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

  • Cancer Epidemiology (G Colditz, Section Editor)
  • Published:
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Abstract

Purpose of Review

Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice.

Recent Findings

Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening.

Summary

Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.

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Funding

This work was supported in part by a career development award to Dr. Sakoda (K07 CA188142).

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  1. Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA, 94612, USA

    Lori C. Sakoda

  2. Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA

    Louise M. Henderson

  3. Center for Clinical Management Research, Veteran Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA

    Tanner J. Caverly

  4. Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA

    Tanner J. Caverly

  5. Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA

    Karen J. Wernli

  6. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA

    Hormuzd A. Katki

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  1. Lori C. Sakoda
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  2. Louise M. Henderson
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  3. Tanner J. Caverly
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  4. Karen J. Wernli
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  5. Hormuzd A. Katki
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Correspondence to Lori C. Sakoda.

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Conflict of Interest

Lori C. Sakoda, Louise M. Henderson, Karen J. Wernli, and Hormuzd A. Katki each declare no potential conflicts of interest.

Tanner J. Caverly reports grants from Genentech Corporate Giving Scientific Project Support Program outside the submitted work.

Human and Animal Rights and Informed Consent

All reported studies/experiments with human or animal subjects performed by the authors have been previously published and complied with all applicable ethical standards (including the Helsinki Declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).

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This article is part of the Topical Collection on Cancer Epidemiology

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Sakoda, L.C., Henderson, L.M., Caverly, T.J. et al. Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions. Curr Epidemiol Rep 4, 307–320 (2017). https://doi.org/10.1007/s40471-017-0126-8

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  • DOI: https://doi.org/10.1007/s40471-017-0126-8

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