Liverpool Lung Project Research Articles

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.

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

Identification of high-risk individuals will facilitate early diagnosis, reduce overall costs, and also improve the current poor survival from lung cancer. The Liverpool Lung Project prospective cohort of 8,760 participants ages 45 to 79 years, recruited between 1998 and 2008, was followed annually through the hospital episode statistics until January 31, 2013. Cox proportional hazards models were used to identify risk predictors of lung cancer incidence. C-statistic was used to assess the discriminatory accuracy of the models. Models were internally validated using the bootstrap method. During mean follow-up of 8.7 years, 237 participants developed lung cancer. Age [hazard ratio (HR), 1.04; 95% confidence interval (CI), 1.02-1.06], male gender (HR, 1.48; 95% CI, 1.10-1.98), smoking duration (HR, 1.04; 95% CI, 1.03-1.05), chronic obstructive pulmonary disease (HR, 2.43; 95% CI, 1.79-3.30), prior diagnosis of malignant tumor (HR, 2.84; 95% CI, 2.08-3.89), and early onset of family history of lung cancer (HR, 1.68; 95% CI, 1.04-2.72) were associated with the incidence of lung cancer. The LLPi risk model had a good calibration (goodness-of-fit χ(2) 7.58, P = 0.371). The apparent C-statistic was 0.852 (95% CI, 0.831-0.873) and the optimism-corrected bootstrap resampling C-statistic was 0.849 (95% CI, 0.829-0.873). The LLPi risk model may assist in identifying individuals at high risk of developing lung cancer in population-based screening programs.

Lung cancer screening: identifying the high risk cohort.

Low dose computed tomography (LDCT) is a viable screening tool for early lung cancer detection and mortality reduction. In practice, the success of any lung cancer screening programme will depend on successful identification of individuals at high risk in order to maximise the benefit-harm ratio. Risk prediction models incorporating multiple risk factors have been recognised as a method of identifying individuals at high risk of developing lung cancer. Identification of individuals at high risk will facilitate early diagnosis, reduce overall costs and also improve the current poor survival from lung cancer. This review summarises the current methods utilised in identifying high risk cohorts for lung cancer as proposed by the Liverpool Lung Project (LLP) risk model, Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial risk models and the prediction model for lung cancer death using quintiles. In addition, the cost-effectiveness of CT screening and future perspective for selecting high risk individuals is discussed.

Impact of comorbidity on lung cancer mortality - a report from the Liverpool Lung Project

The aim of the present study was to apply the Charlson comorbidity index (CCI) to evaluate the impact of comorbidity on lung cancer mortality in individuals not exhibiting lung cancer at the commencement of follow-up. Data from 9,579 participants without lung cancer were extracted from the Liverpool Lung Project between 1999 and 2010 and linked to the Hospital Episode Statistics database. The occurrence of comorbidities was assessed one year prior to the individual inclusion date. CCI scores were computed and Cox regression analysis was used to evaluate the association between comorbidity and lung cancer mortality using a competitive risk approach. During a median follow-up of 11 years, 1,320/9,579 (13.7%) individuals developed lung cancer, of which 886 (67.1%) succumbed to lung cancer and 875 of the 9,579 individuals (9.1%) succumbed due to other causes. The severity of comorbidity was associated with higher lung cancer-specific mortality; low to moderate comorbidity exhibited a hazard ratio (HR) of 2.86 [95% confidence interval (CI), 1.17–7.02] and severe comorbidity exhibited an HR of 5.16 (95% CI, 2.07–12.89). Furthermore, the CCI score determined that the severity of comorbidity increased the risk of lung cancer-specific mortality. Thus, CCI score is a good predictor of lung cancer-specific mortality and the use of comorbidity burdens in the clinical management of lung cancer is recommended.

11: Application of SureSeq solid tumour panel sequencing to a panel of FFPE samples of non small cell lung cancer from the Liverpool lung project

11: Application of SureSeq solid tumour panel sequencing to a panel of FFPE samples of non small cell lung cancer from the Liverpool lung project

Factors associated with dropout in a lung cancer high-risk cohort - the Liverpool lung project

In long-term longitudinal cohort studies the dropout of participants occurring as a result of withdrawal or lost to follow-up may have greater impact on the effect estimates, if characteristics of participants who drop out and those still active in the study differ significantly. The study aimed to investigate factors associated with dropout in a 5-year follow-up of individuals at 'high‑risk' of lung cancer. We studied 'high‑risk' group of 1,486 individuals aged 45-79 selected from the Liverpool Lung Prospective (LLP) cohort study using a strategy reflecting only age, smoking duration and history of pulmonary disease. Study subjects were recalled annually from 2005-2009 for follow-up collection of specimens and questionnaire data. The dropout rate over the follow-up time was investigated using the Kaplan‑Meier survival curve and the Cox proportional hazard model. Dropout rate was 31% after an average of 3annual visits. Female gender hazard ratio (HR) 1.35 (95%CI 1.09-1.66), current smoking 1.26 (1.02-1.57), prior diagnosis of malignant disease 0.54 (0.36-0.79), home visits 0.67 (0.48-0.94) and systolic blood pressure 1.46 (1.10-1.94) were significantly associated with the dropout rate. Nearly 40% of individuals selected into the 'high‑risk' group by the old criteria were low risk with predicted 5-year absolute risk of less than 2.5%. In conclusion, follow-up of individuals is feasible within the LLP, but may be prone to selective withdrawal attributable to patient's state of health and mobility. We recommend future design of 'high‑risk' follow‑up studies to consider home visit as a useful strategy to encourage continued participation.

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.

MyLungRisk: a user-friendly, web-based calculator for risk assessment of lung cancer based on the validated Liverpool Lung Project risk prediction model

Lung cancer is among the most common and fatal malignancies worldwide, and is often diagnosed at an advanced stage. Self-assessment tools for individualised lung cancer risk could potentially refine our understanding of risk factors, and contribute to disease prevention and earlier detection, thus reducing the disease burden. The originally published algorithm of the validated Liverpool Lung Project risk prediction model for lung cancer was transformed into a web-based questionnaire called MyLungRisk (MLR, www.mylungrisk.org). This provides a user-friendly interface for individuals aged 50–79 years to estimate their risk of developing lung cancer in the next five years. It incorporates epidemiological risk factors such as age, gender, smoking duration, family history of lung cancer, previous history of pneumonia, previous diagnosis of cancer (other than lung cancer) and exposure to asbestos. Upon the completion of the web-based questionnaire, the individual is given an estimated lung cancer risk score for the next five years with an explanation of their risk, as per 100 average people in the population. In addition, advice is provided if the individual completing the questionnaire has any current symptoms (as per UK National Institute for Health and Care guidelines). MLR is a user-friendly interface for lung cancer risk assessment, based on validated scientific modelling. It is the first of its kind to be put on a widely accessible platform to the public.

Abstract 3631: United Kingdom lung cancer screening trial (UKLS): First 88897 approaches.

Abstract The UK Lung Cancer Screening trial (UKLS) aims to inform decision makers about the desirability of introducing lung cancer population screening in the UK, and to assess mortality and cost effectiveness. The UKLS pilot started recruitment in 2011 at the Liverpool Heart &amp; Chest Hospital and Papworth Hospital, Cambridge. 250,000 individuals were approached, of whom 4000 were identified as at high risk of lung cancer by the Liverpool Lung Project (LLP) validated Risk Prediction Model. Participants randomised to the study arm have a single low dose computed tomography (CT) scan; controls have usual care. We now have data from the first 88,897 people approached. 26.8% responded to the first recruitment questionnaire (‘positive responders’), of whom 12% had a LLP risk score &amp;lt;5% risk of lung cancer over 5 years. 65% of these high risk individuals agreed to participate in the trial. 9.7% of the 88,897 approached provided some basic information but did not wish to participate (‘negative responders’), while 63.5% were non-responders. On examining the age distribution, the 50-55 age group were the least likely to participate and, when they did, practically all had a LLP risk score &amp;lt;5%. The UK Index of Multiple Deprivation score (IMD) was available on 88,896 individuals. Higher socioeconomic status correlated positively with response rate, but inversely with LLP risk score. High risk recruits spanned all national quintiles of IMD in proportion to the approached subjects. Within each IMD quintile, no significant differences in response rate or recruitment were seen between the Liverpool and Cambridgeshire populations, although the overall social demographics of the two areas are quite different, with much more deprivation in Liverpool. Among the positive responders (n=23,794), 45% were never smokers, 14% smokers and 40% were ex-smokers (compared with national figures [all ages] of 54%, 21% and 25% respectively; p&amp;lt;0.0001). Only four high risk individuals were identified amongst the never smoking positive responders (0.04%; none of whom attended clinic). 32.7% of current smokers and 18.4% of ex-smokers were designated high risk. Overall, 6.1% of positive responders were deemed high risk and attended a recruitment clinic. Of the smoking and ex-smoking positive responders (3345 and 9520 respectively), 22.1% were high risk, and 11.3% attended clinic and were recruited into the RCT. UKLS is the first CT screening RCT to select high risk people using a validated risk prediction model at a population level. Only 5% of clinic attendees were 60 or younger (compared to 47% of all 88,897 approached); this may have implications for cost effectiveness of CT screening. As expected, very few never smokers were at high risk. The response rate and clinic attendance is better in ex-smokers than in current smokers. An understanding of the factors influencing high risk individuals’ uptake of CT screening will be important in developing future CT screening programmes, if adopted. Citation Format: John K. Field, David Baldwin, Anand Devaraj, Kate Brain, Tim Eisen, John Holemans, Martin Ledson, Nicholas Screaton, Robert C. Rintoul, Ghasem Yadegarfar, Chris Hands, Fiona E. McRonald, Kate Lifford, David Whynes, Keith Kerr, Richard Page, Mahesh Parmar, David Weller, David Whynes, Paula Williamson, David Hansell, Stephen W. Duffy. United Kingdom lung cancer screening trial (UKLS): First 88897 approaches. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3631. doi:10.1158/1538-7445.AM2013-3631

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 ...

Quantitative Methylation Specific PCR (qMSP)

Detection of low copies of methylated DNA targets in clinical specimens is challenging. The quantitative Methylation-Specific PCR (qMSP) assays were designed to specifically amplify bisulphite-converted methylated DNA target sequences in the presence of an excess of unmethylated counterpart sequences. These qMSP assays are real-time PCR assays utilizing, sequence-specific primers and an intervening, also sequence specific, Taqman probe to cover an amplicon of approximately 100 bp in length. The use of Taqman probes bearing a minor groove binding (MGB) allow for the use of shorter probes and therefore facilitate design and significantly increases the analytical specificity of the reaction. In the context of the biomarker discovery program of the Liverpool Lung Project (LLP), ten gene promoters were selected. qMSP assays were developed, validated and used to screen 655 bronchial washings from patients with lung cancer and age/sex matched controls with non malignant lung disease (Nikolaidis et al., 2012).

54 MyLungRisk calculator: a user-friendly, web-based questionnaire for risk prediction of lung cancer based on the validated Liverpool Lung Project model

54 MyLungRisk calculator: a user-friendly, web-based questionnaire for risk prediction of lung cancer based on the validated Liverpool Lung Project model

DNA Methylation Biomarkers Offer Improved Diagnostic Efficiency in Lung Cancer

The exceptional high mortality of lung cancer can be instigated to a high degree by late diagnosis. Despite the plethora of studies on potential molecular biomarkers for lung cancer diagnosis, very few have reached clinical implementation. In this study, we developed a panel of DNA methylation biomarkers and validated their diagnostic efficiency in bronchial washings from a large retrospective cohort. Candidate targets from previous high-throughput approaches were examined by pyrosequencing in an independent set of 48 lung tumor/normal paired. Ten promoters were selected and quantitative methylation-specific PCR (qMSP) assays were developed and used to screen 655 bronchial washings from the Liverpool Lung Project (LLP) subjects divided into training (194 cases and 214 controls) and validation (139 cases and 109 controls) sets. Three statistical models were used to select the optimal panel of markers and to evaluate the performance of the discriminatory algorithms. The final logit regression model incorporated hypermethylation at p16, TERT, WT1, and RASSF1. The performance of this 4-gene methylation signature in the validation set showed 82% sensitivity and 91% specificity. In comparison, cytology alone in this set provided 43% sensitivity at 100% specificity. The diagnostic efficiency of the panel did not show any biases with age, gender, smoking, and the presence of a nonlung neoplasm. However, sensitivity was predictably higher in central (squamous and small cell) than peripheral (adenocarcinomas) tumors, as well as in stage 2 or greater tumors. These findings clearly show the impact of DNA methylation-based assays in the diagnosis of cytologically occult lung neoplasms. A prospective trial is currently imminent in the LLP study to provide data on the enhancement of diagnostic accuracy in a clinical setting, including by additional markers.

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 4133: MicroRNAs for early detection of lung cancer

Abstract Introduction: Early detection of lung cancer by screening of high risk populations (identified by epidemiological and life-style factors) has the potential to save many lives. However, effective screening is reliant on minimally invasive techniques, such as CT screening, bronchioalveolar lavage (BAL) and blood tests, and the identification of suitable biomarkers. CT screening is effective in reducing mortality, but generates a large proportion of indeterminate nodules that must be further characterised. MicroRNAs (miRNA) have great potential as biomarkers due to their tissue-specific and cancer-specific expression patterns. We have identified tumour-specific miRNAs for non-small cell lung cancer (NSCLC), using a combination of screening on TaqMan microRNA TLDA cards and validation with qRTPCR assays, with the aim of utilising these as biomarkers in the early detection setting. Methods: Our sample group consisted of 31 frozen samples from 20 Liverpool Lung Project (LLP) NSCLC patients, including 10 adenocarcinomas (Ad), 10 squamous cell carcinomas (SCC) &amp; matched normal tissue. Two further validation sets consisted of equal numbers of Ad and SCC tumour/normal pairs (124 in total). MiRNA was prepared from tumour and normal specimens using Qiagen MicroRNeasy kits. Reverse transcription and pre-amplification was performed using Applied Biosystems MegaPlex Pools and miRNAs were quantified on a 7900HT Real-Time PCR System with TaqMan Array Human MiRNA Card Set v3.0 (covering 754 human miRNAs). Ct values were exported using SDS v2.3 data and RQ Manager software and further analysed in Bioconductor. Validation qRTPCR was performed with individual miRNA assays, following reverse transcription with MegaPlex pools. Results: When Benjamin-Hoechst-adjusted-p value &amp;lt;0.05 was used as a cut-off, of the 754 miRNA targets, 68 miRNAs were upregulated and 8 were downregulated with &amp;gt;4.0 fold-change in the cancer group. A subset of 22 miRNAs including miR-34a, miR-96, let-7g and miR-183 was identified with the greatest expression in tumours. Differential expression of all 22 miRNAs was confirmed in an independent set of 24 tumour/normal pairs. Using these 22 validated miRNAs we performed discriminative modelling and identified a model based on just 8 markers that gave a specificity of 100% and a sensitivity of 98%. This panel was validated, with 97% specificity and 91% sensitivity, in a 2nd independent sample set containing 48 tumours and paired normal samples. Conclusion: A number of miRNAs was identified that showed good discriminatory power individually, but greatest sensitivity and specificity when combined as an 8 member panel. The lung cancer specific miRNAs we have identified provide a potential source of early detection biomarkers. Their applicability to minimally-invasive samples is being evaluated in a range of samples including plasma and bronchial lavage. 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 4133. doi:1538-7445.AM2012-4133

Abstract IA21: Risk prediction models for lung cancer

Abstract IA21: Risk prediction models for lung cancer

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

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

Abstract SS03-01: Follow-up on the National Lung Screening Trial: The next steps

Abstract The publication of the NLST randomized controlled trial from the USA has provided evidence that CT screening reduces mortality from lung cancer by more than 20.3 percent and the all cause mortality was 6.9 percent lower in those screened with low-dose CT, relative to those screened with chest radiography1. The NLST is the first randomized controlled trial for lung cancer screening to show a significant mortality benefit. These findings may potentially lead to one of the most important developments in lung cancer care. However there remain important questions about the applicability of the results globally and the clinical effectiveness of this intervention including its feasibility and cost-effectiveness. A number of randomized trials have recently reported their preliminary findings, which includes the ItaLung and Dante Trials in Italy, the French randomized pilot study, Depiscan, comparing LDCT and chest radiography, and the Danish lung cancer screening trial, however, the largest of the European trials is the DutcheBelgian NELSON (NEderlands-Leuvens longkanker Screenings ONderzoek). Recently the UK have launched the UKLS CT screening trial. One of the major attributes of the European CT trial groups, is their readiness to share data in the coming years, as agreed in the Liverpool Statement 20052. Many of the European studies have employed conventional methods to analyses the images, but the NELSON trial uses volumetric analysis to classify both the initial characteristics and growth behavior of nodules3. The UKLS trial design team used the data available from NELSON to develop the nodule management protocol which relies heavily on this technology4. The UKLS pilot trial will randomize 4000 individuals with at least a 5% risk of developing lung cancer over 5 years as predicted by the Liverpool Lung Project Risk Prediction Model5. If the pilot shows that a trial is feasible, a further submission will be made to undertake an RCT, randomizing 28 000 individuals from seven centers in the UK with the same 5% 5-year risk of lung cancer. The ‘Wald Single Screen Design’4 has been chosen for the UKLS RCT. The aim of the main UKLS trial is to establish whether CT screening can reduce lung cancer deaths by at least 30% and to determine the cost effectiveness of CT screening in the UK. The IASLC set up a Task Force6 to respond to the NLST press release in October 2010. The NLST is the first randomized clinical trial to show a significant decline in lung cancer deaths, however, there are a number of opportunities to improve further this approach. There is a need to introduce quality control measures to ensure the quality of the screening management. For example, the follow-up of patients with indeterminate nodules is critically important and should be done by a team experienced in evaluation of such nodules to ensure safe, economical screening care. The Position Statement from the UKLS team supported these recommendations and also indicated that additional research will also define risk profiles of individuals who would benefit most from screening7. Screening can be improved through ongoing research, which is essential, as the resolution of spiral CT imaging continues to improve, and this can drive further progress with a safer and more effective surgical management approach. The advice of the IASLC Task Force at this time is that, it is appropriate for heavy smokers ages 55 to 74 to discuss relevant lung cancer screening information with their physicians to assist them in deciding whether to undergo spiral CT screening. In addition it is very important that smoking cessation is linked with all future CT screening programs. There remain unresolved issues with respect to CT screening for lung cancer. These include its feasibility, psychosocial and cost-effectiveness, harmonization of CT acquisition techniques, management of suspicious screening findings, the choice of screening frequency and the selection of an appropriate risk group for the intervention.