Risk-based lung cancer screening performance in a universal healthcare setting

Globally, lung cancer is the leading cause of cancer death. Previous trials demonstrated that low-dose computed tomography lung cancer screening of high-risk individuals can reduce lung cancer mortality by 20% or more. Lung cancer screening has been approved by major guidelines in the United States, and over 4,000 sites offer screening. Adoption of lung screening outside the United States has, until recently, been slow. Between June 2017 and May 2019, the Ontario Lung Cancer Screening Pilot successfully recruited 7,768 individuals at high risk identified by using the PLCOm2012noRace lung cancer risk prediction model. In total, 4,451 participants were successfully screened, retained and provided with high-quality follow-up, including appropriate treatment. In the Ontario Lung Cancer Screening Pilot, the lung cancer detection rate and the proportion of early-stage cancers were 2.4% and 79.2%, respectively; serious harms were infrequent; and sensitivity to detect lung cancers was 95.3% or more. With abnormal scans defined as ones leading to diagnostic investigation, specificity was 95.5% (positive predictive value, 35.1%), and adherence to annual recall and early surveillance scans and clinical investigations were high (>85%). The Ontario Lung Cancer Screening Pilot provides insights into how a risk-based organized lung screening program can be implemented in a large, diverse, populous geographic area within a universal healthcare system.

Risk model-based management for second primary lung cancer among lung cancer survivors through a validated risk prediction model

BACKGROUND

Recent therapeutic advances and screening technologies have improved survival among patients with lung cancer, who are now at high risk of developing second primary lung cancer (SPLC). Recently, an SPLC risk-prediction model (called SPLC-RAT) was developed and validated using data from population-based epidemiological cohorts and clinical trials, but real-world validation has been lacking. The predictive performance of SPLC-RAT was evaluated in a hospital-based cohort of lung cancer survivors.

METHODS

The authors analyzed data from 8448 ever-smoking patients diagnosed with initial primary lung cancer (IPLC) in 1997-2006 at Mayo Clinic, with each patient followed for SPLC through 2018. The predictive performance of SPLC-RAT and further explored the potential of improving SPLC detection through risk model-based surveillance using SPLC-RAT versus existing clinical surveillance guidelines.

RESULTS

Of 8448 IPLC patients, 483 (5.7%) developed SPLC over 26,470 person-years. The application of SPLC-RAT showed high discrimination area under the receiver operating characteristics curve: 0.81). When the cohort was stratified by a 10-year risk threshold of ≥5.6% (i.e., 80th percentile from the SPLC-RAT development cohort), the observed SPLC incidence was significantly elevated in the high-risk versus low-risk subgroup (13.1% vs. 1.1%, p < 1 × 10-6 ). The risk-based surveillance through SPLC-RAT (≥5.6% threshold) outperformed the National Comprehensive Cancer Network guidelines with higher sensitivity (86.4% vs. 79.4%) and specificity (38.9% vs. 30.4%) and required 20% fewer computed tomography follow-ups needed to detect one SPLC (162 vs. 202).

CONCLUSION

In a large, hospital-based cohort, the authors validated the predictive performance of SPLC-RAT in identifying high-risk survivors of SPLC and showed its potential to improve SPLC detection through risk-based surveillance.

PLAIN LANGUAGE SUMMARY

Lung cancer survivors have a high risk of developing second primary lung cancer (SPLC). However, no evidence-based guidelines for SPLC surveillance are available for lung cancer survivors. Recently, an SPLC risk-prediction model was developed and validated using data from population-based epidemiological cohorts and clinical trials, but real-world validation has been lacking. Using a large, real-world cohort of lung cancer survivors, we showed the high predictive accuracy and risk-stratification ability of the SPLC risk-prediction model. Furthermore, we demonstrated the potential to enhance efficiency in detecting SPLC using risk model-based surveillance strategies compared to the existing consensus-based clinical guidelines, including the National Comprehensive Cancer Network.

Risk prediction models for lung cancer in people who have never smoked: a protocol of a systematic review

BACKGROUND

Lung cancer is one of the most commonly diagnosed cancers and the leading cause of cancer-related death worldwide. Although smoking is the primary cause of the cancer, lung cancer is also commonly diagnosed in people who have never smoked. Currently, the proportion of people who have never smoked diagnosed with lung cancer is increasing. Despite this alarming trend, this population is ineligible for lung screening. With the increasing proportion of people who have never smoked among lung cancer cases, there is a pressing need to develop prediction models to identify high-risk people who have never smoked and include them in lung cancer screening programs. Thus, our systematic review is intended to provide a comprehensive summary of the evidence on existing risk prediction models for lung cancer in people who have never smoked.

METHODS

Electronic searches will be conducted in MEDLINE (Ovid), Embase (Ovid), Web of Science Core Collection (Clarivate Analytics), Scopus, and Europe PMC and Open-Access Theses and Dissertations databases. Two reviewers will independently perform title and abstract screening, full-text review, and data extraction using the Covidence review platform. Data extraction will be performed based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS). The risk of bias will be evaluated independently by two reviewers using the Prediction model Risk-of-Bias Assessment Tool (PROBAST) tool. If a sufficient number of studies are identified to have externally validated the same prediction model, we will combine model performance measures to evaluate the model's average predictive accuracy (e.g., calibration, discrimination) across diverse settings and populations and explore sources of heterogeneity.

DISCUSSION

The results of the review will identify risk prediction models for lung cancer in people who have never smoked. These will be useful for researchers planning to develop novel prediction models, and for clinical practitioners and policy makers seeking guidance for clinical decision-making and the formulation of future lung cancer screening strategies for people who have never smoked.

SYSTEMATIC REVIEW REGISTRATION

This protocol has been registered in PROSPERO under the registration number CRD42023483824.

Risk Model-Based Lung Cancer Screening and Racial and Ethnic Disparities in the US

Importance

The revised 2021 US Preventive Services Task Force (USPSTF) guidelines for lung cancer screening have been shown to reduce disparities in screening eligibility and performance between African American and White individuals vs the 2013 guidelines. However, potential disparities across other racial and ethnic groups in the US remain unknown. Risk model-based screening may reduce racial and ethnic disparities and improve screening performance, but neither validation of key risk prediction models nor their screening performance has been examined by race and ethnicity.

Objective

To validate and recalibrate the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 (PLCOm2012) model-a well-established risk prediction model based on a predominantly White population-across races and ethnicities in the US and evaluate racial and ethnic disparities and screening performance through risk-based screening using PLCOm2012 vs the USPSTF 2021 criteria.

Design, Setting, and Participants

In a population-based cohort design, the Multiethnic Cohort Study enrolled participants in 1993-1996, followed up through December 31, 2018. Data analysis was conducted from April 1, 2022, to May 19. 2023. A total of 105 261 adults with a smoking history were included.

Exposures

The 6-year lung cancer risk was calculated through recalibrated PLCOm2012 (ie, PLCOm2012-Update) and screening eligibility based on a 6-year risk threshold greater than or equal to 1.3%, yielding similar eligibility as the USPSTF 2021 guidelines.

Outcomes

Predictive accuracy, screening eligibility-incidence (E-I) ratio (ie, ratio of the number of eligible to incident cases), and screening performance (sensitivity, specificity, and number needed to screen to detect 1 lung cancer).

Results

Of 105 261 participants (60 011 [57.0%] men; mean [SD] age, 59.8 [8.7] years), consisting of 19 258 (18.3%) African American, 27 227 (25.9%) Japanese American, 21 383 (20.3%) Latino, 8368 (7.9%) Native Hawaiian/Other Pacific Islander, and 29 025 (27.6%) White individuals, 1464 (1.4%) developed lung cancer within 6 years from enrollment. The PLCOm2012-Update showed good predictive accuracy across races and ethnicities (area under the curve, 0.72-0.82). The USPSTF 2021 criteria yielded a large disparity among African American individuals, whose E-I ratio was 53% lower vs White individuals (E-I ratio: 9.5 vs 20.3; P < .001). Under the risk-based screening (PLCOm2012-Update 6-year risk ≥1.3%), the disparity between African American and White individuals was substantially reduced (E-I ratio: 15.9 vs 18.4; P < .001), with minimal disparities observed in persons of other minoritized groups, including Japanese American, Latino, and Native Hawaiian/Other Pacific Islander. Risk-based screening yielded superior overall and race and ethnicity-specific performance to the USPSTF 2021 criteria, with higher overall sensitivity (67.2% vs 57.7%) and lower number needed to screen (26 vs 30) at similar specificity (76.6%).

Conclusions

The findings of this cohort study suggest that risk-based lung cancer screening can reduce racial and ethnic disparities and improve screening performance across races and ethnicities vs the USPSTF 2021 criteria.

Integrating Smoking Cessation Into Low-Dose Computed Tomography Lung Cancer Screening: Results of the Ontario, Canada Pilot

INTRODUCTION

Low-dose computed tomography screening in high-risk individuals reduces lung cancer mortality. To inform the implementation of a provincial lung cancer screening program, Ontario Health undertook a Pilot study, which integrated smoking cessation (SC).

METHODS

The impact of integrating SC into the Pilot was assessed by the following: rate of acceptance of a SC referral; proportion of individuals who were currently smoking cigarettes and attended a SC session; the quit rate at 1 year; change in the number of quit attempts; change in Heaviness of Smoking Index; and relapse rate in those who previously smoked.

RESULTS

A total of 7768 individuals were recruited predominantly through primary care physician referral. Of these, 4463 were currently smoking and were risk assessed and referred to SC services, irrespective of screening eligibility: 3114 (69.8%) accepted referral to an in-hospital SC program, 431 (9.7%) to telephone quit lines, and 50 (1.1%) to other programs. In addition, 4.4% reported no intention to quit and 8.5% were not interested in participating in a SC program. Of the 3063 screen-eligible individuals who were smoking at baseline low-dose computed tomography scan, 2736 (89.3%) attended in-hospital SC counseling. The quit rate at 1 year was 15.5% (95% confidence interval: 13.4%-17.7%; range: 10.5%-20.0%). Improvements were also observed in Heaviness of Smoking Index (p < 0.0001), number of cigarettes smoked per day (p < 0.0001), time to first cigarette (p < 0.0001), and number of quit attempts (p < 0.001). Of those who reported having quit within the previous 6 months, 6.3% had resumed smoking at 1 year. Furthermore, 92.7% of the respondents reported satisfaction with the hospital-based SC program.

CONCLUSIONS

On the basis of these observations, the Ontario Lung Screening Program continues to recruit through primary care providers, to assess risk for eligibility using trained navigators, and to use an opt-out approach to referral for cessation services. In addition, initial in-hospital SC support and intensive follow-on cessation interventions will be provided to the extent possible.

Evaluation of the accuracy of the PLCOm2012 6-year lung cancer risk prediction model among smokers in the CARTaGENE population-based cohort

BACKGROUND

The PLCO_m2012 prediction tool for risk of lung cancer has been proposed for a pilot program for lung cancer screening in Quebec, but has not been validated in this population. We sought to validate PLCO_m2012 in a cohort of Quebec residents, and to determine the hypothetical performance of different screening strategies.

METHODS

We included smokers without a history of lung cancer from the population-based CARTaGENE cohort. To assess PLCO_m2012 calibration and discrimination, we determined the ratio of expected to observed number of cases, as well as the sensitivity, specificity and positive predictive values of different risk thresholds. To assess the performance of screening strategies if applied between Jan. 1, 1998, and Dec. 31, 2015, we tested different thresholds of the PLCO_m2012 detection of lung cancer over 6 years (1.51%, 1.70% and 2.00%), the criteria of Quebec's pilot program (for people aged 55-74 yr and 50-74 yr) and recommendations from 2021 United States and 2016 Canada guidelines. We assessed shift and serial scenarios of screening, whereby eligibility was assessed annually or every 6 years, respectively.

RESULTS

Among 11 652 participants, 176 (1.51%) lung cancers were diagnosed in 6 years. The PLCO_m2012 tool underestimated the number of cases (expected-to-observed ratio 0.68, 95% confidence interval [CI] 0.59-0.79), but the discrimination was good (C-statistic 0.727, 95% CI 0.679-0.770). From a threshold of 1.51% to 2.00%, sensitivities ranged from 52.3% (95% CI 44.6%-59.8%) to 44.9% (95% CI 37.4%-52.6%), specificities ranged from 81.6% (95% CI 80.8%-82.3%) to 87.7% (95% CI 87.0%-88.3%) and positive predictive values ranged from 4.2% (95% CI 3.4%-5.1%) to 5.3% (95% CI 4.2%-6.5%). Overall, 8938 participants had sufficient data to test performance of screening strategies. If eligibility was estimated annually, Quebec pilot criteria would have detected fewer cancers than PLCO_m2012 at a 2.00% threshold (48.3% v. 50.2%) for a similar number of scans per detected cancer. If eligibility was estimated every 6 years, up to 26 fewer lung cancers would have been detected; however, this scenario led to higher positive predictive values (highest for PLCO_m2012 with a 2.00% threshold at 6.0%, 95% CI 4.8%-7.3%).

INTERPRETATION

In a cohort of Quebec smokers, the PLCO_m2012 risk prediction tool had good discrimination in detecting lung cancer, but it may be helpful to adjust the intercept to improve calibration. The implementation of risk prediction models in some of the provinces of Canada should be done with caution.

Economic impact of using risk models for eligibility selection to the International lung screening Trial

OBJECTIVES

Using risk models as eligibility criteria for lung screening can reduce race and sex-based disparities. We used data from the International Lung Screening Trial(ILST; NCT02871856) to compare the economic impact of using the PLCOm2012 risk model or the US Preventative Services' categorical age-smoking history-based criteria (USPSTF-2013).

MATERIALS AND METHODS

The cost-effectiveness of using PLCOm2012 versus USPSTF-2013 was evaluated with a decision analytic model based on the ILST and other screening trials. The primary outcomes were costs in 2020 International Dollars ($), quality-adjusted life-years (QALY) and incremental net benefit (INB, in $ per QALY). Secondary outcomes were selection characteristics and cancer detection rates (CDR).

RESULTS

Compared with the USPSTF-2013 criteria, the PLCOm2012 risk model resulted in $355 of cost savings per 0.2 QALYs gained (INB=$4294 at a willingness-to-pay threshold of $20 000/QALY (95 %CI: $4205-$4383). Using the risk model was more cost-effective in females at both a 1.5 % and 1.7 % 6-year risk threshold (INB=$6616 and $6112, respectively), compared with males ($5221 and $695). The PLCOm2012 model selected more females, more individuals with fewer years of formal education, and more people with other respiratory illnesses in the ILST. The CDR with the risk model was higher in females compared with the USPSTF-2013 criteria (Risk Ratio = 7.67, 95 % CI: 1.87-31.38).

CONCLUSION

The PLCOm2012 model saved costs, increased QALYs and mitigated socioeconomic and sex-based disparities in access to screening.

Design and methodological considerations for biomarker discovery and validation in the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Program

The Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) program is an NCI-funded initiative with an objective to develop tools to optimize low-dose CT (LDCT) lung cancer screening. Here, we describe the rationale and design for the Risk Biomarker and Nodule Malignancy projects within INTEGRAL. The overarching goal of these projects is to systematically investigate circulating protein markers to include on a panel for use (i) pre-LDCT, to identify people likely to benefit from screening, and (ii) post-LDCT, to differentiate benign versus malignant nodules. To identify informative proteins, the Risk Biomarker project measured 1161 proteins in a nested-case control study within 2 prospective cohorts (n = 252 lung cancer cases and 252 controls) and replicated associations for a subset of proteins in 4 cohorts (n = 479 cases and 479 controls). Eligible participants had a current or former history of smoking and cases were diagnosed up to 3 years following blood draw. The Nodule Malignancy project measured 1078 proteins among participants with a heavy smoking history within four LDCT screening studies (n = 425 cases diagnosed up to 5 years following blood draw, 430 benign-nodule controls, and 398 nodule-free controls). The INTEGRAL panel will enable absolute quantification of 21 proteins. We will evaluate its performance in the Risk Biomarker project using a case-cohort study including 14 cohorts (n = 1696 cases and 2926 subcohort representatives), and in the Nodule Malignancy project within five LDCT screening studies (n = 675 cases, 680 benign-nodule controls, and 648 nodule-free controls). Future progress to advance lung cancer early detection biomarkers will require carefully designed validation, translational, and comparative studies.

Lung Cancer Absolute Risk Models for Mortality in an Asian Population using the China Kadoorie Biobank

BACKGROUND

Lung cancer is the leading cause of cancer mortality globally. Early detection through risk-based screening can markedly improve prognosis. However, most risk models were developed in North American cohorts of smokers, whereas less is known about risk profiles for never-smokers, which represent a growing proportion of lung cancers, particularly in Asian populations.

METHODS

Based on the China Kadoorie Biobank, a population-based prospective cohort of 512 639 adults with up to 12 years of follow-up, we built Asian Lung Cancer Absolute Risk Models (ALARM) for lung cancer mortality using flexible parametric survival models, separately for never and ever-smokers, accounting for competing risks of mortality. Model performance was evaluated in a 25% hold-out test set using the time-dependent area under the curve and by comparing model-predicted and observed risks for calibration.

RESULTS

Predictors assessed in the never-smoker lung cancer mortality model were demographics, body mass index, lung function, history of emphysema or bronchitis, personal or family history of cancer, passive smoking, and indoor air pollution. The ever-smoker model additionally assessed smoking history. The 5-year areas under the curve in the test set were 0.77 (95% confidence interval = 0.73 to 0.80) and 0.81 (95% confidence interval = 0.79 to 0.84) for ALARM-never-smokers and ALARM-ever smokers, respectively. The maximum 5-year risk for never and ever-smokers was 2.6% and 12.7%, respectively.

CONCLUSIONS

This study is among the first to develop risk models specifically for Asian populations separately for never and ever-smokers. Our models accurately identify Asians at high risk of lung cancer death and may identify those with risks exceeding common eligibility thresholds who may benefit from screening.

Delivery of Cancer Care in Ontario, Canada, During the First Year of the COVID-19 Pandemic

IMPORTANCE

The COVID-19 pandemic has impacted cancer systems worldwide. Quantifying the changes is critical to informing the delivery of care while the pandemic continues, as well as for system recovery and future pandemic planning.

OBJECTIVE

To quantify change in the delivery of cancer services across the continuum of care during the COVID-19 pandemic.

DESIGN, SETTING, AND PARTICIPANTS

This population-based cohort study assessed cancer screening, imaging, diagnostic, treatment, and psychosocial oncological care services delivered in pediatric and adult populations in Ontario, Canada (population 14.7 million), from April 1, 2019, to March 1, 2021. Data were analyzed from May 1 to July 31, 2021.

EXPOSURES

COVID-19 pandemic.

MAIN OUTCOMES AND MEASURES

Cancer service volumes from the first year of the COVID-19 pandemic, defined as April 1, 2020, to March 31, 2021, were compared with volumes during a prepandemic period of April 1, 2019, to March 31, 2020.

RESULTS

During the first year of the pandemic, there were a total of 4 476 693 cancer care services, compared with 5 644 105 services in the year prior, a difference of 20.7% fewer services of cancer care, representing a potential backlog of 1 167 412 cancer services. While there were less pronounced changes in systemic treatments, emergency and urgent imaging examinations (eg, 1.9% more parenteral systemic treatments) and surgical procedures (eg, 65% more urgent surgical procedures), major reductions were observed for most services beginning in March 2020. Compared with the year prior, during the first pandemic year, cancer screenings were reduced by 42.4% (-1 016 181 screening tests), cancer treatment surgical procedures by 14.1% (-8020 procedures), and radiation treatment visits by 21.0% (-141 629 visits). Biopsies to confirm cancer decreased by up to 41.2% and surgical cancer resections by up to 27.8% during the first pandemic wave. New consultation volumes also decreased, such as for systemic treatment (-8.2%) and radiation treatment (-9.3%). The use of virtual cancer care increased for systemic treatment and radiation treatment and psychosocial oncological care visits, increasing from 0% to 20% of total new or follow-up visits prior to the pandemic up to 78% of total visits in the first pandemic year.

CONCLUSIONS AND RELEVANCE

In this population-based cohort study in Ontario, Canada, large reductions in cancer service volumes were observed. While most services recovered to prepandemic levels at the end of the first pandemic year, a substantial care deficit likely accrued. The anticipated downstream morbidity and mortality associated with this deficit underscore the urgent need to address the backlog and recover cancer care and warrant further study.

Blood-Based Biomarker Panel for Personalized Lung Cancer Risk Assessment

PURPOSE

To investigate whether a panel of circulating protein biomarkers would improve risk assessment for lung cancer screening in combination with a risk model on the basis of participant characteristics.

METHODS

A blinded validation study was performed using prostate lung colorectal ovarian (PLCO) Cancer Screening Trial data and biospecimens to evaluate the performance of a four-marker protein panel (4MP) consisting of the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19 fragment in combination with a lung cancer risk prediction model (PLCO_m2012) compared with current US Preventive Services Task Force (USPSTF) screening criteria. The 4MP was assayed in 1,299 sera collected preceding lung cancer diagnosis and 8,709 noncase sera.

RESULTS

The 4MP alone yielded an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77 to 0.82) for case sera collected within 1-year preceding diagnosis and 0.74 (95% CI, 0.72 to 0.76) among the entire specimen set. The combined 4MP + PLCO_m2012 model yielded an area under the receiver operating characteristic curve of 0.85 (95% CI, 0.82 to 0.88) for case sera collected within 1 year preceding diagnosis. The benefit of the 4MP in the combined model resulted from improvement in sensitivity at high specificity. Compared with the USPSTF2021 criteria, the combined 4MP + PLCO_m2012 model exhibited statistically significant improvements in sensitivity and specificity. Among PLCO participants with ≥ 10 smoking pack-years, the 4MP + PLCO_m2012 model would have identified for annual screening 9.2% more lung cancer cases and would have reduced referral by 13.7% among noncases compared with USPSTF2021 criteria.

CONCLUSION

A blood-based biomarker panel in combination with PLCO_m2012 significantly improves lung cancer risk assessment for lung cancer screening.

Lung cancer screening use and implications of varying eligibility criteria by race and ethnicity: 2019 Behavioral Risk Factor Surveillance System data

BACKGROUND

In 2021, the US Preventive Services Task Force (USPSTF) expanded the eligibility criteria for low-dose computed tomographic lung cancer screening (LCS) to reduce racial disparities that resulted from the 2013 USPSTF criteria. The annual LCS rate has risen slowly since the 2013 USPSTF screening recommendations. Using the 2019 Behavioral Risk Factor Surveillance System (BRFSS), this study 1) describes LCS use in 2019, 2) compares the percent eligible for LCS using the 2013 versus 2021 USPSTF criteria, and 3) determines the percent eligible using the more detailed PLCOm2012_Race3L risk-prediction model.

METHODS

The analysis included 41,544 individuals with a smoking history from states participating in the BRFSS LCS module who were ≥50 years old.

RESULTS

Using the 2013 USPSTF criteria, 20.7% (95% confidence interval [CI], 19.0-22.4) of eligible individuals underwent LCS in 2019. The 2013 USPSTF criteria was compared to the 2021 USPSTF criteria, and the overall proportion eligible increased from 21.0% (95% CI, 20.2-21.8) to 34.7% (95 CI, 33.8-35.6). Applying the 2021 criteria, the proportion eligible by race was 35.8% (95% CI, 34.8-36.7) among Whites, 28.5% (95% CI, 25.2-31.9) among Blacks, and 18.0% (95% CI, 12.4-23.7) among Hispanics. Using the 1.0% 6-year threshold that is comparable to the 2021 USPSTF criteria, the PLCOm2012_Race3L model selected more individuals overall and by race.

CONCLUSIONS

Using data from 20 states and using multiple imputation, higher LCS rates have been reported compared to prior BRFSS data. The 2021 expanded criteria will result in a greater number of screen-eligible individuals. However, risk-based screening that uses additional risk factors may be more inclusive overall and across subgroups.

LAY SUMMARY

In 2013, lung cancer screening (lung screening) was recommended for high risk individuals. The annual rate of lung screening has risen slowly, particularly among Black individuals. In part, this racial disparity resulted in expanded 2021 criteria. Survey data was used to: 1) describe the number of people screened in 2019, 2) compare the percent eligible for lung screening using the 2013 versus 2021 guidelines, and 3) determine the percent eligible using more detailed criteria. Lung screening rates increased in 2019, and the 2021 criteria will result in more individuals eligible for screening. Using additional criteria may identify more individuals eligible for lung screening.

Lung Cancer Screening Considerations During Respiratory Infection Outbreaks, Epidemics or Pandemics: An International Association for the Study of Lung Cancer Early Detection and Screening Committee Report

After the results of two large, randomized trials, the global implementation of lung cancer screening is of utmost importance. However, coronavirus disease 2019 infections occurring at heightened levels during the current global pandemic and also other respiratory infections can influence scan interpretation and screening safety and uptake. Several respiratory infections can lead to lesions that mimic malignant nodules and other imaging changes suggesting malignancy, leading to an increased level of follow-up procedures or even invasive diagnostic procedures. In periods of increased rates of respiratory infections from severe acute respiratory syndrome coronavirus 2 and others, there is also a risk of transmission of these infections to the health care providers, the screenees, and patients. This became evident with the severe acute respiratory syndrome coronavirus 2 pandemic that led to a temporary global stoppage of lung cancer and other cancer screening programs. Data on the optimal management of these situations are not available. The pandemic is still ongoing and further periods of increased respiratory infections will come, in which practical guidance would be helpful. The aims of this report were: (1) to summarize the data available for possible false-positive results owing to respiratory infections; (2) to evaluate the safety concerns for screening during times of increased respiratory infections, especially during a regional outbreak or an epidemic or pandemic event; (3) to provide guidance on these situations; and (4) to stimulate research and discussions about these scenarios.

OUP accepted manuscript

Background

Methods

Results

Conclusions

USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (International Lung Screening Trial): interim analysis of a prospective cohort study

BACKGROUND

Lung cancer is a major health problem. CT lung screening can reduce lung cancer mortality through early diagnosis by at least 20%. Screening high-risk individuals is most effective. Retrospective analyses suggest that identifying individuals for screening by accurate prediction models is more efficient than using categorical age-smoking criteria, such as the US Preventive Services Task Force (USPSTF) criteria. This study prospectively compared the effectiveness of the USPSTF2013 and PLCOm2012 model eligibility criteria.

METHODS

In this prospective cohort study, participants from the International Lung Screening Trial (ILST), aged 55-80 years, who were current or former smokers (ie, had ≥30 pack-years smoking history or ≤15 quit-years since last permanently quitting), and who met USPSTF2013 criteria or a PLCOm2012 risk threshold of at least 1·51% within 6 years of screening, were recruited from nine screening sites in Canada, Australia, Hong Kong, and the UK. After enrolment, patients were assessed with the USPSTF2013 criteria and the PLCOm2012 risk model with a threshold of at least 1·70% at 6 years. Data were collected locally and centralised. Main outcomes were the comparison of lung cancer detection rates and cumulative life expectancies in patients with lung cancer between USPSTF2013 criteria and the PLCOm2012 model. In this Article, we present data from an interim analysis. To estimate the incidence of lung cancers in individuals who were USPSTF2013-negative and had PLCOm2012 of less than 1·51% at 6 years, ever-smokers in the Prostate Lung Colorectal and Ovarian Cancer Screening Trial (PLCO) who met these criteria and their lung cancer incidence were applied to the ILST sample size for the mean follow-up occurring in the ILST. This trial is registered at ClinicalTrials.gov, NCT02871856. Study enrolment is almost complete.

FINDINGS

Between June 17, 2015, and Dec 29, 2020, 5819 participants from the International Lung Screening Trial (ILST) were enrolled on the basis of meeting USPSTF2013 criteria or the PLCOm2012 risk threshold of at least 1·51% at 6 years. The same number of individuals was selected for the PLCOm2012 model as for the USPSTF2013 criteria (4540 [78%] of 5819). After a mean follow-up of 2·3 years (SD 1·0), 135 lung cancers occurred in 4540 USPSTF2013-positive participants and 162 in 4540 participants included in the PLCOm2012 of at least 1·70% at 6 years group (cancer sensitivity difference 15·8%, 95% CI 10·7-22·1%; absolute odds ratio 4·00, 95% CI 1·89-9·44; p<0·0001). Compared to USPSTF2013-positive individuals, PLCOm2012-selected participants were older (mean age 65·7 years [SD 5·9] vs 63·3 years [5·7]; p<0·0001), had more comorbidities (median 2 [IQR 1-3] vs 1 [1-2]; p<0·0001), and shorter life expectancy (13·9 years [95% CI 12·8-14·9] vs 14·8 [13·6-16·0] years). Model-based difference in cumulative life expectancies for those diagnosed with lung cancer were higher in those who had PLCOm2012 risk of at least 1·70% at 6 years than individuals who were USPSTF2013-positive (2248·6 years [95% CI 2089·6-2425·9] vs 2000·7 years [1841·2-2160·3]; difference 247·9 years, p=0·015).

INTERPRETATION

PLCOm2012 appears to be more efficient than the USPSTF2013 criteria for selecting individuals to enrol into lung cancer screening programmes and should be used for identifying high-risk individuals who benefit from the inclusion in these programmes.

FUNDING

Terry Fox Research Institute, The UBC-VGH Hospital Foundation and the BC Cancer Foundation, the Alberta Cancer Foundation, the Australian National Health and Medical Research Council, Cancer Research UK and a consortium of funders, and the Roy Castle Lung Cancer Foundation for the UK Lung Screen Uptake Trial.

Smoking Cessation After Lung Cancer Diagnosis and the Risk of Second Primary Lung Cancer: The Multiethnic Cohort Study

Background

Smoking cessation reduces lung cancer mortality. However, little is known about whether diagnosis of lung cancer impacts changes in smoking behaviors. Furthermore, the effects of smoking cessation on the risk of second primary lung cancer (SPLC) have not been established yet. This study aims to examine smoking behavior changes after initial primary lung cancer (IPLC) diagnosis and estimate the effect of smoking cessation on SPLC risk following IPLC diagnosis.

Methods

The study cohort consisted of 986 participants in the Multiethnic Cohort Study who were free of lung cancer and active smokers at baseline (1993-1996), provided 10-year follow-up smoking data (2003-2008), and were diagnosed with IPLC in 1993-2017. The primary outcome was a change in smoking status from “current” at baseline to “former” at 10-year follow-up (ie, smoking cessation), analyzed using logistic regression. The second outcome was SPLC incidence after smoking cessation, estimated using cause-specific Cox regression. All statistical tests were 2-sided.

Results

Among 986 current smokers at baseline, 51.1% reported smoking cessation at 10-year follow-up. The smoking cessation rate was statistically significantly higher (80.6%) for those diagnosed with IPLC between baseline and 10-year follow-up vs those without IPLC diagnosis (45.4%) during the 10-year period (adjusted odds ratio = 5.12, 95% confidence interval [CI] = 3.38 to 7.98; P < .001). Incidence of SPLC was statistically significantly lower among the 504 participants who reported smoking cessation at follow-up compared with those without smoking cessation (adjusted hazard ratio = 0.31, 95% CI = 0.14 to 0.67; P = .003).

Conclusion

Lung cancer diagnosis has a statistically significant impact on smoking cessation. Quitting smoking after IPLC diagnosis may reduce the risk of developing a subsequent malignancy in the lungs.

Brief Report: Risk Prediction Model Versus United States Preventive Services Task Force 2020 Draft Lung Cancer Screening Eligibility Criteria-Reducing Race Disparities

Introduction

Eligibility criteria for lung cancer screening based solely on age and smoking history are less sensitive than validated risk prediction models. The U.S. Preventive Services Task Force (USPSTF) has proposed new guidelines to improve the sensitivity for selecting high-risk individuals and to decrease race disparity. In this retrospective study, termed the Chicago Race Eligibility for Screening Cohort, we compare the sensitivity of the proposed USPSTF2020 criteria versus the PLCOm2012 risk prediction model for selecting a racially diverse lung cancer population with a smoking history for lung cancer screening.

Methods

This Chicago Race Eligibility for Screening Cohort study applies the PLCOm2012 model with a risk threshold of 1.0%/6 years and the USPSTF2020 criteria (age 50-80 y, pack-years ≥ 20 y, quit-years ≤ 15 y) to 883 individuals with a smoking history diagnosed with having lung cancer.

Results

The PLCOm2012 was more sensitive than the USPSTF2020 overall (79.1% versus 68.6%, p < 0.0001) in White (81.5% versus 75.4%, p = 0.029) and in African American (82.8% versus 70.6% p < 0.0001) individuals. Of the total cohort, 254 (28.8%) would not have qualified owing to less than 20 pack-years, quit-time of more than 15 years, and age less than 50 years. Of these 254 cases, 40% would have qualified by the PLCOm2012 model. For the 20 pack-year criterion, of the 497 African American individuals, 19.3% did not meet this criterion, and of these, an additional 31.3% would have qualified by the PLCOm2012 model (p = 0.002).

Conclusions

Although more sensitive than USPSTF2013, the proposed USPSTF2020 draft guidelines still have a race disparity in eligibility for screening. This study provides "real world" evidence that use of the PLCOm2012 risk prediction model eliminates this race disparity.

Measuring the impact of the COVID-19 pandemic on organized cancer screening and diagnostic follow-up care in Ontario, Canada: A provincial, population-based study

It is essential to quantify the impacts of the COVID-19 pandemic on cancer screening, including for vulnerable sub-populations, to inform the development of evidence-based, targeted pandemic recovery strategies. We undertook a population-based retrospective observational study in Ontario, Canada to assess the impact of the pandemic on organized cancer screening and diagnostic services, and assess whether patterns of cancer screening service use and diagnostic delay differ across population sub-groups during the pandemic. Provincial health databases were used to identify age-eligible individuals who participated in one or more of Ontario's breast, cervical, colorectal, and lung cancer screening programs from January 1, 2019-December 31, 2020. Ontario's screening programs delivered 951,000 (-41%) fewer screening tests in 2020 than in 2019 and volumes for most programs remained more than 20% below historical levels by the end of 2020. A smaller percentage of cervical screening participants were older (50-59 and 60-69 years) during the pandemic when compared with 2019. Individuals in the oldest age groups and in lower-income neighborhoods were significantly more likely to experience diagnostic delay following an abnormal breast, cervical, or colorectal cancer screening test during the pandemic, and individuals with a high probability of living on a First Nation reserve were significantly more likely to experience diagnostic delay following an abnormal fecal test. Ongoing monitoring and management of backlogs must continue. Further evaluation is required to identify populations for whom access to cancer screening and diagnostic care has been disproportionately impacted and quantify impacts of these service disruptions on cancer incidence, stage, and mortality. This information is critical to pandemic recovery efforts that are aimed at achieving equitable and timely access to cancer screening-related care.

Two-Year Follow-Up of a Randomized Controlled Study of Integrated Smoking Cessation in a Lung Cancer Screening Program

Introduction

Smoking cessation activities incorporated into lung cancer screening programs have been broadly recommended, but studies to date have not exhibited increased quit rates associated with cessation programs in this setting. We aimed to determine the long-term effectiveness of smoking cessation counseling in smokers presenting for lung cancer screening.

Methods

This was a randomized control trial of an intensive, telephone-based smoking cessation counseling intervention incorporating lung cancer screening results versus usual care (information pamphlet). This analysis reports on the long-term impact (24-mo) of the intervention on abstinence from smoking.

Results

A total of 337 active smokers who participated in the screening study were randomized to active smoking cessation counseling (n = 171) or control arm (n = 174) and completed a 24-month assessment. The 30-day smoking abstinence rates at 24 months postrandomization was 18.3% and 21.4% in the control and intervention arms, respectively—a 3.1% difference (95% confidence interval: −5.4 to 11.6, p = 0.48). No statistically significant differences in the 7-day abstinence, the use of pharmacologic cessation aids, nicotine replacement therapies, nor intent to quit in the following 30 days were noted (p > 0.05). The abstinence rates at 24-months were higher overall than at 12-months (19.9% versus 13.3%, p < 0.001), and smoking intensity was lower than at baseline for ongoing smokers.

Conclusions

A telephone-based smoking cessation counseling intervention incorporating lung cancer screening results did not result in increased long-term cessation rates versus written information alone in unselected smokers undergoing lung cancer screening. Overall, quit rates were high and continued to improve throughout participation in the screening program. (ClinicalTrials.govNCT02431962).

Development and Validation of a Risk Prediction Tool for Second Primary Lung Cancer

Background

With advancing therapeutics, lung cancer (LC) survivors are rapidly increasing in number. Although mounting evidence suggests LC survivors have high risk of second primary lung cancer (SPLC), there is no validated prediction model available for clinical use to identify high-risk LC survivors for SPLC.

Methods

Using data from 6325 ever-smokers in the Multiethnic Cohort (MEC) study diagnosed with initial primary lung cancer (IPLC) in 1993-2017, we developed a prediction model for 10-year SPLC risk after IPLC diagnosis using cause-specific Cox regression. We evaluated the model’s clinical utility using decision curve analysis and externally validated it using 2 population-based data—Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and National Lung Screening Trial (NLST)—that included 2963 and 2844 IPLC (101 and 93 SPLC cases), respectively.

Results

Over 14 063 person-years, 145 (2.3%) ever-smoking IPLC patients developed SPLC in MEC. Our prediction model demonstrated a high predictive accuracy (Brier score = 2.9, 95% confidence interval [CI] = 2.4 to 3.3) and discrimination (area under the receiver operating characteristics [AUC] = 81.9%, 95% CI = 78.2% to 85.5%) based on bootstrap validation in MEC. Stratification by the estimated risk quartiles showed that the observed SPLC incidence was statistically significantly higher in the 4th vs 1st quartile (9.5% vs 0.2%; P < .001). Decision curve analysis indicated that in a wide range of 10-year risk thresholds from 1% to 20%, the model yielded a larger net-benefit vs hypothetical all-screening or no-screening scenarios. External validation using PLCO and NLST showed an AUC of 78.8% (95% CI = 74.6% to 82.9%) and 72.7% (95% CI = 67.7% to 77.7%), respectively.

Conclusions

We developed and validated a SPLC prediction model based on large population-based cohorts. The proposed prediction model can help identify high-risk LC patients for SPLC and can be incorporated into clinical decision making for SPLC surveillance and screening.

Addressing Gender Disparities in Lung Cancer Screening Eligibility: USPSTF versus PLCOm2012 Criteria

BACKGROUND

Lung cancer is the leading cause of cancer death in women in the United States. Prospective randomized lung screening trials suggest a greater lung cancer mortality benefit from screening women compared to men.

RESEARCH QUESTION

Do the United States Preventative Services Task Force (USPSTF) lung screening guidelines that are based solely on age and smoking history contribute to gender disparities in eligibility, and if so, does the use of the PLCOm2012 risk prediction model that is based on 11 predictors of lung cancer reduce gender disparities?

STUDY DESIGN AND METHODS

This retrospective analysis of 883 lung cancer cases in the Chicago Race Eligibility for Screening Cohort (CREST) determined the sensitivity of USPSTF versus PLCOm2012 eligibility criteria, stratified by gender. For comparisons to the USPSTF 2013and the recently published USPSTF 2021(released March 9^th, 2021) eligibility criteria, the PLCOm2012 model was used with risk thresholds of ≥1.7%/6y and >1.0%/6y, respectively.

RESULTS

The sensitivities for screening by the USPSTF 2013were 46.7% for women and 64.6% for men (p=0.003) and by the USPSTF 2021were 56.8% and 71.8%, respectively (p=0.02). In contrast, the PLCOm2012 ≥1.7%/6y sensitivities were 64.6% and 70.4%, respectively, and the PLCOm2012 ≥1.0%/6y sensitivities were 77.4% and 82.4%, respectively. The PLCOm2012 differences in sensitivity using ≥1.7%/6y and ≥1.0%/6y thresholds between women and men were nonsignificant (both p=0.07). Compared to men, women were more likely to be ineligible by the USPSTF 2021criteria because their smoking exposures were <20 pack-years (22.8% vs 14.8%, OR_{Women vs Men} 1.70, 95% CI 1.19-2.44; p=0.002) and 27% of these ineligible women were eligible by the PLCOm2012 >1.0%/6y criteria.

INTERPRETATION

Although the USPSTF 2021eligibility criteria are more sensitive than the USPSTF 2013guidelines, there remains gender disparities in eligibility. Adding the PLCOm2012 risk prediction model to the USPSTF guidelines would improve sensitivity and attenuate gender disparities.

Personalising lung cancer screening: An overview of risk-stratification opportunities and challenges

Randomised clinical trials have shown the efficacy of computed tomography lung cancer screening, initiating discussions on whether and how to implement population‐based screening programs. Due to smoking behaviour being the primary risk‐factor for lung cancer and part of the criteria for determining screening eligibility, lung cancer screening is inherently risk‐based. In fact, the selection of high‐risk individuals has been shown to be essential in implementing lung cancer screening in a cost‐effective manner. Furthermore, studies have shown that further risk‐stratification may improve screening efficiency, allow personalisation of the screening interval and reduce health disparities. However, implementing risk‐based lung cancer screening programs also requires overcoming a number of challenges. There are indications that risk‐based approaches can negatively influence the trade‐off between individual benefits and harms if not applied thoughtfully. Large‐scale implementation of targeted, risk‐based screening programs has been limited thus far. Consequently, questions remain on how to efficiently identify and invite high‐risk individuals from the general population. Finally, while risk‐based approaches may increase screening program efficiency, efficiency should be balanced with the overall impact of the screening program. In this review, we will address the opportunities and challenges in applying risk‐stratification in different aspects of lung cancer screening programs, as well as the balance between screening program efficiency and impact.

Selection of individuals for lung cancer screening based on risk prediction model performance and economic factors - The Ontario experience

INTRODUCTION

Randomized controlled trials have shown that screening with computed tomography reduces lung cancer mortality but is most effective when applied to high-risk individuals. Accurate lung cancer risk prediction models effectively select individuals for screening. Few pilots or programs have implemented risk models for enrolling individuals for screening in real-world, population-based settings. This report describes implementation of the PLCOm2012 risk prediction model in the Ontario Health (Cancer Care Ontario) lung cancer screening Pilot.

METHODS

In the Pilot's Health Technology Assessment, 576 categorical age/pack-years/quit-years scenarios were evaluated using MISCAN microsimulation modeling and cost-effectiveness analyses. A preferred model was selected which provided the most life-years gained per cost. The PLCOm2012 was compared to the preferred MISCAN scenario at a threshold that yielded the same number eligible (risk ≥2.0 %/6-years).

RESULTS

The PLCOm2012 had significantly higher sensitivity and predictive value (68.1 % vs 59.6 %, p < 0.0001; 4.90 % vs 4.29 %, p = 0.044), and an Expert Panel selected it for use in the Pilot. The Pilot cancer detection rate was significantly higher than in the NLST (p = 0.009) or NELSON (p = 0.003) and there was a significant shift to early stage compared to historical Ontario Cancer Registry statistics (p < 0.0001). Pre- and post-Pilot evaluations found that conducting quality risk assessments were not excessively time consuming or difficult, and participants' satisfaction was high.

CONCLUSIONS

The PLCOm2012 was efficiently implemented in the Pilot in a real-world setting and is being used to transition into a provincial program. Compared to categorical age/pack-years/quit-years criteria, risk assessment using the PLCOm2012 can lead to effective and efficient screening.

Lung Cancer Screening With Low Dose Computed Tomography in Patients With and Without Prior History of Cancer in the National Lung Screening Trial

INTRODUCTION

Patients with a prior history of cancer (PHC) are at increased risk of second primary malignancy, of which lung cancer is the most common. We compared the performance metrics of positive screening rates and cancer detection rates (CDRs) among those with versus without PHC.

METHODS

We conducted a secondary analysis of 26,366 National Lung Screening Trial participants screened with low dose computed tomography between August 2002 and September 2007. We evaluated absolute rates and age-adjusted relative risks (RRs) of positive screening rates on the basis of retrospective Lung CT Screening Reporting & Data System (Lung-RADS) application, invasive diagnostic procedure rate, complication rate, and CDR in those with versus without PHC using a binary logistic regression model using Firth's penalized likelihood. We also compared cancer type, stage, and treatment in those with versus without PHC.

RESULTS

A total of 4.1% (n = 1071) of patients had PHC. Age-adjusted rates of positive findings were similar in those with versus without PHC (Baseline: PHC = 13.7% versus no PHC = 13.3%, RR [95% confidence interval (CI)]: 1.04 [0.88-1.24]; Subsequent: PHC = 5.6% versus no PHC = 5.5%, RR [95% CI]: 1.02 [0.84-1.23]). Age-adjusted CDRs were higher in those with versus without PHC on baseline (PHC=1.9% versus no PHC = 0.8%, RR [95% CI]: 2.51 [1.67-3.81]) but not on subsequent screenings (PHC = 0.6% versus no PHC = 0.4%, RR [95% CI]: 1.37 [0.99-1.93]). There were no differences in cancer stage, type, or treatment by PHC status.

CONCLUSIONS

Patients with PHC may benefit from lung cancer screening, and with their providers, should be made aware of the possibility of higher cancer detection, invasive procedures, and complication rates on baseline lung cancer screening, but not on subsequent low dose computed tomography screening examinations.

A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies

BACKGROUND

Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared with current United States Preventive Services Task Force (USPSTF) recommendations.

METHODS

Four independent natural history models were used to perform a comparative modeling study evaluating long-term benefits and harms of selecting individuals for lung cancer screening through risk-prediction models. In total, 363 risk-based screening strategies varying by screening starting and stopping age, risk-prediction model used for eligibility (Bach, PLCOm2012, or Lung Cancer Death Risk Assessment Tool [LCDRAT]), and risk threshold were evaluated for a 1950 US birth cohort. Among the evaluated outcomes were percentage of individuals ever screened, screens required, lung cancer deaths averted, life-years gained, and overdiagnosis.

RESULTS

Risk-based screening strategies requiring similar screens among individuals ages 55-80 years as the USPSTF criteria (corresponding risk thresholds: Bach = 2.8%; PLCOm2012 = 1.7%; LCDRAT = 1.7%) averted considerably more lung cancer deaths (Bach = 693; PLCOm2012 = 698; LCDRAT = 696; USPSTF = 613). However, life-years gained were only modestly higher (Bach = 8660; PLCOm2012 = 8862; LCDRAT = 8631; USPSTF = 8590), and risk-based strategies had more overdiagnosed cases (Bach = 149; PLCOm2012 = 147; LCDRAT = 150; USPSTF = 115). Sensitivity analyses suggest excluding individuals with limited life expectancies (<5 years) from screening retains the life-years gained by risk-based screening, while reducing overdiagnosis by more than 65.3%.

CONCLUSIONS

Risk-based lung cancer screening strategies prevent considerably more lung cancer deaths than current recommendations do. However, they yield modest additional life-years and increased overdiagnosis because of predominantly selecting older individuals. Efficient implementation of risk-based lung cancer screening requires careful consideration of life expectancy for determining optimal individual stopping ages.

Lung Screening Benefits and Challenges: A Review of The Data and Outline for Implementation

Lung cancer is the leading cause of cancer-related deaths worldwide, accounting for almost a fifth of all cancer-related deaths. Annual computed tomographic lung cancer screening (CTLS) detects lung cancer at earlier stages and reduces lung cancer-related mortality among high-risk individuals. Many medical organizations, including the U.S. Preventive Services Task Force, recommend annual CTLS in high-risk populations. However, fewer than 5% of individuals worldwide at high risk for lung cancer have undergone screening. In large part, this is owing to delayed implementation of CTLS in many countries throughout the world. Factors contributing to low uptake in countries with longstanding CTLS endorsement, such as the United States, include lack of patient and clinician awareness of current recommendations in favor of CTLS and clinician concerns about CTLS-related radiation exposure, false-positive results, overdiagnosis, and cost. This review of the literature serves to address these concerns by evaluating the potential risks and benefits of CTLS. Review of key components of a lung screening program, along with an updated shared decision aid, provides guidance for program development and optimization. Review of studies evaluating the population considered "high-risk" is included as this may affect future guidelines within the United States and other countries considering lung screening implementation.

Organized Lung Cancer Screening Pilot: Informing a Province-Wide Program in Ontario, Canada

BACKGROUND

Lung cancer is the leading cause of cancer deaths in Ontario. The National Lung Screening Trial demonstrated that screening with low-dose computed tomography (LDCT) reduces lung cancer mortality.

METHODS

In June 2017, Ontario Health (Cancer Care Ontario) initiated a pilot for lung cancer screening to inform implementation of a province-wide initiative. The screening pathway includes targeted recruitment strategies, the Tammemägi risk prediction model (PLCOm2012) to determine eligibility, opt-out smoking cessation services for all current smokers, use of the Lung-RADS scoring system to guide abnormal results management, and screening navigators providing end-to-end support. Referral criteria include being 55 years of age to 74 years of age and a current or former daily cigarette smoker for greater than or equal to 20 years, while the screening eligibility criterion is a PLCOm2012 risk greater than or equal to 2% in 6 years. Selected results of the interim pilot evaluation are presented. Four hospitals contributed data in the first year of the pilot.

RESULTS

During 2017 to 2018, 4205 Ontarians were recruited, 3234 risk assessments were conducted, and 2151 (66.5%) individuals were eligible for screening. Baseline LDCT scans were performed in 1624 (50.2%) individuals. Diagnostic evaluation in 120 (7.4%) individuals identified 28 (1.7%) with lung cancer, and proportions of stage I to II and stage III to IV were 71% and 29%, respectively. Of those recruited, 1443 (34.3%) individuals were smokers and 1326 (91.9%) accepted smoking cessation services.

CONCLUSIONS

The pilot is the largest in Canada and aligns with International Agency for Research on Cancer standards for population-based, organized cancer screening. Recruitment of high-risk individuals, high rates of smoking cessation program acceptance, and detection of early-stage cancers are demonstrated.

Correction: Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study

[This corrects the article DOI: 10.1371/journal.pmed.1002277.].

Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial

IMPORTANCE

Low-dose computed tomography lung cancer screening is most effective when applied to high-risk individuals.

OBJECTIVES

To develop and validate a risk prediction model that incorporates low-dose computed tomography screening results.

DESIGN, SETTING, AND PARTICIPANTS

A logistic regression risk model was developed in National Lung Screening Trial (NLST) Lung Screening Study (LSS) data and was validated in NLST American College of Radiology Imaging Network (ACRIN) data. The NLST was a randomized clinical trial that recruited participants between August 2002 and April 2004, with follow-up to December 31, 2009. This secondary analysis of data from the NLST took place between August 10, 2013, and November 1, 2018. Included were LSS (n = 14 576) and ACRIN (n = 7653) participants who had 3 screens, adequate follow-up, and complete predictor information.

MAIN OUTCOMES AND MEASURES

Incident lung cancers occurring 1 to 4 years after the third screen (202 LSS and 96 ACRIN). Predictors included scores from the validated PLCOm2012 risk model and Lung CT Screening Reporting & Data System (Lung-RADS) screening results.

RESULTS

Overall, the mean (SD) age of 22 229 participants was 61.3 (5.0) years, 59.3% were male, and 90.9% were of non-Hispanic white race/ethnicity. During follow-up, 298 lung cancers were diagnosed in 22 229 individuals (1.3%). Eight result combinations were pooled into 4 groups based on similar associations. Adjusted for PLCOm2012 risks, compared with participants with 3 negative screens, participants with 1 positive screen and last negative had an odds ratio (OR) of 1.93 (95% CI, 1.34-2.76), and participants with 2 positive screens with last negative or 2 negative screens with last positive had an OR of 2.66 (95% CI, 1.60-4.43); when 2 or more screens were positive with last positive, the OR was 8.97 (95% CI, 5.76-13.97). In ACRIN validation data, the model that included PLCOm2012 scores and screening results (PLCO2012results) demonstrated significantly greater discrimination (area under the curve, 0.761; 95% CI, 0.716-0.799) than when screening results were excluded (PLCOm2012) (area under the curve, 0.687; 95% CI, 0.645-0.728) (P < .001). In ACRIN validation data, PLCO2012results demonstrated good calibration. Individuals who had initial negative scans but elevated PLCOm2012 six-year risks of at least 2.6% did not have risks decline below the 1.5% screening eligibility criterion when subsequent screens were negative.

CONCLUSIONS AND RELEVANCE

According to this analysis, some individuals with elevated risk scores who have negative initial screens remain at elevated risks, warranting annual screening. Positive screens seem to increase baseline risk scores and may identify high-risk individuals for continued screening and enrollment into clinical trials.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00047385.

Selecting lung cancer screenees using risk prediction models-where do we go from here

The National Lung Screening Trial (NLST) demonstrated that low dose computed tomography (LDCT) screening could reduce lung cancer mortality by 20% in high-risk individuals. The United States Preventive Services Task Force (USPSTF) and Centers for Medicare and Medicaid Services (CMS) approved lung cancer screening. The NLST, USPSTF and CMS define high risk as smoking ≥30 pack-years, smoking within the past 15 years, and being ages 55-74, 55-80 or 55-77. Retrospective studies demonstrated selection using model-estimated risk is superior to NLST-like criteria: higher sensitivity and positive predictive value (PPV), more deaths averted and higher cost-effectiveness. Projects are underway that may additionally support use of risk to determine eligibility. Firstly, the International Lung Screen Trial (ILST) is prospectively enrolling 4,000 individuals for screening if individuals have PLCOm2012 model risk ≥1.5% or are USPSTF+ve. Six-year follow-up will allow comparisons. Interim results support the risk approach. Secondly, Cancer Care Ontario started the Lung Cancer Screening Pilot for People at High Risk in order to find optimal design for province-wide programmatic screening. They are enrolling 3,000 individuals to screening based on PLCOm2012 risk ≥2%. Some hesitation to recommend screening selection based on model risk comes from the observation that selected individuals are older, have more comorbidities, are expected to have fewer life years and quality-adjusted life years (QALY) and are more likely to die from competing causes. We show that 25.6% of NLST eligible smokers are at low risk (6-year lung cancer incidence proportion =0.008). This group will not benefit from screening but has lower age, fewer comorbidities and fewer competing causes of death. When they are excluded from the NLST+ve group, age, comorbidity count and competing causes of death are similar to those in the PLCOm2012+ve group. In some jurisdictions, model-based lung cancer screening selection needs to take into consideration the elevated risk in blacks and indigenous peoples.

Factors Associated With Small Aggressive Non-Small Cell Lung Cancers in the National Lung Screening Trial: A Validation Study

Background

A small proportion of non-small cell lung cancers (NSCLCs) have been observed to spread to distant lymph nodes (N3) or metastasize (M1) or both, while the primary tumor is small (≤3 cm, T1). These small aggressive NSCLCs (SA-NSLSC) are important as they are clinically significant, may identify unique biologic pathways, and warrant aggressive follow-up and treatment. This study identifies factors associated with SA-NSCLC and attempts to validate a previous finding that women with a family history of lung cancer are at particularly elevated risk of SA-NSCLC.

Methods

This study used a case-case design within the National Cancer Institute's National Lung Screening Trial (NLST) cohort. Case patients and "control" patients were selected based on TNM staging parameters. Case patients (n = 64) had T1 NSCLCs that were N3 or M1 or both, while "control" patients (n = 206) had T2 or T3, N0 to N2, and M0 NSCLCs. Univariate and multivariable logistic regression were used to identify factors associated with SA-NSCLC.

Results

In bootstrap bias-corrected multivariable logistic regression models, small aggressive adenocarcinomas were associated with a positive history of emphysema (odds ratio [OR] = 5.15, 95% confidence interval [CI] = 1.63 to 23.00) and the interaction of female sex and a positive family history of lung cancer (OR = 6.55, 95% CI = 1.06 to 50.80).

Conclusions

Emphysema may play a role in early lung cancer progression. Females with a family history of lung cancer are at increased risk of having small aggressive lung adenocarcinomas. These results validate previous findings and encourage research on the role of female hormones interacting with family history and genetic factors in lung carcinogenesis and progression.

Risk Stratification for Second Primary Lung Cancer

Purpose This study estimated the 10-year risk of developing second primary lung cancer (SPLC) among survivors of initial primary lung cancer (IPLC) and evaluated the clinical utility of the risk prediction model for selecting eligibility criteria for screening. Methods SEER data were used to identify a population-based cohort of 20,032 participants diagnosed with IPLC between 1988 and 2003 and who survived ≥ 5 years after the initial diagnosis. We used a proportional subdistribution hazards model to estimate the 10-year risk of developing SPLC among survivors of lung cancer LC in the presence of competing risks. Considered predictors included age, sex, race, treatment, histology, stage, and extent of disease. We examined the risk-stratification ability of the prediction model and performed decision curve analysis to evaluate the clinical utility of the model by calculating its net benefit in varied risk thresholds for screening. Results Although the median 10-year risk of SPLC among survivors of LC was 8.36%, the estimated risk varied substantially (range, 0.56% to 14.3%) when stratified by age, histology, and extent of IPLC in the final prediction model. The stratification by deciles of estimated risk showed that the observed incidence of SPLC was significantly higher in the tenth-decile group (12.5%) versus the first-decile group (2.9%; P < 10^-10). The decision curve analysis yielded a range of risk thresholds (1% to 11.5%) at which the clinical net benefit of the risk model was larger than those in hypothetical all-screening or no-screening scenarios. Conclusion The risk stratification approach in SPLC can be potentially useful for identifying survivors of LC to be screened by computed tomography. More comprehensive environmental and genetic data may help enhance the predictability and stratification ability of the risk model for SPLC.

The Cost-Effectiveness of High-Risk Lung Cancer Screening and Drivers of Program Efficiency

INTRODUCTION

Lung cancer risk prediction models have the potential to make programs more affordable; however, the economic evidence is limited.

METHODS

Participants in the National Lung Cancer Screening Trial (NLST) were retrospectively identified with the risk prediction tool developed from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. The high-risk subgroup was assessed for lung cancer incidence and demographic characteristics compared with those in the low-risk subgroup and the Pan-Canadian Early Detection of Lung Cancer Study (PanCan), which is an observational study that was high-risk-selected in Canada. A comparison of high-risk screening versus standard care was made with a decision-analytic model using data from the NLST with Canadian cost data from screening and treatment in the PanCan study. Probabilistic and deterministic sensitivity analyses were undertaken to assess uncertainty and identify drivers of program efficiency.

RESULTS

Use of the risk prediction tool developed from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial with a threshold set at 2% over 6 years would have reduced the number of individuals who needed to be screened in the NLST by 81%. High-risk screening participants in the NLST had more adverse demographic characteristics than their counterparts in the PanCan study. High-risk screening would cost $20,724 (in 2015 Canadian dollars) per quality-adjusted life-year gained and would be considered cost-effective at a willingness-to-pay threshold of $100,000 in Canadian dollars per quality-adjusted life-year gained with a probability of 0.62. Cost-effectiveness was driven primarily by non-lung cancer outcomes. Higher noncurative drug costs or current costs for immunotherapy and targeted therapies in the United States would render lung cancer screening a cost-saving intervention.

CONCLUSIONS

Non-lung cancer outcomes drive screening efficiency in diverse, tobacco-exposed populations. Use of risk selection can reduce the budget impact, and screening may even offer cost savings if noncurative treatment costs continue to rise.

Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study

BACKGROUND

Selection of candidates for lung cancer screening based on individual risk has been proposed as an alternative to criteria based on age and cumulative smoking exposure (pack-years). Nine previously established risk models were assessed for their ability to identify those most likely to develop or die from lung cancer. All models considered age and various aspects of smoking exposure (smoking status, smoking duration, cigarettes per day, pack-years smoked, time since smoking cessation) as risk predictors. In addition, some models considered factors such as gender, race, ethnicity, education, body mass index, chronic obstructive pulmonary disease, emphysema, personal history of cancer, personal history of pneumonia, and family history of lung cancer.

METHODS AND FINDINGS

Retrospective analyses were performed on 53,452 National Lung Screening Trial (NLST) participants (1,925 lung cancer cases and 884 lung cancer deaths) and 80,672 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) ever-smoking participants (1,463 lung cancer cases and 915 lung cancer deaths). Six-year lung cancer incidence and mortality risk predictions were assessed for (1) calibration (graphically) by comparing the agreement between the predicted and the observed risks, (2) discrimination (area under the receiver operating characteristic curve [AUC]) between individuals with and without lung cancer (death), and (3) clinical usefulness (net benefit in decision curve analysis) by identifying risk thresholds at which applying risk-based eligibility would improve lung cancer screening efficacy. To further assess performance, risk model sensitivities and specificities in the PLCO were compared to those based on the NLST eligibility criteria. Calibration was satisfactory, but discrimination ranged widely (AUCs from 0.61 to 0.81). The models outperformed the NLST eligibility criteria over a substantial range of risk thresholds in decision curve analysis, with a higher sensitivity for all models and a slightly higher specificity for some models. The PLCOm2012, Bach, and Two-Stage Clonal Expansion incidence models had the best overall performance, with AUCs >0.68 in the NLST and >0.77 in the PLCO. These three models had the highest sensitivity and specificity for predicting 6-y lung cancer incidence in the PLCO chest radiography arm, with sensitivities >79.8% and specificities >62.3%. In contrast, the NLST eligibility criteria yielded a sensitivity of 71.4% and a specificity of 62.2%. Limitations of this study include the lack of identification of optimal risk thresholds, as this requires additional information on the long-term benefits (e.g., life-years gained and mortality reduction) and harms (e.g., overdiagnosis) of risk-based screening strategies using these models. In addition, information on some predictor variables included in the risk prediction models was not available.

CONCLUSIONS

Selection of individuals for lung cancer screening using individual risk is superior to selection criteria based on age and pack-years alone. The benefits, harms, and feasibility of implementing lung cancer screening policies based on risk prediction models should be assessed and compared with those of current recommendations.

Performance and Cost-Effectiveness of Computed Tomography Lung Cancer Screening Scenarios in a Population-Based Setting: A Microsimulation Modeling Analysis in Ontario, Canada

BACKGROUND

The National Lung Screening Trial (NLST) results indicate that computed tomography (CT) lung cancer screening for current and former smokers with three annual screens can be cost-effective in a trial setting. However, the cost-effectiveness in a population-based setting with >3 screening rounds is uncertain. Therefore, the objective of this study was to estimate the cost-effectiveness of lung cancer screening in a population-based setting in Ontario, Canada, and evaluate the effects of screening eligibility criteria.

METHODS AND FINDINGS

This study used microsimulation modeling informed by various data sources, including the Ontario Health Insurance Plan (OHIP), Ontario Cancer Registry, smoking behavior surveys, and the NLST. Persons, born between 1940 and 1969, were examined from a third-party health care payer perspective across a lifetime horizon. Starting in 2015, 576 CT screening scenarios were examined, varying by age to start and end screening, smoking eligibility criteria, and screening interval. Among the examined outcome measures were lung cancer deaths averted, life-years gained, percentage ever screened, costs (in 2015 Canadian dollars), and overdiagnosis. The results of the base-case analysis indicated that annual screening was more cost-effective than biennial screening. Scenarios with eligibility criteria that required as few as 20 pack-years were dominated by scenarios that required higher numbers of accumulated pack-years. In general, scenarios that applied stringent smoking eligibility criteria (i.e., requiring higher levels of accumulated smoking exposure) were more cost-effective than scenarios with less stringent smoking eligibility criteria, with modest differences in life-years gained. Annual screening between ages 55-75 for persons who smoked ≥40 pack-years and who currently smoke or quit ≤10 y ago yielded an incremental cost-effectiveness ratio of $41,136 Canadian dollars ($33,825 in May 1, 2015, United States dollars) per life-year gained (compared to annual screening between ages 60-75 for persons who smoked ≥40 pack-years and who currently smoke or quit ≤10 y ago), which was considered optimal at a cost-effectiveness threshold of $50,000 Canadian dollars ($41,114 May 1, 2015, US dollars). If 50% lower or higher attributable costs were assumed, the incremental cost-effectiveness ratio of this scenario was estimated to be $38,240 ($31,444 May 1, 2015, US dollars) or $48,525 ($39,901 May 1, 2015, US dollars), respectively. If 50% lower or higher costs for CT examinations were assumed, the incremental cost-effectiveness ratio of this scenario was estimated to be $28,630 ($23,542 May 1, 2015, US dollars) or $73,507 ($60,443 May 1, 2015, US dollars), respectively. This scenario would screen 9.56% (499,261 individuals) of the total population (ever- and never-smokers) at least once, which would require 4,788,523 CT examinations, and reduce lung cancer mortality in the total population by 9.05% (preventing 13,108 lung cancer deaths), while 12.53% of screen-detected cancers would be overdiagnosed (4,282 overdiagnosed cases). Sensitivity analyses indicated that the overall results were most sensitive to variations in CT examination costs. Quality of life was not incorporated in the analyses, and assumptions for follow-up procedures were based on data from the NLST, which may not be generalizable to a population-based setting.

CONCLUSIONS

Lung cancer screening with stringent smoking eligibility criteria can be cost-effective in a population-based setting.

Evaluation of the lung cancer risks at which to screen ever- and never-smokers: screening rules applied to the PLCO and NLST cohorts

BACKGROUND

Lung cancer risks at which individuals should be screened with computed tomography (CT) for lung cancer are undecided. This study's objectives are to identify a risk threshold for selecting individuals for screening, to compare its efficiency with the U.S. Preventive Services Task Force (USPSTF) criteria for identifying screenees, and to determine whether never-smokers should be screened. Lung cancer risks are compared between smokers aged 55-64 and ≥ 65-80 y.

METHODS AND FINDINGS

Applying the PLCO(m2012) model, a model based on 6-y lung cancer incidence, we identified the risk threshold above which National Lung Screening Trial (NLST, n = 53,452) CT arm lung cancer mortality rates were consistently lower than rates in the chest X-ray (CXR) arm. We evaluated the USPSTF and PLCO(m2012) risk criteria in intervention arm (CXR) smokers (n = 37,327) of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). The numbers of smokers selected for screening, and the sensitivities, specificities, and positive predictive values (PPVs) for identifying lung cancers were assessed. A modified model (PLCOall2014) evaluated risks in never-smokers. At PLCO(m2012) risk ≥ 0.0151, the 65th percentile of risk, the NLST CT arm mortality rates are consistently below the CXR arm's rates. The number needed to screen to prevent one lung cancer death in the 65th to 100th percentile risk group is 255 (95% CI 143 to 1,184), and in the 30th to <65th percentile risk group is 963 (95% CI 291 to -754); the number needed to screen could not be estimated in the <30th percentile risk group because of absence of lung cancer deaths. When applied to PLCO intervention arm smokers, compared to the USPSTF criteria, the PLCO(m2012) risk ≥ 0.0151 threshold selected 8.8% fewer individuals for screening (p<0.001) but identified 12.4% more lung cancers (sensitivity 80.1% [95% CI 76.8%-83.0%] versus 71.2% [95% CI 67.6%-74.6%], p<0.001), had fewer false-positives (specificity 66.2% [95% CI 65.7%-66.7%] versus 62.7% [95% CI 62.2%-63.1%], p<0.001), and had higher PPV (4.2% [95% CI 3.9%-4.6%] versus 3.4% [95% CI 3.1%-3.7%], p<0.001). In total, 26% of individuals selected for screening based on USPSTF criteria had risks below the threshold PLCO(m2012) risk ≥ 0.0151. Of PLCO former smokers with quit time >15 y, 8.5% had PLCO(m2012) risk ≥ 0.0151. None of 65,711 PLCO never-smokers had PLCO(m2012) risk ≥ 0.0151. Risks and lung cancers were significantly greater in PLCO smokers aged ≥ 65-80 y than in those aged 55-64 y. This study omitted cost-effectiveness analysis.

CONCLUSIONS

The USPSTF criteria for CT screening include some low-risk individuals and exclude some high-risk individuals. Use of the PLCO(m2012) risk ≥ 0.0151 criterion can improve screening efficiency. Currently, never-smokers should not be screened. Smokers aged ≥ 65-80 y are a high-risk group who may benefit from screening. Please see later in the article for the Editors' Summary.

Impact of lung cancer screening results on smoking cessation

BACKGROUND

Lung cancer screening programs may provide opportunities to reduce smoking rates among participants. This study evaluates the impact of lung cancer screening results on smoking cessation.

METHODS

Data from Lung Screening Study participants in the National Lung Screening Trial (NLST; 2002-2009) were used to prepare multivariable longitudinal regression models predicting annual smoking cessation in those who were current smokers at study entry (n = 15489, excluding those developing lung cancer in follow-up). The associations of lung cancer screening results on smoking cessation over the trial period were analyzed. All hypothesis testing used two sided P values.

RESULTS

In adjusted analyses, smoking cessation was strongly associated with the amount of abnormality observed in the previous year's screening (P < .0001). Compared with those with a normal screen, individuals were less likely to be smokers if their previous year's screen had a major abnormality that was not suspicious for lung cancer (odds ratio [OR] = 0.811; 95% confidence interval [CI] = 0.722 to 0.912; P < .001), was suspicious for lung cancer but stable from previous screens (OR = 0.785; 95% CI = 0.706 to 0.872; P < .001), or was suspicious for lung cancer and was new or changed from the previous screen (OR = 0.663; 95% CI = 0.607 to 0.724; P < .001). Differences in smoking prevalence were present up to 5 years after the last screen.

CONCLUSIONS

Smoking cessation is statistically significantly associated with screen-detected abnormality. Integration of effective smoking cessation programs within screening programs should lead to further reduction in smoking-related morbidity and mortality.

Benefits and harms of computed tomography lung cancer screening strategies: a comparative modeling study for the U.S. Preventive Services Task Force

BACKGROUND

The optimum screening policy for lung cancer is unknown.

OBJECTIVE

To identify efficient computed tomography (CT) screening scenarios in which relatively more lung cancer deaths are averted for fewer CT screening examinations.

DESIGN

Comparative modeling study using 5 independent models.

DATA SOURCES

The National Lung Screening Trial; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening trial; the Surveillance, Epidemiology, and End Results program; and the U.S. Smoking History Generator.

TARGET POPULATION

U.S. cohort born in 1950.

TIME HORIZON

Cohort followed from ages 45 to 90 years.

PERSPECTIVE

Societal.

INTERVENTION

576 scenarios with varying eligibility criteria (age, pack-years of smoking, years since quitting) and screening intervals.

OUTCOME MEASURES

Benefits included lung cancer deaths averted or life-years gained. Harms included CT examinations, false-positive results (including those obtained from biopsy/surgery), overdiagnosed cases, and radiation-related deaths.

RESULTS OF BEST-CASE SCENARIO

The most advantageous strategy was annual screening from ages 55 through 80 years for ever-smokers with a smoking history of at least 30 pack-years and ex-smokers with less than 15 years since quitting. It would lead to 50% (model ranges, 45% to 54%) of cases of cancer being detected at an early stage (stage I/II), 575 screening examinations per lung cancer death averted, a 14% (range, 8.2% to 23.5%) reduction in lung cancer mortality, 497 lung cancer deaths averted, and 5250 life-years gained per the 100,000-member cohort. Harms would include 67,550 false-positive test results, 910 biopsies or surgeries for benign lesions, and 190 overdiagnosed cases of cancer (3.7% of all cases of lung cancer [model ranges, 1.4% to 8.3%]).

RESULTS OF SENSITIVITY ANALYSIS

The number of cancer deaths averted for the scenario varied across models between 177 and 862; the number of overdiagnosed cases of cancer varied between 72 and 426.

LIMITATIONS

Scenarios assumed 100% screening adherence. Data derived from trials with short duration were extrapolated to lifetime follow-up.

CONCLUSION

Annual CT screening for lung cancer has a favorable benefit-harm ratio for individuals aged 55 through 80 years with 30 or more pack-years' exposure to smoking.

PRIMARY FUNDING SOURCE

National Cancer Institute.

Overdiagnosis in low-dose computed tomography screening for lung cancer

IMPORTANCE

Screening for lung cancer has the potential to reduce mortality, but in addition to detecting aggressive tumors, screening will also detect indolent tumors that otherwise may not cause clinical symptoms. These overdiagnosis cases represent an important potential harm of screening because they incur additional cost, anxiety, and morbidity associated with cancer treatment.

OBJECTIVE

To estimate overdiagnosis in the National Lung Screening Trial (NLST).

DESIGN, SETTING, AND PARTICIPANTS

We used data from the NLST, a randomized trial comparing screening using low-dose computed tomography (LDCT) vs chest radiography (CXR) among 53 452 persons at high risk for lung cancer observed for 6.4 years, to estimate the excess number of lung cancers in the LDCT arm of the NLST compared with the CXR arm.

MAIN OUTCOMES AND MEASURES

We calculated 2 measures of overdiagnosis: the probability that a lung cancer detected by screening with LDCT is an overdiagnosis (PS), defined as the excess lung cancers detected by LDCT divided by all lung cancers detected by screening in the LDCT arm; and the number of cases that were considered overdiagnosis relative to the number of persons needed to screen to prevent 1 death from lung cancer.

RESULTS

During follow-up, 1089 lung cancers were reported in the LDCT arm and 969 in the CXR arm of the NLST. The probability is 18.5% (95% CI, 5.4%-30.6%) that any lung cancer detected by screening with LDCT was an overdiagnosis, 22.5% (95% CI, 9.7%-34.3%) that a non-small cell lung cancer detected by LDCT was an overdiagnosis, and 78.9% (95% CI, 62.2%-93.5%) that a bronchioalveolar lung cancer detected by LDCT was an overdiagnosis. The number of cases of overdiagnosis found among the 320 participants who would need to be screened in the NLST to prevent 1 death from lung cancer was 1.38.

CONCLUSIONS AND RELEVANCE

More than 18% of all lung cancers detected by LDCT in the NLST seem to be indolent, and overdiagnosis should be considered when describing the risks of LDCT screening for lung cancer.

Selection criteria for lung-cancer screening

BACKGROUND

The National Lung Screening Trial (NLST) used risk factors for lung cancer (e.g., ≥30 pack-years of smoking and <15 years since quitting) as selection criteria for lung-cancer screening. Use of an accurate model that incorporates additional risk factors to select persons for screening may identify more persons who have lung cancer or in whom lung cancer will develop.

METHODS

We modified the 2011 lung-cancer risk-prediction model from our Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to ensure applicability to NLST data; risk was the probability of a diagnosis of lung cancer during the 6-year study period. We developed and validated the model (PLCO(M2012)) with data from the 80,375 persons in the PLCO control and intervention groups who had ever smoked. Discrimination (area under the receiver-operating-characteristic curve [AUC]) and calibration were assessed. In the validation data set, 14,144 of 37,332 persons (37.9%) met NLST criteria. For comparison, 14,144 highest-risk persons were considered positive (eligible for screening) according to PLCO(M2012) criteria. We compared the accuracy of PLCO(M2012) criteria with NLST criteria to detect lung cancer. Cox models were used to evaluate whether the reduction in mortality among 53,202 persons undergoing low-dose computed tomographic screening in the NLST differed according to risk.

RESULTS

The AUC was 0.803 in the development data set and 0.797 in the validation data set. As compared with NLST criteria, PLCO(M2012) criteria had improved sensitivity (83.0% vs. 71.1%, P<0.001) and positive predictive value (4.0% vs. 3.4%, P=0.01), without loss of specificity (62.9% and. 62.7%, respectively; P=0.54); 41.3% fewer lung cancers were missed. The NLST screening effect did not vary according to PLCO(M2012) risk (P=0.61 for interaction).

CONCLUSIONS

The use of the PLCO(M2012) model was more sensitive than the NLST criteria for lung-cancer detection.

Using cancer registry data: agreement in cause-of-death data between the Ontario Cancer Registry and a longitudinal study of breast cancer patients

Data from the Ontario Cancer Registry (OCR) were compared with data from a multi-centred prospective cohort of 1655 node-negative breast cancer patients with intensive clinical follow-up. Agreement in cause of death was evaluated using kappa statistics. The accuracy of OCR classification was evaluated against the Mount Sinai Hospital (MSH) study oncologist's interpretation of intensely followed, cohort-collected data as the reference standard. The two sources showed a high level of agreement (kappa statistic [kappa] = 0.88; 95% confidence interval [CI]: 0.86, 0.90) in vital status and cause of death. Among those cases where both sources reported a death, the OCR had a sensitivity of 95% (95% CI: 90.5, 98.8) and a specificity of 88% (95% CI: 79.6, 92.4). The OCR is a valuable tool for epidemiologic studies of breast cancer to acquire adequate and easily attainable cause-of-death information.