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J Thorac Oncol. 2011 Nov;6(11):1841-8. doi: 10.1097/JTO.0b013e31822e59b3.

Cost-effectiveness of computed tomography screening for lung cancer in the United States.

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Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts, USA.



A randomized trial has demonstrated that lung cancer screening reduces mortality. Identifying participant and program characteristics that influence the cost-effectiveness of screening will help translate trial results into benefits at the population level.


Six U.S. cohorts (men and women aged 50, 60, or 70 years) were simulated in an existing patient-level lung cancer model. Smoking histories reflected observed U.S. patterns. We simulated lifetime histories of 500,000 identical individuals per cohort in each scenario. Costs per quality-adjusted life-year gained ($/QALY) were estimated for each program: computed tomography screening; stand-alone smoking cessation therapies (4-30% 1-year abstinence); and combined programs.


Annual screening of current and former smokers aged 50 to 74 years costs between $126,000 and $169,000/QALY (minimum 20 pack-years of smoking) or $110,000 and $166,000/QALY (40 pack-year minimum), when compared with no screening and assuming background quit rates. Screening was beneficial but had a higher cost per QALY when the model included radiation-induced lung cancers. If screen participation doubled background quit rates, the cost of annual screening (at age 50 years, 20 pack-year minimum) was below $75,000/QALY. If screen participation halved background quit rates, benefits from screening were nearly erased. If screening had no effect on quit rates, annual screening costs more but provided fewer QALYs than annual cessation therapies. Annual combined screening/cessation therapy programs at age 50 years costs $130,500 to $159,700/QALY, when compared with annual stand-alone cessation.


The cost-effectiveness of computed tomography screening will likely be strongly linked to achievable smoking cessation rates. Trials and further modeling should explore the consequences of relationships between smoking behaviors and screen participation.

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