Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results | Zendy

Martin C. Tammemägi | Zendy; Kevin ten Haaf | Zendy; Iakovos Toumazis | Zendy; Chung Yin Kong | Zendy; Summer S. Han | Zendy; Jihyoun Jeon | Zendy; John Commins | Zendy; Thomas Riley | Zendy; Rafael Meza | Zendy

Open Access

Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results

Author(s) -

Martin C. Tammemägi,

Kevin ten Haaf,

Iakovos Toumazis,

Chung Yin Kong,

Summer S. Han,

Jihyoun Jeon,

John Commins,

Thomas Riley,

Rafael Meza

Publication year - 2019

Publication title -

jama network open

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.278

H-Index - 39

ISSN - 2574-3805

DOI - 10.1001/jamanetworkopen.2019.0204

Subject(s) - national lung screening trial , medicine , lung cancer screening , logistic regression , lung cancer , odds ratio , computed tomography , radiology

Key Points Question In this study of data from the National Lung Screening Trial (NLST), can a lung cancer risk model’s prediction be improved by inclusion of lung cancer screening results? Findings In this secondary analysis of NLST data including 22 229 participants, a model incorporating a validated lung cancer risk prediction model, the PLCOm2012 model, with National Lung Screening Trial results (PLCO2012results) predicted future lung cancer significantly better than a model excluding results. Meaning The PLCO2012results model estimates may improve stratification of patients being screened for lung cancer into high- and low-risk strata and may help guide decision making regarding screening interval.

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