2022-2025 (prospective cohort). The overarching goal of the project is to evaluate the comparative effectiveness of lung cancer surveillance strategies, principally semi-annual versus annual CT surveillance, in relation to long-term outcomes among diverse patients with early-stage NSCLC within the U.S. population. By generating previously unavailable real-world data from NCI’s Lung Cancer Intervention and Surveillance Modeling Network (CISNET) for use in validated simulation models, this proposal responds directly to calls to improve patient-centered decision-making in lung cancer surveillance candidates for whom the net benefits of surveillance are currently uncertain. This study is foundational for lung cancer surveillance practice change.




Toward a Computable Phenotype for Determining Eligibility of Lung Cancer Screening Using Electronic Health Records.
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PMID: 39818952
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Me-LLaMA: Medical Foundation Large Language Models for Comprehensive Text Analysis and Beyond.
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EPub date: 2024-12-18 00:00:00.0.
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Racial/ethnic disparities, artificial intelligence, and cutting-edge research: Proceedings from the 2023 Florida cardio-oncology symposium.
Authors: Bruno K.A. , Fradley M.G. , Brown S.A. , Guha A. , Cousin L. , Guo Y. , O'Dell W.G. , Smuder A.J. , Yang S. , Braithwaite D. , et al. .
Source: American Heart Journal Plus : Cardiology Research And Practice, 2024 Oct; 46, p. 100469.
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Household Income Is Associated with Chronic Pain and High-Impact Chronic Pain among Cancer Survivors: A Cross-Sectional Study Using NHIS Data.
Authors: Valvi N. , Tamargo J.A. , Braithwaite D. , Fillingim R.B. , Karanth S.D. .
Source: Cancers, 2024-08-15 00:00:00.0; 16(16), .
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Evolution of digital twins in precision health applications: a scoping review study.
Authors: Huang Y. , Dai H. , Xu J. , Wei R. , Sun L. , Guo Y. , Guo J. , Bian J. .
Source: Research Square, 2024-08-07 00:00:00.0; , .
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Authors: Xie Q. , Chen Q. , Chen A. , Peng C. , Hu Y. , Lin F. , Peng X. , Huang J. , Zhang J. , Keloth V. , et al. .
Source: Research Square, 2024-05-22 00:00:00.0; , .
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Authors: Yang D. , Karanth S.D. , Yoon H.S. , Yang J.J. , Lou X. , Bian J. , Zhang D. , Guo Y. , Yaghjyan L. , Akinyemiju T. , et al. .
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Creating Opportunities to Eliminate Disparities in Lung Cancer Outcomes: A Call for Diverse Study Populations. Comment on Kohan et al. Disparity and Diversity in NSCLC Imaging and Genomics: Evaluation of a Mature, Multicenter Database. Cancers 2023, 15, 2096.
Authors: Washington C.J. , Braithwaite D. .
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