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.