Lung function impairment in the German Lung Cancer Screening Intervention Study (LUSI): prevalence, symptoms, and associations with lung cancer risk, tumor histology and all-cause mortality.

Background: Lung cancer screening may provide a favorable opportunity for a spirometry examination, to diagnose participants with undiagnosed lung function impairments, or to improve targeting of computed tomography (CT) screening intensity in view of expected net benefit. Methods: Spirometry was performed in the CT screening arm (n=2,029) of the German Lung Cancer Screening Intervention Study (LUSI)-a trial examining the effects of annual CT screening on lung cancer mortality, in 50-69-year-old long-term smokers. Participants were classified as having chronic obstructive pulmonary disease (COPD) [forced expiration in one second (FEV1)/forced vital lung capacity (FVC) <0.7], preserved ratio impaired spirometry (PRISm; FEV1/FVC ≥0.7 and FEV1% predicted <80%), or normal spirometry. Descriptive statistics were used to examine associations of COPD or PRISm with respiratory symptoms, and self-reported medical diagnoses of respiratory and other morbidities. Logistic regression and proportional hazards regression were used to examine associations of COPD and PRISm, as well as their self-reported medical diagnoses, with risks of lung cancer and all-cause mortality. Results: A total of 1,987 screening arm participants (98%) provided interpretable spirometry measurements; of these, 34.3% had spirometric patterns consistent with either COPD (18.6%) or PRISm (15.7%). Two thirds of participants with COPD or PRISm were asymptomatic, and only 23% reported a previous medical diagnosis concordant with COPD. Participants reporting a diagnosis tended to be more often current and heavier smokers, and more often had respiratory symptoms, cardiovascular comorbidities, or more severe lung function impairments. Independently of smoking history, moderate-to-severe (GOLD 2-4) COPD (OR =2.14; 95% CI: 1.54-2.98), and PRISm (OR =2.68; 95% CI: 1.61-4.40), were associated with increased lung cancer risk. Lung cancer patients with PRISm less frequently had adenocarcinomas, and more often squamous cell or small cell tumors, compared to those with normal spirometry (n=45), and both PRISm and COPD were associated with more advanced lung cancer tumor stage for screen-detected cancers. PRISm and COPD, depending on GOLD stage, were also associated with about 2- to 4-fold increases in risk of overall mortality, which to 87 percent had causes other than lung cancer. Conclusions: About one third of smokers eligible for lung cancer screening in Germany have COPD or PRISm. As these conditions were associated with detection of lung cancer, spirometry may help identify populations at high risk for death of lung cancer or other causes, and who might particularly benefit from CT screening.

Validation of multivariable lung cancer risk prediction models for the personalized assignment of optimal screening frequency: a retrospective analysis of data from the German Lung Cancer Screening Intervention Trial (LUSI).

BACKGROUND: Current guidelines for lung cancer screening via low-dose computed tomography recommend annual screening for all candidates meeting basic eligibility criteria. However, lung cancer risk of eligible screening participants can vary widely, and further risk stratification could be used to individually optimize screening intervals in view of expected benefits, possible harms and financial costs. To this effect, models have been developed in the US National Lung Screening Trial based on self-reported lung cancer risk factors and imaging data. We evaluated these models using data from an independent screening trial in Germany. METHODS: We examined the Polynomial model by Schreuder et al., the Lung Cancer Risk Assessment Tool extended by CT characteristics (LCRAT + CT) by Robbins et al., and a criterion of presence vs. absence of pulmonary nodules ≥4 mm (Patz et al.), applied to sub-sets of screening participants according to eligibility criteria. Discrimination was evaluated via the receiver operating characteristic curve. Delayed diagnoses and false positive results were calculated at various thresholds of predicted risk. Model calibration was assessed by comparing mean predicted risk versus observed incidence. RESULTS: One thousand five hundred and six participants were eligible for the validation of the LCRAT + CT model, and 1,889 for the validation of the Polynomial model and Patz criterion, yielding areas under the receiver operating characteristic curve of 0.73 (95% CI: 0.63, 0.82), 0.75 (0.67, 0.83), and 0.56 (0.53, 0.72) respectively. Skipping 50% annual screenings (participants within the 5 lowest risk deciles by LCRAT + CT in any round or by the Polynomial model; baseline screening round), would have avoided 75% (21.9%, 98.7%) and 40% (21.8%, 61.1%) false positive screen tests and delayed 10% (1.8%, 33.1%) or no (0%, 32.1%) diagnoses, respectively. Using the Patz criterion, referring 63.2% (61.0% to 65.4%) of participants to biennial screening would have avoided 4% (0.2% to 22.3%) of false positive screen tests but delayed 55% (24.6% to 81.9%) diagnoses. CONCLUSIONS: In this German trial, the LCRAT + CT and Polynomial models showed useful discrimination of screening participants for one-year lung cancer risk following CT examination. Our results illustrate the remaining heterogeneity in risk within screening-eligible subjects and the trade-off between a low-frequency screening approach and delayed detection.

Can autoantibody tests enhance lung cancer screening?-an evaluation of EarlyCDT®-Lung in context of the German Lung Cancer Screening Intervention Trial (LUSI).

BACKGROUND: Tumor-associated autoantibodies are considered promising markers for early lung cancer detection; so far, however, their capacity to detect cancer has been tested mostly in a clinical context, but not in population screening settings. This study evaluates the early detection accuracy, in terms of sensitivity and specificity, of EarlyCDT®-Lung-a test panel of seven tumor-associated autoantibodies optimized for lung cancer detection-using blood samples originally collected as part of the German Lung Cancer Screening Intervention Trial. METHODS: The EarlyCDT®-Lung test was performed for all participants with lung cancer detected via low-dose computed tomography and with available blood samples taken at detection, and for 180 retrospectively selected cancer-free participants at the end of follow-up: 90 randomly selected from among all cancer-free participants (baseline controls) and 90 randomly selected from among cancer-free participants with suspicious imaging findings (suspicious nodules controls). Sensitivity and specificity of lung cancer detection were estimated in the case group and the two control groups, respectively. RESULTS: In the case group, the test panel showed a sensitivity of only 13.0% (95% CI: 4.9-26.3%). Specificity was estimated at 88.9% (95% CI: 80.5-94.5%) in the baseline control group, and 91.1% (95% CI: 83.2-96.1%) among controls presenting CT-detected nodules. CONCLUSIONS: The test panel showed insufficient sensitivity for detecting lung cancer at an equally early stage as with low-dose computed tomography screening.

Overdiagnosis in lung cancer screening: Estimates from the German Lung Cancer Screening Intervention Trial.

Overdiagnosis is a major potential harm of lung cancer screening; knowing its potential magnitude helps to optimize screening eligibility criteria. The German Lung Screening Intervention Trial ("LUSI") is a randomized trial among 4052 long-term smokers (2622 men), 50.3 to 71.9 years of age from the general population around Heidelberg, Germany, comparing five annual rounds of low-dose computed tomography (n = 2029) with a control arm without intervention (n = 2023). After a median follow-up of 9.77 years postrandomization and 5.73 years since last screening, 74 participants were diagnosed with lung cancer in the control arm and 90 in the screening arm: 69 during the active screening period; of which 63 screen-detected and 6 interval cancers. The excess cumulative incidence in the screening arm (N = 16) represented 25.4% (95% confidence interval: -11.3, 64.3] of screen-detected cancer cases (N = 63). Analyzed by histologic subtype, excess incidence in the screening arm appeared largely driven by adenocarcinomas. Statistical modeling yielded an estimated mean preclinical sojourn time (MPST) of 5.38 (4.76, 5.88) years and a screen-test sensitivity of 81.6 (74.4%, 88.8%) for lung cancer overall, all histologic subtypes combined. Based on modeling, we further estimated that about 48% (47.5% [43.2%, 50.7%]) of screen-detected tumors have a lead time ≥4 years, whereas about 33% (32.8% [28.4%, 36.1%]) have a lead time ≥6 years, 23% (22.6% [18.6%, 25.7%]) ≥8 years, 16% (15.6% [12.2%, 18.3%]) ≥10 years and 11% (10.7% [8.0%, 13.0%]) ≥12 years. The high proportions of tumors with relatively long lead times suggest a major risk of overdiagnosis for individuals with comparatively short remaining life expectancies.

Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography.

Importance: Malignancy prediction models based on participant-related characteristics and imaging parameters from low-dose computed tomography (CT) may improve decision-making regarding nodule management and diagnosis in lung cancer screening. Objective: To externally validate 5 malignancy prediction models that were developed in screening settings, compared with 3 models that were developed in clinical settings, in terms of discrimination and absolute risk calibration among participants in the German Lung Cancer Screening Intervention trial. Design, Setting, and Participants: In this population-based diagnostic study, malignancy probabilities were estimated by applying 8 prediction models to data from 1159 participants in the intervention arm of the Lung Cancer Screening Intervention trial, a randomized clinical trial conducted from October 23, 2007, to April 30, 2016, with ongoing follow-up. This analysis considers end points up to 1 year after individuals' last screening visit. Inclusion criteria for participants were at least 1 noncalcified pulmonary nodule detected on any of 5 annual screening visits, receiving a lung cancer diagnosis within the active screening phase of the Lung Cancer Screening Intervention trial, and an unequivocal identification of the malignant nodules. Data analysis was performed from February 1, 2019, through December 5, 2019. Interventions: Five annual rounds of low-dose multislice CT. Main Outcomes and Measures: Discrimination ability and calibration of malignancy probabilities estimated by 5 models developed in data from screening studies (4 Pan-Canadian Early Detection of Lung Cancer Study [PanCan] models using a parsimonious approach including nodule spiculation [PanCan-1b] or a comprehensive approach including nodule spiculation [PanCan-2b], and PanCan-2b replacing the nodule diameter variable with mean diameter [PanCan-MD] or volume [PanCan-VOL], as well as a model developed by the UK Lung Cancer Screening trial) and 3 models developed in clinical settings (US Department of Veterans Affairs, Mayo Clinic, and Peking University People's Hospital). Results: A total of 1159 participants (median [range] age, 57.63 [50.34-71.89] years; 763 [65.8%] men) with 3903 pulmonary nodules were included in this study. For nodules detected in the prevalence round of CT, the PanCan models showed excellent discrimination (PanCan-1b: area under the curve [AUC], 0.93 [95% CI, 0.87-0.99]; PanCan-2b: AUC, 0.94 [95% CI, 0.89-0.99]; PanCan-MD: AUC, 0.94 [95% CI, 0.91-0.98]; PanCan-VOL: AUC, 0.94 [95% CI, 0.90-0.98]), and all of the screening models except PanCan-MD and PanCan-VOL showed acceptable calibration (PanCan-1b: Spiegelhalter z = -1.081; P = .28; PanCan-2b: Spiegelhalter z = 0.436; P = .67; PanCan-MD: Spiegelhalter z = 3.888; P < .001; PanCan-VOL: Spiegelhalter z = 1.978; P = .05; UK Lung Cancer Screening trial: Spiegelhalter z = -1.076; P = .28), whereas the other models showed worse discrimination and calibration, from an AUC of 0.58 (95% CI, 0.46-0.70) for the UK Lung Cancer Screening trial model to an AUC of 0.89 (95% CI, 0.82-0.97) for the Mayo Clinic model. Conclusions and Relevance: This diagnostic study found that PanCan models showed excellent discrimination and calibration in prevalence screenings, confirming their ability to improve nodule management in screening settings, although calibration to nodules detected in follow-up scans should be improved. The models developed by the Mayo Clinic, Peking University People's Hospital, Department of Veterans Affairs, and UK Lung Cancer Screening Trial did not perform as well.

Lung cancer mortality reduction by LDCT screening-Results from the randomized German LUSI trial.

In 2011, the U.S. National Lung Cancer Screening Trial (NLST) reported a 20% reduction of lung cancer mortality after regular screening by low-dose computed tomography (LDCT), as compared to X-ray screening. The introduction of lung cancer screening programs in Europe awaits confirmation of these first findings from European trials that started in parallel with the NLST. The German Lung cancer Screening Intervention (LUSI) is a randomized trial among 4,052 long-term smokers, 50-69 years of age, recruited from the general population, comparing five annual rounds of LDCT screening (screening arm; n = 2,029 participants) with a control arm (n = 2,023) followed by annual postal questionnaire inquiries. Data on lung cancer incidence and mortality and vital status were collected from hospitals or office-based physicians, cancer registries, population registers and health offices. Over an average observation time of 8.8 years after randomization, the hazard ratio for lung cancer mortality was 0.74 (95% CI: 0.46-1.19; p = 0.21) among men and women combined. Modeling by sex, however showed a statistically significant reduction in lung cancer mortality among women (HR = 0.31 [95% CI: 0.10-0.96], p = 0.04), but not among men (HR = 0.94 [95% CI: 0.54-1.61], p = 0.81) screened by LDCT (pheterogeneity = 0.09). Findings from LUSI are in line with those from other trials, including NLST, that suggest a stronger reduction of lung cancer mortality after LDCT screening among women as compared to men. This heterogeneity could be the result of different relative counts of lung tumor subtypes occurring in men and women.

Longitudinal airway remodeling in active and past smokers in a lung cancer screening population.

OBJECTIVES: To longitudinally investigate smoking cessation-related changes of quantitative computed tomography (QCT)-based airway metrics in a group of heavy smokers. METHODS: CT scans were acquired in a lung cancer screening population over 4 years at 12-month intervals in 284 long-term ex-smokers (ES), 405 continuously active smokers (CS), and 31 subjects who quitted smoking within 2 years after baseline CT (recent quitters, RQ). Total diameter (TD), lumen area (LA), and wall percentage (WP) of 1st-8th generation airways were computed using airway analysis software. Inter-group comparison was performed using Mann-Whitney U test or Student's t test (two groups), and ANOVA or ANOVA on ranks with Dunn's multiple comparison test (more than two groups), while Fisher's exact test or chi-squared test was used for categorical data. Multiple linear regression was used for multivariable analysis. RESULTS: At any time, TD and LA were significantly higher in ES than CS, for example, in 5th-8th generation airways at baseline with 6.24 mm vs. 5.93 mm (p < 0.001) and 15.23 mm2 vs. 13.51 mm2 (p < 0.001), respectively. RQ showed higher TD (6.15 mm vs. 5.93 mm, n.s.) and significantly higher LA (14.77 mm2 vs. 13.51 mm2, p < 0.001) than CS after 3 years, and after 4 years. In multivariate analyses, smoking status independently predicted TD, LA, and WP at baseline, at 3 years and 4 years (p < 0.01-0.001), with stronger impact than pack years. CONCLUSIONS: Bronchial dimensions depend on the smoking status. Smoking-induced airway remodeling can be partially reversible after smoking cessation even in long-term heavy smokers. Therefore, QCT-based airway metrics in clinical trials should consider the current smoking status besides pack years. KEY POINTS: • Airway lumen and diameter are decreased in active smokers compared to ex-smokers, and there is a trend towards increased airway wall thickness in active smokers. • Smoking-related airway changes improve within 2 years after smoking cessation. • Smoking status is an independent predictor of airway dimensions.

Effect of smoking cessation on quantitative computed tomography in smokers at risk in a lung cancer screening population.

OBJECTIVE: To longitudinally evaluate effects of smoking cessation on quantitative CT in a lung cancer screening cohort of heavy smokers over 4 years. METHODS: After 4 years, low-dose chest CT was available for 314 long-term ex-smokers (ES), 404 continuous smokers (CS) and 39 recent quitters (RQ) who quitted smoking within 2 years after baseline CT. CT acquired at baseline and after 3 and 4 years was subjected to well-evaluated densitometry software, computing mean lung density (MLD) and 15th percentile of the lung density histogram (15TH). RESULTS: At baseline, active smokers showed significantly higher MLD and 15TH (-822±35 and -936±25 HU, respectively) compared to ES (-831±31 and -947±22 HU, p<0.01-0.001). After 3 years, CS again had significantly higher MLD and 15TH (-801±29 and -896±23 HU) than ES (-808±27 and -906±20 HU, p<0.01-0.001) but also RQ (-813±20 and -909±15 HU, p<0.05-0.001). Quantitative CT parameters did not change significantly after 4 years. Importantly, smoking status independently predicted MLD at baseline and year 3 (p<0.001) in multivariate analysis. CONCLUSION: On quantitative CT, lung density is higher in active smokers than ex-smokers, and sustainably decreases after smoking cessation, reflecting smoking-induced inflammation. Interpretations of quantitative CT data within clinical trials should consider smoking status. KEY POINTS: • Lung density is higher in active smokers than ex-smokers. • Lung density sustainably decreases after smoking cessation. • Impact of smoking cessation on lung density is independent of potentially confounding factors. • Smoke-induced pulmonary inflammation and particle deposition influence lung density on CT.