2011
DOI: 10.1093/jnci/djr173
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Lung Cancer Risk Prediction: Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial Models and Validation

Abstract: The PLCO lung cancer risk models demonstrate high discrimination and calibration.

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Cited by 258 publications
(223 citation statements)
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“…Other researchers have developed risk models based on demographics from large population-based studies (43). This approach may be useful for the initial identification of a cohort at high risk of lung cancer over a defined period but does not allow repeated reassessment of the risk as many of the demographic factors in the models do not change significantly over the time period.…”
Section: Discussionmentioning
confidence: 99%
“…Other researchers have developed risk models based on demographics from large population-based studies (43). This approach may be useful for the initial identification of a cohort at high risk of lung cancer over a defined period but does not allow repeated reassessment of the risk as many of the demographic factors in the models do not change significantly over the time period.…”
Section: Discussionmentioning
confidence: 99%
“…NLST data suggest that screening is efficacious in a high risk population, but a uniform definition of high risk does not exist. Several models have been created to help predict risk of developing lung cancer in a given individual, using clinical risk factors such as smoking history, age, asbestos exposure and prior history of malignancy [23][24][25] . Use of these models can further define a population at risk who might benefit from screening.…”
Section: Potential Risks Of Ldct Screeningmentioning
confidence: 99%
“…Sigaraya bağlı hasarın ortaya çıkması ortalama 20 yıl sürmekte bu nedenle hastalık 50 yaş ve sonrasında ortaya çıkmaktadır [33,34]. Benzer şekilde bizim çalışma grubumuzda yapılan çoklu risk analizinde artan her bir yaş için 1.373 kat ve sigara içmeyenlere göre içenlerde 35.104 kat daha fazla risk olduğu belirlenmiştir.…”
Section: Demografik öZelliklerin Değerlendirilmesiunclassified