Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges,... Curr Epidemiol Rep (2017) 4:307–320 https://doi.org/10.1007/s40471-017-0126-8 CANCER EPIDEMIOLOGY (G COLDITZ, SECTION EDITOR) Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions 1 2 3,4 5 Lori C. Sakoda & Louise M. Henderson & Tanner J. Caverly & Karen J. Wernli & Hormuzd A. Katki Published online: 24 October 2017 Springer International Publishing AG 2017 Abstract populations are limited. Little is also known about the extent Purpose of Review Risk prediction models may be useful for to which risk prediction models are being applied in clinical facilitating effective and high-quality decision-making at crit- practice and influencing decision-making processes and out- ical steps in the lung cancer screening process. This review comes related to lung cancer screening. provides a current overview of published lung cancer risk Summary Current evidence is insufficient to determine which prediction models and their applications to lung cancer screen- lung cancer risk prediction models are most clinically useful ing and highlights both challenges and strategies for improv- and how to best implement their use to optimize screening ing their predictive performance and use in clinical practice. effectiveness and quality. To address these knowledge gaps, Recent Findings Since the 2011 publication of http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Current Epidemiology Reports Springer Journals

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

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Publisher
Springer International Publishing
Copyright
Copyright © 2017 by Springer International Publishing AG
Subject
Medicine & Public Health; Epidemiology
eISSN
2196-2995
D.O.I.
10.1007/s40471-017-0126-8
Publisher site
See Article on Publisher Site

Abstract

Curr Epidemiol Rep (2017) 4:307–320 https://doi.org/10.1007/s40471-017-0126-8 CANCER EPIDEMIOLOGY (G COLDITZ, SECTION EDITOR) Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions 1 2 3,4 5 Lori C. Sakoda & Louise M. Henderson & Tanner J. Caverly & Karen J. Wernli & Hormuzd A. Katki Published online: 24 October 2017 Springer International Publishing AG 2017 Abstract populations are limited. Little is also known about the extent Purpose of Review Risk prediction models may be useful for to which risk prediction models are being applied in clinical facilitating effective and high-quality decision-making at crit- practice and influencing decision-making processes and out- ical steps in the lung cancer screening process. This review comes related to lung cancer screening. provides a current overview of published lung cancer risk Summary Current evidence is insufficient to determine which prediction models and their applications to lung cancer screen- lung cancer risk prediction models are most clinically useful ing and highlights both challenges and strategies for improv- and how to best implement their use to optimize screening ing their predictive performance and use in clinical practice. effectiveness and quality. To address these knowledge gaps, Recent Findings Since the 2011 publication of

Journal

Current Epidemiology ReportsSpringer Journals

Published: Oct 24, 2017

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