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

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 agestandardised 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. In addition, being the most common cancer with over 1.3 million incident cases per year, lung cancer has the highest worldwide rate of cancer mortality (Parkin et al, 2005). More than half of all cases are diagnosed at an advanced stage when surgical removal is no longer a viable treatment strategy. As a result, the overall 5-year survival rate is low, but stage-specific survival rates differ substantially by stage at presentation (van Rens et al, 2000). This raises the possibility that lung cancer may be an attractive candidate for screening, to detect disease at an early stage when treatment would be more effective. Recent results from the International Early Lung Cancer Action Program would appear to support this argument (I-ELCAP Investigators et al, 2006). While the International Early Lung Cancer Action Program results are very encouraging, there are also potential negative consequences of screening, including screen-detected false positives.Although a mortality benefit from spiral CT has not yet been confirmed in ongoing, large-scale randomised studies, the need to specify a high-risk target population is well accepted, and there has been increasing interest in methods of individual risk prediction for lung cancer. Models have been developed for use within highrisk groups (Bach et al, 2003), and for the general population (van Klaveren et al, 2002), although the latter tend to rely only on age and smoking. While epidemiological risk factors usually show poor discrimination between those who do and do not develop disease (Wald et al, 1999), lung cancer is an exception in that a high proportion of cases are attributable to one risk factor, smoking. However, there is room for further improvement in that many long-term smokers do not develop lung cancer. The predictive accuracy of lung cancer ri...

LLPi: Liverpool Lung Project Risk Prediction Model for Lung Cancer Incidence

“…A plausible explanation for this observation is the small numbers of participants with these respiratory conditions in our study compared with the pooled analysis of 16, 17, and 39 studies for asthma, and other respiratory diseases in the aforementioned meta-analysis, respectively. In addition, occupational exposure to asbestos was a risk factor for lung cancer in the LLP risk model (8). However, in this present study, occupational exposure to asbestos was not a risk factor for lung cancer.…”

“…To date, most risk prediction models for lung cancer were developed in case-control studies (6)(7)(8)(9). Case-control studies are proficient in studying dynamic populations where follow-up is difficult, are usually less expensive, and are less time consuming, but may be plagued by biases and cannot study the incidence of a disease (10)(11)(12)(13).…”

Incorporation of a Genetic Factor into an Epidemiologic Model for Prediction of Individual Risk of Lung Cancer: The Liverpool Lung Project

“…This group of subjects were among those used for developing the original LLP model and were genotyped as part of the Sequenom-Genefinder project. A standardized questionnaire was used to collect clinical and lifestyle data including the five epidemiologic risk factors included in the LLP risk model (4). These were smoking duration (years), occupational exposure to asbestos, family history of lung cancer including age at onset in the affected relatives, prior diagnosis of malignant diseases, and prior diagnosis of pneumonia.…”

“…The detailed LLP recruitment procedure and study protocol have been previously described elsewhere (4,9). Briefly, incident cases of histologically or cytologically confirmed lung cancer, ages between 20 and 80 years, were included.…”

“…Within the Liverpool Lung Project (LLP), we have previously developed and validated a predictive model for 5-year absolute risk of developing lung cancer for an individual with a specific combination of epidemiologic risk factors including smoking duration, previous diagnosis of pneumonia, prior diagnosis of malignant tumor, occupational exposure to asbestos, and family history of lung cancer (4). Recent advancement in genetic epidemiology leading to identification of genetic and molecular variants affecting the risk of disease means that genetic markers such as single-nucleotide polymorphisms (SNP) can be added to risk models for improved prediction of future risk of disease (5)(6)(7).…”