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.
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.
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.
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.
•USPSTF recommendations do not account for race and sex differences in lung cancer.•Using Georgetown tumor registry, we identified lung cancer cases between 2014 and 2018.•Compared lung screening ...eligibility using USPSTF2013 criteria vs. PLCOm2012 model.•Compared sensitivity for finding cases eligible for screening overall, and by race/sex.•PLCOm2012 selected a larger proportion of lung cancer cases in all race-sex strata.
The United States Preventive Services Task Force (USPSTF) recommendations do not account for race and sex differences in lung cancer risk. We compared the sensitivity for finding lung cancer cases eligible for lung cancer screening (LCS) by USPSTF 2013 recommendations versus the PLCOm2012 model at an equivalent threshold.
Using Georgetown University Hospital tumor registry, we identified lung cancer cases (≥55 years old) between 2014 and 2018. Medical chart review collected age, sex, race, education, smoking, and clinical characteristics. We compared the percentage meeting eligibility criteria overall, and by race and sex.
The cases (N = 447) were 36.6% Black and 52.6% female. The PLCOm2012 and USPSTF 2013 criteria identified 71.4% and 45.6% of cases, respectively (p < 0.0001). This difference was consistent across race and sex sub-groups (p < 0.0001). The PLCOm2012 was more sensitive than the USPSTF in Blacks (69.9% vs. 46.6%, p < 0.0001) and in women (69.8% vs. 41.3%, p < 0.0001). The USPSTF had poor sensitivity for both race groups (Black 46.6%, White 45.9%, p = 0.886) and had lower sensitivity in women vs. men (41.3% vs. 51.4%, p = 0.032). The PLCOm2012 had higher sensitivities in women and men, and difference between sexes was not significant (69.8% vs. 72.6%, p = 0.506).
Compared to the USPSTF 2013 recommendations, the PLCOm2012 model selected a larger proportion of lung cancer cases in all race-sex strata and removed the sex disparity observed for the USPSTF. The PLCOm2012 risk model could be used to identify those who will benefit from LCS.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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.
Given the rapidly growing number of lung cancer survivors who are now at high risk of developing second primary lung cancer (SPLC), previous studies have identified SPLC risk factors and developed SPLC risk‐prediction models, but they lack insight into real‐world validation to help improve clinical decision‐making in SPLC surveillance for lung cancer survivors. Using a large, hospital‐based real‐world cohort of lung cancer survivors, the authors validated the predictive accuracy of an SPLC risk‐prediction model (area under the curve of 0.81), that can identify high‐risk lung cancer survivors for SPLC and can be incorporated into clinical decision‐making for SPLC surveillance to improve the systematic management of lung cancer survivors.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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).
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.
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.
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.
Low-dose computed tomography lung cancer screening is most effective when applied to high-risk individuals.
To develop and validate a risk prediction model that incorporates low-dose computed ...tomography screening results.
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.
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.
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.
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.
ClinicalTrials.gov Identifier: NCT00047385.
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.
BACKGROUNDThe PLCOm2012 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 PLCOm2012 in a cohort of Quebec residents, and to determine the hypothetical performance of different screening strategies. METHODSWe included smokers without a history of lung cancer from the population-based CARTaGENE cohort. To assess PLCOm2012 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 PLCOm2012 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. RESULTSAmong 11 652 participants, 176 (1.51%) lung cancers were diagnosed in 6 years. The PLCOm2012 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 PLCOm2012 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 PLCOm2012 with a 2.00% threshold at 6.0%, 95% CI 4.8%-7.3%). INTERPRETATIONIn a cohort of Quebec smokers, the PLCOm2012 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.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Abstract
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.
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.
Full text
Available for:
GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
•Using the PLCOm2012 risk model saves costs and improves outcomes compared with age-smoking history categorical methods.•The risk model mitigated gender-based and socioeconomic disparities in access ...to lung screening.•The PLCOm2012 risk model was most cost-effective for females and at a 1.5 %/6 year threshold for both sexes.•Our findings call for deeper inquiry into screening eligibility processes through a sex and gender plus-based lens.
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).
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).
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).
The PLCOm2012 model saved costs, increased QALYs and mitigated socioeconomic and sex-based disparities in access to screening.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PDF
