Abstract

Recent studies with lung MRI (MRI) have shown high sensitivity (Sn) and specificity (Sp) for lung nodule detection and characterization relative to low-dose CT (LDCT). Using this background data, we sought to compare the potential screening performance of MRI vs. LDCT using a Markov model of lung cancer screening. We created a Markov cohort model of lung cancer screening which incorporated lung cancer incidence, progression, and mortality based on gender, age, and smoking burden. Sensitivity (Sn) and Sp for LDCT were taken from the MISCAN Lung Microsimulation and Sn/Sp for MRI was estimated from a published substudy of the German Lung Cancer Screening and Intervention Trial. Screening, work-up, and treatment costs were estimated from published data. Screening with MRI and LDCT was simulated for a cohort of male and female smokers (2 packs per day; 36 pack/years of smoking history) starting at age 60. We calculated the screening performance and cost-effectiveness of MRI screening and performed a sensitivity analysis on MRI Sn/Sp and cost. There was no difference in life expectancy between MRI and LDCT screening (males 13.28 vs. 13.29 life-years; females 14.22 vs. 14.22 life-years). MRI had a favorable cost-effectiveness ratio of $258,169 in men and $403,888 in women driven by fewer false-positive screens. On sensitivity analysis, MRI remained cost effective at screening costs < $396 dollars and Sp > 81%. In this Markov model of lung cancer screening, MRI has a near-equivalent life expectancy benefit and has superior cost-effectiveness relative to LDCT. • In this Markov model of lung cancer screening, there is no difference in mortality between yearly screening with MRI and low-dose CT. • Compared to low-dose CT, screening with MRI led to a reduction in false-positive studies from 26 to 2.8% in men and 26 to 2.6% in women. • Due to similar life-expectancy and reduced false-positive rate, we found a favorable cost-effectiveness ratio of $258,169 in men and $403,888 in women of MRI relative to low-dose CT.

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