Liverpool Lung Project Risk Model Research Articles

Abstract IA17: Risk prediction modeling in lung cancer: How can we improve?

Abstract IA17: Risk prediction modeling in lung cancer: How can we improve?

The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer.

Lung cancer kills more people than any other cancer in the UK (5-year survival < 13%). Early diagnosis can save lives. The USA-based National Lung Cancer Screening Trial reported a 20% relative reduction in lung cancer mortality and 6.7% all-cause mortality in low-dose computed tomography (LDCT)-screened subjects. To (1) analyse LDCT lung cancer screening in a high-risk UK population, determine optimum recruitment, screening, reading and care pathway strategies; and (2) assess the psychological consequences and the health-economic implications of screening. A pilot randomised controlled trial comparing intervention with usual care. A population-based risk questionnaire identified individuals who were at high risk of developing lung cancer (≥ 5% over 5 years). Thoracic centres with expertise in lung cancer imaging, respiratory medicine, pathology and surgery: Liverpool Heart & Chest Hospital, Merseyside, and Papworth Hospital, Cambridgeshire. Individuals aged 50-75 years, at high risk of lung cancer, in the primary care trusts adjacent to the centres. A thoracic LDCT scan. Follow-up computed tomography (CT) scans as per protocol. Referral to multidisciplinary team clinics was determined by nodule size criteria. Population-based recruitment based on risk stratification; management of the trial through web-based database; optimal characteristics of CT scan readers (radiologists vs. radiographers); characterisation of CT-detected nodules utilising volumetric analysis; prevalence of lung cancer at baseline; sociodemographic factors affecting participation; psychosocial measures (cancer distress, anxiety, depression, decision satisfaction); and cost-effectiveness modelling. A total of 247,354 individuals were approached to take part in the trial; 30.7% responded positively to the screening invitation. Recruitment of participants resulted in 2028 in the CT arm and 2027 in the control arm. A total of 1994 participants underwent CT scanning: 42 participants (2.1%) were diagnosed with lung cancer; 36 out of 42 (85.7%) of the screen-detected cancers were identified as stage 1 or 2, and 35 (83.3%) underwent surgical resection as their primary treatment. Lung cancer was more common in the lowest socioeconomic group. Short-term adverse psychosocial consequences were observed in participants who were randomised to the intervention arm and in those who had a major lung abnormality detected, but these differences were modest and temporary. Rollout of screening as a service or design of a full trial would need to address issues of outreach. The health-economic analysis suggests that the intervention could be cost-effective but this needs to be confirmed using data on actual lung cancer mortality. The UK Lung Cancer Screening (UKLS) pilot was successfully undertaken with 4055 randomised individuals. The data from the UKLS provide evidence that adds to existing data to suggest that lung cancer screening in the UK could potentially be implemented in the 60-75 years age group, selected via the Liverpool Lung Project risk model version 2 and using CT volumetry-based management protocols. The UKLS data will be pooled with the NELSON (Nederlands Leuvens Longkanker Screenings Onderzoek: Dutch-Belgian Randomised Lung Cancer Screening Trial) and other European Union trials in 2017 which will provide European mortality and cost-effectiveness data. For now, there is a clear need for mortality results from other trials and further research to identify optimal methods of implementation and delivery. Strategies for increasing uptake and providing support for underserved groups will be key to implementation. Current Controlled Trials ISRCTN78513845. This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 20, No. 40. See the NIHR Journals Library website for further project information.

Incorporating epistasis interaction of genetic susceptibility single nucleotide polymorphisms in a lung cancer risk prediction model.

Incorporation of genetic variants such as single nucleotide polymorphisms (SNPs) into risk prediction models may account for a substantial fraction of attributable disease risk. Genetic data, from 2385 subjects recruited into the Liverpool Lung Project (LLP) between 2000 and 2008, consisting of 20 SNPs independently validated in a candidate-gene discovery study was used. Multifactor dimensionality reduction (MDR) and random forest (RF) were used to explore evidence of epistasis among 20 replicated SNPs. Multivariable logistic regression was used to identify similar risk predictors for lung cancer in the LLP risk model for the epidemiological model and extended model with SNPs. Both models were internally validated using the bootstrap method and model performance was assessed using area under the curve (AUC) and net reclassification improvement (NRI). Using MDR and RF, the overall best classifier of lung cancer status were SNPs rs1799732 (DRD2), rs5744256 (IL-18), rs2306022 (ITGA11) with training accuracy of 0.6592 and a testing accuracy of 0.6572 and a cross-validation consistency of 10/10 with permutation testing P<0.0001. The apparent AUC of the epidemiological model was 0.75 (95% CI 0.73–0.77). When epistatic data were incorporated in the extended model, the AUC increased to 0.81 (95% CI 0.79–0.83) which corresponds to 8% increase in AUC (DeLong's test P=2.2e-16); 17.5% by NRI. After correction for optimism, the AUC was 0.73 for the epidemiological model and 0.79 for the extended model. Our results showed modest improvement in lung cancer risk prediction when the SNP epistasis factor was added.

The UK Lung Screen (UKLS): Demographic Profile of First 88,897 Approaches Provides Recommendations for Population Screening

The UK Lung Cancer Screening trial (UKLS) aims to evaluate low-dose computed tomography (LDCT) lung cancer population screening in the United Kingdom. In UKLS, a large population sample ages 50 to 75 years is approached with a questionnaire to determine lung cancer risk. Those with an estimated risk of at least 5% of developing lung cancer in the next 5 years (using the Liverpool Lung project risk model) are invited to participate in the trial. Here, we present demographic, risk, and response rate data from the first 88,897 individuals approached. Of note, 23,794 individuals (26.8% of all approached) responded positively to the initial questionnaire; 12% of these were high risk. Higher socioeconomic status correlated positively with response, but inversely with risk (P < 0.001). The 50- to 55-year age group was least likely to participate, and at lowest cancer risk. Only 5% of clinic attendees were ages ≤60 years (compared with 47% of all 88,897 approached); this has implications for cost effectiveness. Among positive responders, there were more ex-smokers than expected from population figures (40% vs. 33%), and fewer current smokers (14% vs. 17.5%). Of note, 32.7% of current smokers and 18.4% of ex-smokers were designated as high risk. Overall, 1,452 of 23,794 positive responders (6.1%) were deemed high risk and attended a recruitment clinic. UKLS is the first LDCT population screening trial, selecting high-risk subjects using a validated individual risk prediction model. (i) better recruitment from ex- rather than current smokers, (ii) few clinic attendees ages early 50s, and (iii) representative number of socioeconomically deprived people recruited, despite lower response rates.

Predictive Accuracy of the Liverpool Lung Project Risk Model

Predictive Accuracy of the Liverpool Lung Project Risk Model

Predictive Accuracy of the Liverpool Lung Project Risk Model

Letters2 April 2013Predictive Accuracy of the Liverpool Lung Project Risk ModelJohn K. Field, PhD, Olaide Y. Raji, PhD, and Stephen W. Duffy, MScJohn K. Field, PhDFrom The University of Liverpool Cancer Research Centre, Institute of Translational Medicine, The University of Liverpool, Liverpool, and Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.Search for more papers by this author, Olaide Y. Raji, PhDFrom The University of Liverpool Cancer Research Centre, Institute of Translational Medicine, The University of Liverpool, Liverpool, and Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.Search for more papers by this author, and Stephen W. Duffy, MScFrom The University of Liverpool Cancer Research Centre, Institute of Translational Medicine, The University of Liverpool, Liverpool, and Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-158-7-201304020-00015 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We thank Dr. Young and colleagues for their interest and useful comments on our article. The LLP risk model was shown to perform better than those based on age and smoking history alone. The superior net benefit shown in 3 independent studies suggested a predicted clinical utility in terms of screening decision at relevant thresholds; however, this was a retrospective performance assessment and the actual effect can only be shown with prospective CT screening trials.The detection rate of 1.5% that Baldwin and associates (1) reported in 2011 was a conservative estimate used to power the UKLS (United ...

Predictive Accuracy of the Liverpool Lung Project Risk Model for Stratifying Patients for Computed Tomography Screening for Lung Cancer

External validation of existing lung cancer risk prediction models is limited. Using such models in clinical practice to guide the referral of patients for computed tomography (CT) screening for lung cancer depends on external validation and evidence of predicted clinical benefit. To evaluate the discrimination of the Liverpool Lung Project (LLP) risk model and demonstrate its predicted benefit for stratifying patients for CT screening by using data from 3 independent studies from Europe and North America. Case-control and prospective cohort study. Europe and North America. Participants in the European Early Lung Cancer (EUELC) and Harvard case-control studies and the LLP population-based prospective cohort (LLPC) study. 5-year absolute risks for lung cancer predicted by the LLP model. The LLP risk model had good discrimination in both the Harvard (area under the receiver-operating characteristic curve [AUC], 0.76 [95% CI, 0.75 to 0.78]) and the LLPC (AUC, 0.82 [CI, 0.80 to 0.85]) studies and modest discrimination in the EUELC (AUC, 0.67 [CI, 0.64 to 0.69]) study. The decision utility analysis, which incorporates the harms and benefit of using a risk model to make clinical decisions, indicates that the LLP risk model performed better than smoking duration or family history alone in stratifying high-risk patients for lung cancer CT screening. The model cannot assess whether including other risk factors, such as lung function or genetic markers, would improve accuracy. Lack of information on asbestos exposure in the LLPC limited the ability to validate the complete LLP risk model. Validation of the LLP risk model in 3 independent external data sets demonstrated good discrimination and evidence of predicted benefits for stratifying patients for lung cancer CT screening. Further studies are needed to prospectively evaluate model performance and evaluate the optimal population risk thresholds for initiating lung cancer screening.

Abstract 3571: UKLS - United Kingdom Lung Cancer Screening Trial

Abstract In the UK 33,500 individuals die from lung cancer each year. The effectiveness of screening pre-symptomatic individuals urgently requires evaluation. The aim of UK Lung cancer Screening trial (UKLS) is to provide data required for an informed decision about the desirability of introducing population screening for lung cancer in the UK. The aim of the UKLS is to demonstrate a 25-30% mortality advantage and cost effectiveness of the screening. The UKLS pilot has started to recruit in 2011. The pilot sites are based at the Liverpool Heath &amp; Chest Hospital, Liverpool and the Papworth Hospital, Cambridgeshire with a second reading site at the Royal Brompton Hospital, London. It will randomise 4,000 high risk patients identified by the Liverpool Lung Project (LLP) Risk Prediction Model. If the pilot trial is successful and funding is available, we plan to randomise a further 28,000 subjects from seven centres in the UK. Individuals are invited to participate if they have at least 5% risk of developing lung cancer over 5 years as predicted by the LLP risk model. The “Wald Single Screen” Design has been chosen for the UKLS pilot trial. Individuals meeting the inclusion/exclusion criteria will be sent invitations to participate and selected on the basis of the LLP risk questionnaire and selected to attend one of the pilot recruitment sites. After consent has been obtained, the participant will undertake spirometry and be asked to provide specimens (of blood, oral and nasal brushings and sputum) and to complete baseline lifestyle, psychosocial, and health economic questionnaire. Participants will be randomised to the study arm and will have a single low dose computed tomography scan (CT); those in the control arm will have usual care (no CT). CT scans will be read both locally and centrally and volumetric analysis will be performed on detected nodules. Both arms will be followed up for lung cancer incidence and mortality. Mortality will be documented after 10 years of follow-up. Modelling has resulted in the development of the UKLS care pathway to manage identified nodules. The aim of the main UKLS trial is to assess whether screening by low dose CT will confer a mortality advantage of 30% and also be cost effective for the UK population. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3571. doi:1538-7445.AM2012-3571

Abstract 1898: The Liverpool Lung Project risk model: An independent validation and clinical utility in primary care

Abstract Background: The results from a recent CT screening trial for lung cancer showed evidence of mortality reduction in the screened arm of the study. The long term success of future National Screening Programme as an early diagnosis tool may be dependent upon identifying populations at sufficient risk of developing the disease such that the benefit:harm ratio of the intervention can be maximised. Risk prediction models can play an important role in risk stratification by providing an estimate of individual's risk of developing a disease at a future time, but require validation in independent populations before they can be successfully generalised. Within the Liverpool Lung Project (LLP), we have developed a risk prediction model for estimating individual's 5-year absolute risk of lung cancer. The model was based on five epidemiological risk factors namely smoking duration, prior diagnosis of pneumonia, family history of lung cancer, occupational exposure to asbestos and prior diagnosis of other cancer. We present here an independent validation and clinical utility of the model using data from two case-control studies and a cohort population from Europe and North America. Method: The 5-year absolute risk of lung cancer was estimated for subjects in the Harvard and European Early Lung Cancer (EUELC) case-control and LLP prospective cohort studies. The model's performance was assessed through its predictive accuracy (discrimination and calibration) and clinical utility. The area under the receiver-operator characteristic curve (AUC) measures the model's discriminatory power while calibration was assessed by comparing the observed and the expected lung cancer cases in the cohort population. The clinical relevance of the model was examined using the decision and relative utility curves. Results: There was an evidence of good discriminations in Harvard (AUC = 0.76) and LLP cohort (c-index = 0.77, 0.82) with no significant difference in discrimination by age, gender and smoking status. In general, the model calibration indicated an underestimation of absolute risk; this showed improvement with high absolute risks. The application of the model was associated with reasonably good clinical utility across the three datasets as demonstrated by its superior ‘net benefit’ against any other alternative strategy. Conclusion: The LLP risk model demonstrates good performance and evidence of clinical usefulness in three independent settings and can serve as an adjunct tool for clinicians or used in primary care for referring patients for early detection and prevention intervention. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1898. doi:10.1158/1538-7445.AM2011-1898

UK Lung Screen (UKLS) nodule management protocol: modelling of a single screen randomised controlled trial of low-dose CT screening for lung cancer

The UK Lung Screen (UKLS) is a randomised controlled trial of the use of low-dose multidetector CT for lung cancer screening. It completed the Health Technology Appraisal (HTA)-funded feasibility stage...

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

The Liverpool Lung Project (LLP) has previously developed a risk model for prediction of 5-year absolute risk of lung cancer based on five epidemiologic risk factors. SEZ6L, a Met430IIe polymorphic variant found on 22q12.2 region, has been previously linked with an increased risk of lung cancer in a case-control population. In this article, we quantify the improvement in risk prediction with addition of SEZ6L to the LLP risk model. Data from 388 LLP subjects genotyped for SEZ6L single-nucleotide polymorphism (SNP) were combined with epidemiologic risk factors. Multivariable conditional logistic regression was used to predict 5-year absolute risk of lung cancer with and without this SNP. The improvement in the model associated with the SEZ6L SNP was assessed through pairwise comparison of the area under the receiver operating characteristic curve and the net reclassification improvements (NRI). The extended model showed better calibration compared with the baseline model. There was a statistically significant modest increase in the area under the receiver operating characteristic curve when SEZ6L was added into the baseline model. The NRI also revealed a statistically significant improvement of around 12% for the extended model; this improvement was better for subjects classified into the two intermediate-risk categories by the baseline model (NRI, 27%). Our results suggest that the addition of SEZ6L improved the performance of the LLP risk model, particularly for subjects whose initial absolute risks were unable to discriminate into "low-risk" or "high-risk" group. This work shows an approach to incorporate genetic biomarkers in risk models for predicting an individual's lung cancer risk.

Abstract 2893: Incorporation of the SEZ6L SNP into The Liverpool Lung Project Risk Prediction Model

Abstract Lung cancer risk prediction models will be essential tools for the identification of high-risk individuals if ongoing lung cancer CT screening trials report positive findings. The Liverpool Lung Project (LLP) risk model estimates an individual's 5-year absolute risk of lung cancer based on five epidemiological risk factors; smoking duration, prior diagnosis of pneumonia or another cancer; family history of lung cancer including age at onset; and occupational exposure to asbestos (Cassidy et al. The LLP Risk Model: an individual risk prediction model for lung cancer. British J Cancer; 98 (2):270-6, 2008). DNA genotyping indicated a role of the SEZ6L, a Met430IIe polymorphic variant found on 22q12.2 region to have increased risk of lung cancer (Gorlov et al. Cancer Research 7: 8406-11, 2007). Therefore, we quantified the improvement in risk prediction attributed to addition of SEZ6L in the LLP risk model. SEZ6L was genotyped in 200 lung cancer patients and 188 controls from the Liverpool Lung Project, and combined with previously identified epidemiological risk factors. Bivariate association between the SNP and the epidemiological risk factors was studied using the Pearson's chi-squared test; multivariable conditional logistic regression modelled individual risk profile, which was combined with local incidence data for prediction of absolute risk of lung cancer in the next 5-year. The improvements in the models associated with the SNP was assessed through a pairwise comparison of the area under the receiver operating characteristic curve (ROC-AUC) and the Net Reclassification Improvements (NRI). A higher proportion of homozygote mutant genotype was observed among patients (10%) compared to controls (3%). No interaction was observed between any of the epidemiological risk factors and the SEZ6L genotype, in combined or separate analysis of patients and controls. The extended model (model including the SNP genotype) showed better calibration compared to the baseline model (model with only epidemiological factors). There was a modest, but statistically significant increase when SEZ6L was incorporated into the risk model, the ROC-AUC increased from 0.72 (0.66-0.77) for the baseline model to 0.75 (0.71-0.80) for the extended model. The NRI also revealed a significant improvement of around 12% for the extended model; this improvement was even better for individuals classified into the two intermediate risk categories by the baseline model (NRI=27%). Our results suggest addition of SEZ6L improved the performance of the LLP risk model particularly for individuals whose initial absolute risks were unable to discriminate them into ‘low’ or ‘high’ risk group. However, given the small number of patients and controls included in this analysis, further validation of the observed improvement is essential. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2893.