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

The LLP risk model: an individual risk prediction model for lung
cancer
A Cassidy1,5, JP Myles2,5, M van Tongeren3, RD Page4, T Liloglou1, SW Duffy2 and JK Field*,1
1Roy Castle Lung Cancer Research Programme, University of Liverpool Cancer Research Centre, Liverpool, L3 9TA, UK; 2Cancer Research UK Centre for
Epidemiology, Mathematics and Statistics Wolfson Institute of Preventive Medicine, London, EC1M 6BQ, UK; 3Institute of Occupational Medicine,
Research Avenue North, Riccarton, Edinburgh, EH14 4AP, UK; 4Department of Thoracic Surgery, The Cardiothoracic Centre, Liverpool, L14 3PE, UK
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.
British Journal of Cancer (2008) 98, 270–276. doi:10.1038/sj.bjc.6604158 www.bjcancer.com
Published online 18 December 2007
& 2008 Cancer Research UK
Keywords: lung carcinoma; risk prediction; model
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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 conse-
quences 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 high-
risk 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 risk models may be further
improved by the addition of epidemiological risk factors
(Freedman et al, 2005; Cassidy et al, 2007a, b). For instance, Spitz
et al (2007) have recently developed a risk prediction model that
incorporates smoking history variables, environmental tobacco
smoke, family history of cancer, prior respiratory disease and
occupational exposures (dust and asbestos).
Here, we report a method to calculate absolute risk of lung
cancer over a defined period, based on data from a case– control
study of lung cancer in Liverpool, the Liverpool Lung Project
(LLP) (Field et al, 2005). Our aim was to provide a model
(hereafter referred to as the LLP risk model) that would estimate
the absolute risk of lung cancer for a given individual. This could
be utilised for primary and secondary prevention, possibly to help
identify those most likely to benefit from CT screening or as an
additional resource for medical decision making. A secondary
objective was to include in the final model only variables that are
readily available to primary care clinicians when patients present
(not necessarily with suspected lung cancer), so that it could be
Received 10 July 2007; revised 19 October 2007; accepted 25
November 2007; published online 18 December 2007
*Correspondence: Professor JK Field, Roy Castle Lung Cancer Research
Program, Division of Surgery & Oncology, University of Liverpool Cancer
Research Centre, University of Liverpool, 200 London Road, Liverpool,
L3 9TA, UK; E-mail: J.K.Field@liv.ac.uk
5 These authors contributed equally to this work
British Journal of Cancer (2008) 98, 270 – 276
& 2008 Cancer Research UK All rights reserved 0007 – 0920/08 $30.00
www.bjcancer.com
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applied in the primary care setting to facilitate the referral of high-
risk individuals.
MATERIALS AND METHODS
To be included in the LLP case–control study, incident cases of
histologically or cytologically confirmed lung cancer were between
20 and 80 years of age. Lung cancer included cancer in any of the
topographic subcategories of code C34 according to the Inter-
national Classification of Diseases, 9th Revision. Participants
were eligible for inclusion provided they were resident within the
Liverpool area and gave informed consent for baseline interviews.
Both cases and controls were ineligible for the study if they had a
previous cancer within 5-years of interview date (excluding
melanoma). Two population controls per case were selected from
registers of general practitioners in Liverpool and matched to lung
cancer cases by year of birth (±2 years) and gender.
A standardised lifestyle questionnaire was used to collect
detailed information on socioeconomic and demographic char-
acteristics, medical history, family history of cancer, history of
tobacco consumption and lifetime occupational history. Extensive
information about tobacco smoking was elicited for all partici-
pants including smoking status, inhalation, type of cigarette
smoked, number of cigarettes smoked per day, age at start and
end. An ever smoker was defined as someone who had smoked
at least 100 cigarettes in their lifetime and a current smoker was
defined as a participant who reported smoking 2 years prior to the
date of the interview. During the interview, all periods of
consumption were defined and counted towards total exposure.
Information on history of cancer among first degree relatives
(i.e. parents, brothers and sisters and biological children) was
recorded, including age of diagnosis, site of cancer and relation to
the participant. All participants were asked if they had ever been
told by a physician that they had a prior non-malignant lung
disease such as asthma, bronchitis, emphysema, pneumonia or
tuberculosis at any age at least 2 years before any diagnosis of
lung cancer (or date of interview for controls). Furthermore, if
participants had ever been diagnosed with a malignant tumour, the
primary site was recorded, and their age at the time of diagnosis.
The methodology to assess occupational exposure was based on
that used by Siemiatycki et al (1991) and has been decribed
previously (Cassidy et al, 2007b). Briefly, asbestos exposure was
determined firstly by collecting detailed information on each job
held using general and specialised questionnaires. Each job was
then assessed by an expert, who indicated their confidence in the
presence of exposure (categorised as possible, probable or certain).
Asbestos exposure was further assessed by frequency of exposure,
defined as the percentage of working time exposed (categorised as
1–5, 5– 30 or 430%), and intensity of exposure (categorised as
low, medium or high). To adhere to the study’s secondary
objective that only variables readily available to clinicians were
included, we characterised asbestos exposure as present if an
individual was exposed for at least 1 year during their working life.
The study protocol was approved by the Liverpool Research
Ethic Committee and all research participants provided written,
informed consent in accordance with the Declaration of Helsinki.
Statistical analyses
Distributions in demographic variables between cases and controls
were evaluated by the w2 test. Differences between cases and
controls in age and self-reported pack-years were tested using the
Student’s t-test. When the data distribution significantly deviated
from normal, the Wilcoxon rank sum test was performed. The risk
model was developed using conditional logistic regression. The
multivariate model was built up in two phases. First, all statistically
significant covariates (Po0.05) in univariate analyses were
included in a multivariate model, and backward stepwise
regression was performed, whereby those factors losing their
significance (P40.05) in the multivariate analysis were dropped.
Those factors not significant in the univariate analyses were
subsequently fitted to the multivariate model, with adjustment for
the remaining significant effects, to detect effects, which are only
seen when the major risk factors are accounted for. Pairwise
interaction tests were conducted between all the risk factors in the
final multivariate model to ensure that they did not modify each
other’s effects. Once the final multivariate model was determined,
the logistic model was converted to absolute risk using the method
described in the Appendix. The classification power for the model
was determined by means of a 10-fold cross-validation procedure
and by calculations of area under the curve (AUC) in receiver
operating characteristic curve analysis. Statistical analyses were
performed using STATA release 9.0 (Stata Corporation, 2005).
RESULTS
Five hundred and seventy-nine incident cases of lung cancer and
1157 population controls were recruited between 1998 and 2005.
Overall, the response rate was 58.3% for cases and 61.5% for
controls. Caucasians represented approximately 99% of both the
cases and the controls. The majority of lung cancer cases in the
study population presented with non small cell lung cancer
(83.2%). Table 1 shows the distribution of study-specific risk
factors between cases and controls. Men constituted the majority
of the cases (61.7%) and, accordingly the controls (61.6%). The
proportion of ever smokers was significantly higher in cases
(95.3%) compared with controls (71%). Significant differences
Table 1 Distribution of study-specific characteristics of lung cancer cases
and healthy controls
Cases Controls
Variable No. % No. % P-value*
Gender
Male 357 61.7 713 61.6 0.99
Female 222 38.3 444 38.4
Mean age (years; mean±s.d.a) 66.4±9.1 66.3±9.0 0.93
Smoking duration
Never 27 4.7 335 29.0 o0.0001
1–19 years 43 7.4 236 20.4
20–39 years 157 27.1 337 29.1
40–59 years 321 55.4 234 20.2
X60 years 31 5.4 15 1.3
Prior diagnosis of pneumoniab
No 361 62.3 989 85.5 0.001
Yes 104 18.0 168 14.5
Occupational exposure to asbestos
No 287 64.9 664 76.3 o0.0001
Yes 155 35.1 206 23.7
Prior diagnosis of malignant tumour
No 509 87.6 1091 94.3 o0.0001
Yes 72 12.4 66 5.7
Family history of lung cancer
No 456 78.8 947 82.0 0.04
Early onset (o60 years) 46 7.9 62 5.4
Late onset (X60 years) 77 13.3 148 12.8
*P values were derived from univariate conditional logistic regression. as.d. bNumbers
do not add up to total due to missing data.
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were observed in a panel of epidemiological risk factors including
history of lung cancer in a first degree relative (P¼ 0.04), prior
diagnosis of pneumonia (P¼ 0.001), occupational exposure to
asbestos (Po0.0001) and prior diagnosis of a malignant tumour
(Po0.0001).
No significant effect of marital status, education or socio-
economic status was observed on lung cancer risk after adjustment
for smoking. There was a significant increase in risk amongst
individuals with a prior diagnosis of pneumonia both before (odds
ratio (OR)¼ 1.62, 95% confidence interval (CI): 1.21– 2.17) and
after adjustment for smoking (OR¼ 1.70, 95% CI: 1.21– 2.39).
Participants with a prior diagnosis of emphysema had a significant
increase in risk before adjustment (OR¼ 2.19, 95% CI: 1.25–3.84)
but not after (OR¼ 1.78, 95% CI: 0.96– 3.30). No effect was present
for prior asthma, bronchitis and tuberculosis. Sex-specific analyses
indicated that the risk of lung cancer remained significantly
elevated for males who had a prior diagnosis of pneumonia
(OR¼ 1.92, 95% CI: 1.25– 2.95), but not for females (OR¼ 1.30,
95% CI: 0.73– 2.29). Women who had a prior diagnosis of
emphysema exhibited a significantly increased lung cancer risk
(OR¼ 2.72, 95% CI: 1.70–3.70), which was not observed in males
(OR¼ 1.30, 95% CI: 0.58– 2.94). Physician-diagnosed prior cancer
was associated with a significantly increased lung cancer risk
(OR¼ 2.18, 95% CI: 1.39–3.42) after adjustment for age, sex and
smoking. The majority of reported previous cancers were cancers
of the skin, which were associated with a 2.2-fold increased lung
cancer risk (95% CI: 1.12– 4.26) followed by cancers of the breast
(OR¼ 4.81, 95% CI: 1.43–16.15). Although there was a significant
trend of increasing risk with numbers of affected relatives, there
was no significant effect of family history (any vs none) of lung
cancer in the study population overall or in late-onset cases,
regardless of the age of affected relatives. There was, however, a
substantial and statistically significant increase in risk where both
the lung cancer case and the affected relative were diagnosed with
lung cancer before the age of 60 years (OR¼ 4.89, 95% CI: 1.47–
16.25). Significantly elevated odds ratios were also observed in
connection with an affected relative diagnosed before age 60
regardless of age-at-onset of the case (OR¼ 2.08, 95% CI: 1.20–
3.59) (Cassidy et al, 2006). Current smokers (OR¼ 13.15, 95% CI:
8.43– 20.50) were at higher risk than ex-smokers (OR¼ 5.72,
95% CI: 3.71– 8.82). Fitting total years of smoking duration as a
continuous covariate, and in 10- and 20-year intervals revealed a
steady increase in lung cancer risk. There was a steady increase in
risk with increasing pack-years and average amount smoked,
although in neither case was as large as that with smoking
duration. A significant dose–response effect was observed for the
daily number of cigarettes (Po0.0001), smoking duration
(Po0.0001) and smoking pack-years (Po0.0001). No association
was found between smoking pipes or cigars and risk of lung
cancer. A significant increase in risk was observed for those who
reported ever exposure to spousal tobacco smoke (OR¼ 1.44, 95%
CI: 1.04–1.98). A significant dose– response effect was observed
with duration of exposure (P¼ 0.01), with the largest increase in
risk in the highest exposed group corresponding to more than 50
years of exposure (OR¼ 2.51, 95% CI: 1.33–4.71). A nonsignificant
excess risk was observed for ever exposure to secondhand smoke
in the workplace (OR¼ 1.29, 95% CI: 0.97–1.73). However, when
analysed by duration of exposure, a dose– response effect was not
observed (P¼ 0.83). There was no evidence of elevated risk of lung
cancer due to exposure to secondhand smoke from social sources.
The high lifetime exposure prevalence to asbestos seen in this
study is manifest as an overall risk of 1.88 (OR¼ 1.88, 95% CI:
1.36– 2.59), which was reduced after adjustment for occupational
confounders to 1.51 (95% CI: 1.02–2.04).
The final multivariate logistic regression model is presented in
Table 2. Significantly increased risks in the multivariate analysis
were observed for family history of lung cancer (with particularly
high risk in those with a relative aged under 60 at diagnosis of lung
cancer) (P¼ 0.01), prior diagnosis of pneumonia (P¼ 0.002), prior
diagnosis of cancer other than lung (P¼ 0.005), occupational
exposure to asbestos (Po0.001) and duration of smoking
(Po0.001). We observed significant interaction between a prior
diagnosis of pneumonia and prior diagnosis of malignant tumour
(P¼ 0.04), which lost significance when adjusted for other risk
factors in the multivariate model (P¼ 0.07). Although various
measures of smoking were significant in the univariate analysis,
only duration of smoking remained significant in the multivariate
model. An association between a prior diagnosis of emphysema
and lung cancer also lost significance in the multivariate model.
Using the methods described in the Appendix, the absolute risk
of lung cancer within a 5-year period was calculated. The diversity
of these estimates can be illustrated by comparing a smoker and
nonsmoker of similar age. First, the absolute risk for a man aged
77 with a family history of lung cancer (relative aged under 60 at
diagnosis), a history of asbestos exposure and no other risk factors
is 3.17% (95% CI, 1.67– 5.95). Secondly, a man with the same risk
factor profile plus a 45-year smoking history has an absolute risk
of 28.68% (95% CI, 15.07 –47.67). In these examples, smoking
Table 2 LLP multivariate risk model, with unadjusted and adjusted odds ratios and 95% confidence intervals corresponding to the model coefficients
Risk factor/category Odds ratioa (95% CI) Odds ratiob (95% CI) P-value Model coefficient
Smoking duration o0.001
Never 1.00 Reference 1.00 Reference 0.000
1–20 years 2.48 (1.47–4.17) 2.16 (1.21–3.85) 0.769
21–40 years 5.81 (3.68–9.18) 4.27 (2.62–6.94) 1.452
41–60 years 19.24 (12.07–30.67) 12.27 (7.41–20.30) 2.507
460 years 41.74 (17.86–97.56) 15.25 (5.71–40.65) 2.724
Prior diagnosis of pneumonia 0.002
No 1.00 Reference 1.00 Reference 0.000
Yes 1.62 (1.21–2.17) 1.83 (1.26–2.64) 0.602
Occupational exposure to asbestos o0.001
No 1.00 Reference 1.00 Reference 0.000
Yes 1.94 (1.46–2.59) 1.89 (1.35–2.62) 0.634
Prior diagnosis of malignant tumour 0.005
No 1.00 Reference 1.00 Reference 0.000
Yes 2.55 (1.76–3.71) 1.96 (1.22–3.14) 0.675
Family history of lung cancer 0.01
No 1.00 Reference 1.00 Reference 0.000
Early-onset (o60 years) 1.54 (1.03–2.29) 2.02 (1.18–3.45) 0.703
Late-onset (X60 years) 1.08 (0.80–1.46) 1.18 (0.79–1.76) 0.168
aOdds ratios derived from univariate conditional logistic regression. bOdds ratios derived from multivariate conditional logistic regression.
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history contributes to an approximate ninefold increase in the
5-year absolute risk of lung cancer.
The LLP risk model estimates also illustrate how a substantial
risk can be conferred by risk factors other than smoking. Consider
a 67-year-old man who has never smoked, but who has a family
history of lung cancer (with the affected relative aged under 60 at
diagnosis), a history of asbestos exposure and a prior diagnosis of
cancer. His estimated risk in the next 5 years is 3.16%. A man of
the same age without these three risk factors would have a 5-year
risk of 0.43%. The population or baseline risk in UK males of this
age (including those with and without risk factors) as calculated
from National Statistics (Office for National Statistics, 2005) is
approximately 1.5%. The above and other examples are presented
in Table 3.
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.
DISCUSSION
By combining case–control data with regional incidence rates, we
have developed a model to project individual 5-year absolute risks
of developing lung cancer. The model has the potential to identify
high-risk individuals by focusing on information that can be
readily obtained in the primary care setting. The LLP risk model
also appears to discriminate between high and low risk, although it
will require rigorous validation in separate populations. As well as
accounting for the three most important risk factors for lung
cancer: age, sex and smoking, the LLP risk model incorporates
other important disease risk factors such as family history of lung
cancer, occupational exposure to asbestos, prior diagnosis of
pneumonia and prior diagnosis of a malignant tumour other than
lung cancer.
Similar to previously well-conducted cohort studies (Doll and
Peto, 1978; Flanders et al, 2003), we have identified the importance
of duration of cigarette smoking beyond the absolute amount of
tobacco smoked. Indeed, contribution of smoking duration to
the model did not change irrespective of whether 10- or 20-year
categories were added. Therefore, the broadest category of
smoking duration (20 years) was chosen for simplicity. Of the
risk factors included in the LLP risk model, a prior diagnosis of
malignant tumour is particularly interesting. First, it is important
to emphasise that lung cancer cases and controls diagnosed with a
malignant tumour (except melanoma) within 5 years of recruit-
ment were excluded from the study. In addition, there were no
significant effect modifications between history of previous
malignancy and other risk factors in terms of their effect on lung
cancer risk. Having had a previous malignancy was associated with
a twofold increase in lung cancer risk. This is not unprecedented
(Kabat, 1993; Mery et al, 2004). Previous studies have reported an
increased risk of lung cancer among women receiving radio-
therapy for breast cancer (Neugut et al, 1993), possibly related to
an interaction between radiotherapy and cigarette smoking
(Prochazka et al, 2002).
Sensitivity and specificity of the LLP risk model compares
favourably with previous lung cancer absolute risk models
developed by Bach et al (2003) and Spitz et al (2007). The Bach
model is based on a person’s age, sex and smoking history, but it is
predictive only for individuals between the age of 50 and 75, who
Table 3 Projected 5-year absolute risks and 95% confidence intervals for combinations of risk factors
Prior diagnosis of
Gender Age
Smoking
duration
(Years)
Family history
of lung cancera Malignancy Pneumonia
Asbestos
exposure
Absolute
risk (%) (95% CI)
Female 65 37 Late-Onset — Yes — 2.37 (1.14–4.86)
68 26 — — — — 1.50 (0.91–2.46)
69 50 — — — — 4.60 (2.76–7.54)
Male 64 42 Late-Onset Yes — — 9.53 (4.52–18.97)
66 53 — — — Yes 8.75 (4.89–15.18)
66 48 — — Yes Yes 14.91 (7.70–26.89)
67 0 Early-Onset Yes — Yes 3.16 (1.42–6.85)
73 59 Late-Onset Yes — Yes 27.09 (13.57–46.78)
77 0 Early-Onset — — Yes 3.17 (1.67–5.95)
77 45 Early-Onset — — Yes 28.68 (15.07–47.67)
aEarly onset¼o60 years at diagnosis; Late onset ¼X60 years at diagnosis.
0.00
0.25
0.50
0.75
1.00
0.00 0.25 0.50 0.75 1.00
1–specificity
Se
ns
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Figure 1 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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smoked 10 –60 cigarettes per day for 25–55 years. The Spitz
model, like the LLP risk model, expands this concept by
incorporating a panel of epidemiological risk factors to more
accurately predict an individual’s absolute risk of developing lung
cancer. One limitation, however, is that cases and controls were
frequency matched based on smoking status, perhaps affecting the
importance of smoking as a risk factor. We believe that the LLP
risk model’s simplicity makes it more directly applicable for use in
the primary care setting. Indeed, we are about to embark on a
feasibility study in a large medical practice, which utilises the LLP
risk model as the first stage in an early detection strategy,
highlighting the potential importance and clinical relevance of our
model.
An obvious strength of this study is that detailed information
on the main risk factors such as active smoking, family history of
lung cancer and occupational exposure was ascertained by closely
supervised, trained interviewers using standardised question-
naires. No proxy interviews were performed. All cases had
histologically or cytologically confirmed primary lung tumours.
In common with all risk prediction models, the LLP risk model has
several limitations. First, the absolute risks estimated for each
combination of risk factors are based on relative risks derived
from a case–control study. In this study, cases were individuals
with newly diagnosed lung cancer identified by surveillance of all
the hospitals responsible for the treatment of lung cancer in
Liverpool and, after matching for age and sex, controls were
selected from population lists that were essentially complete. Thus,
cases and controls were both drawn from the same underlying
population. However, the refusal rates were high, 42% among cases
and 39% among controls. Given that these data did not permit
description of smoking patterns of cases and controls refusing to
participate, one cannot exclude that the lifestyle characteristics of
nonparticipants may differ from the participants leading to an
under- or overestimation of the true relative risks. There is also the
potential that recall and other information biases could influence
our results, as cases and controls were asked to report their
lifestyle habits and behaviours for many years prior to interview.
For these reasons, it is important that independent validation data
be obtained to assess the relative risk features and absolute risk
projections from this model. The limitations notwithstanding,
this study can inform the debate about the best approach to select
individuals at high risk of lung cancer for surveillance or
prevention programmes.
Numerous nonrandomised studies have demonstrated that lung
cancer can be diagnosed at a significantly earlier stage with CT
screening than in current clinical practice (Humphrey et al, 2004).
Strategies combining smoking history as defined by pack-years
have, for the most part, been used as an approach to more
efficiently conduct screening in high-risk smoking cohorts (van
Klaveren et al, 2002), thereby excluding individuals at significantly
elevated risk of lung cancer not adequately reflected using these
factors. For example, a 60-year-old male with long smoking history
would be included while a similar aged never smoker would not.
Using the LLP risk model, the 5-year estimated risk for a 60-year-
old male with 42 years of smoking and a family history in an
affected relative aged 60 years or over is 3.73% (95% CI, 1.85–7.38)
while the risk for a 60-year-old male with no smoking history, a
family history with an affected relative aged under 60 years, a prior
diagnosis of cancer, a prior diagnosis of pneumonia and exposure
to asbestos is 3.52% (95% CI, 1.90–6.45). Both these individuals
have almost identical risk estimates (a one in 28 and one in 27
chance, respectively of lung cancer in the next 5 years) even
though one has never smoked. The LLP risk model potentially
provides a means to identify subgroups of both the smoking and
nonsmoking populations that may benefit most from prevention
or surveillance.
Although some of the absolute risk estimates may seem rather
high, they are consistent with population incidence. In the
penultimate example in Table 3, a 77-year-old male, never smoker
but with an early-onset family history of lung cancer and
occupational exposure to asbestos, has an estimated 5-year risk
of 3.17%. The general population risk in England and Wales for
males aged 75–79 is approximately 2.5%. The modelled risk is
slightly higher due to the two risk factors. It is not four times
higher as one might anticipate from the odds ratios for early-onset
family history and exposure to asbestos, because the general
population on average has some exposure to the risk factors in the
model. The two most extreme examples in Table 3 have very high-
predicted risks of around 30%, mainly but not entirely due to
smoking. It should be noted that individuals with this level of risk
are very rare; that is not more than 0.7% of cases and 0.3% of
controls. This is reflected in the wide area of uncertainty in the
confidence intervals, in both examples, showing a range of
approximately 15– 50% risk.
Although the results suggest that the LLP risk model may be
useful for predicting risk, more work is needed to test the
applicability of the model in diverse populations, including those
from diverse geographic regions. It is clear that some populations,
for example, African Americans, have risk factors other than those
in our model (Abidoye et al, 2007). Marked geographic differences
in incidence rates necessitate separate evaluation of the LLP risk
model in high- and low-risk areas. Moreover, developing separate
models for men and women may allow for the inclusion of
distinctive predictors and/or account for their variable distribution,
thereby increasing predictive ability.
Although several issues concerning lung cancer risk prediction
have been highlighted, we believe that its application as the first
stage in an early detection strategy is a logical evolution in patient
care. The results presented in this paper suggest that the LLP risk
model could predict approximately two-thirds of lung cancer
within 5-years, screening only 30% of the population. If resources
were limited or the intervention carried such adverse effects as to
require a very high-risk population to have a strong benefit–harm
balance, a subset of 10% of the population could be identified
in which 34% of the cases would arise. The effect of restricting
screening to a subpopulation of high-risk individuals will
markedly reduce the cost of screening programmes at the expense
of missing a proportion of lung cancers in individuals below the
cutoff. This ‘high-risk strategy’ aims to help individuals with the
greatest need of, and the potential to benefit from early detection.
Such stratification on the basis of efficiency implies the difficult
decision of where to place the cutoff, but the issue must be
addressed nonetheless. It is likely that identifiable genetic
susceptibility will, in the future, constitute an important factor in
the selection of a more tightly defined risk group. In the meantime,
it is appropriate to use a risk prediction model such as ours to
identify a high-risk group for CT screening, so long as the results
are not used to make inferences about a screening strategy in the
general population (Baker et al, 2004). If confirmed in validation
studies, the LLP risk model could provide individuals and
healthcare professionals with an easily obtained estimate of lung
cancer risk to guide discussions and decisions regarding prevention
and surveillance.
ACKNOWLEDGEMENTS
We thank the individuals who participated in this research and all
the lung cancer clinicians and support staff who made this study
possible. The principle funding for The Liverpool Lung Project is
provided by the Roy Castle Lung Cancer Foundation, UK. JPM is
funded by Cancer Research UK (Grant No C8649/A5367).
The LLP risk model
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