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The LLP risk model: an individual risk prediction model for lung cancer.

Cassidy A, Myles JP, van Tongeren M, Page RD, Liloglou T, Duffy SW, Field JK - Br. J. Cancer (2007)

Bottom Line: Significant risk factors were fitted into multivariate conditional logistic regression models.Choosing a 2.5% cutoff to trigger increased surveillance, gave a sensitivity of 0.62 and specificity of 0.70, while a 6.0% cutoff gave a sensitivity of 0.34 and specificity of 0.90.A 10-fold cross validation produced an AUC statistic of 0.70, indicating good discrimination.If independent validation studies confirm these results, the LLP risk models' application as the first stage in an early detection strategy is a logical evolution in patient care.

View Article: PubMed Central - PubMed

Affiliation: Roy Castle Lung Cancer Research Programme, University of Liverpool Cancer Research Centre, Liverpool, L3 9TA, UK.

ABSTRACT
Using a model-based approach, we estimated the probability that an individual, with a specified combination of risk factors, would develop lung cancer within a 5-year period. Data from 579 lung cancer cases and 1157 age- and sex-matched population-based controls were available for this analysis. Significant risk factors were fitted into multivariate conditional logistic regression models. The final multivariate model was combined with age-standardised lung cancer incidence data to calculate absolute risk estimates. Combinations of lifestyle risk factors were modelled to create risk profiles. For example, a 77-year-old male non-smoker, with a family history of lung cancer (early onset) and occupational exposure to asbestos has an absolute risk of 3.17% (95% CI, 1.67-5.95). Choosing a 2.5% cutoff to trigger increased surveillance, gave a sensitivity of 0.62 and specificity of 0.70, while a 6.0% cutoff gave a sensitivity of 0.34 and specificity of 0.90. A 10-fold cross validation produced an AUC statistic of 0.70, indicating good discrimination.If independent validation studies confirm these results, the LLP risk models' application as the first stage in an early detection strategy is a logical evolution in patient care.

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Related in: MedlinePlus

Receiver operating curve for the LLP risk model. The area under the curve is 0.71. The straight line represents the receiver operating characteristic curve expected by chance alone.
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fig1: Receiver operating curve for the LLP risk model. The area under the curve is 0.71. The straight line represents the receiver operating characteristic curve expected by chance alone.

Mentions: Figure 1 shows the receiver operating characteristic curve derived when the model was applied to the case–control population. The area under the curve is 0.71. In addition, a 10-fold cross validation of the LLP risk model produced an area under the curve statistic of 0.70, indicating good discrimination between cases and controls. While this remains to be validated using independent data, the receiver operating characteristic curve gives some insight as to the likely performance of the model using a predefined cutoff. For example, a cutoff at 2.5% would capture 62% of lung cancer cases while including 30% of the controls, giving a sensitivity of 0.62 and specificity of 0.70. A 6% cutoff would capture 34% of lung cancer cases and include only 10% of the controls, giving a sensitivity of 0.34 and a specificity of 0.90.


The LLP risk model: an individual risk prediction model for lung cancer.

Cassidy A, Myles JP, van Tongeren M, Page RD, Liloglou T, Duffy SW, Field JK - Br. J. Cancer (2007)

Receiver operating curve for the LLP risk model. The area under the curve is 0.71. The straight line represents the receiver operating characteristic curve expected by chance alone.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2361453&req=5

fig1: Receiver operating curve for the LLP risk model. The area under the curve is 0.71. The straight line represents the receiver operating characteristic curve expected by chance alone.
Mentions: Figure 1 shows the receiver operating characteristic curve derived when the model was applied to the case–control population. The area under the curve is 0.71. In addition, a 10-fold cross validation of the LLP risk model produced an area under the curve statistic of 0.70, indicating good discrimination between cases and controls. While this remains to be validated using independent data, the receiver operating characteristic curve gives some insight as to the likely performance of the model using a predefined cutoff. For example, a cutoff at 2.5% would capture 62% of lung cancer cases while including 30% of the controls, giving a sensitivity of 0.62 and specificity of 0.70. A 6% cutoff would capture 34% of lung cancer cases and include only 10% of the controls, giving a sensitivity of 0.34 and a specificity of 0.90.

Bottom Line: Significant risk factors were fitted into multivariate conditional logistic regression models.Choosing a 2.5% cutoff to trigger increased surveillance, gave a sensitivity of 0.62 and specificity of 0.70, while a 6.0% cutoff gave a sensitivity of 0.34 and specificity of 0.90.A 10-fold cross validation produced an AUC statistic of 0.70, indicating good discrimination.If independent validation studies confirm these results, the LLP risk models' application as the first stage in an early detection strategy is a logical evolution in patient care.

View Article: PubMed Central - PubMed

Affiliation: Roy Castle Lung Cancer Research Programme, University of Liverpool Cancer Research Centre, Liverpool, L3 9TA, UK.

ABSTRACT
Using a model-based approach, we estimated the probability that an individual, with a specified combination of risk factors, would develop lung cancer within a 5-year period. Data from 579 lung cancer cases and 1157 age- and sex-matched population-based controls were available for this analysis. Significant risk factors were fitted into multivariate conditional logistic regression models. The final multivariate model was combined with age-standardised lung cancer incidence data to calculate absolute risk estimates. Combinations of lifestyle risk factors were modelled to create risk profiles. For example, a 77-year-old male non-smoker, with a family history of lung cancer (early onset) and occupational exposure to asbestos has an absolute risk of 3.17% (95% CI, 1.67-5.95). Choosing a 2.5% cutoff to trigger increased surveillance, gave a sensitivity of 0.62 and specificity of 0.70, while a 6.0% cutoff gave a sensitivity of 0.34 and specificity of 0.90. A 10-fold cross validation produced an AUC statistic of 0.70, indicating good discrimination.If independent validation studies confirm these results, the LLP risk models' application as the first stage in an early detection strategy is a logical evolution in patient care.

Show MeSH
Related in: MedlinePlus