Introduction: Several authors have expressed the view that patients with oral lichen planus (OLP) are at increased risk of developing oral cancer. Since OLP cannot be effectively treated, regular screening for the possible development of oral cancer might be considered., Objectives: (i) To calculate costs and effectiveness of screening for oral cancer in OLP patients with a decision model; (ii) to compare the cost-effectiveness of different screening scenarios; and (iii) to perform a sensitivity analysis of several variables used in this model., Methods: Costs and effectiveness of a population of 100,000 OLP patients, being either screened or not screened for oral cancer, were calculated for the period of 1 year. Health gain was expressed as quality adjusted live years (QALY's) and equivalent lives saved (ELS). Cost-effectiveness was expressed as extra costs (costs of screening minus costs of no screening) per ELS. Then, the outcome was compared with the cost-effectiveness of a different screening scenario. Finally, the effect of varying the variables: (i) costs of cancer treatment; (ii) annual malignant transformation rate (MTR); (iii) sensitivity and specificity of an oral examination; and (iv) proportion of cancers found in stage I on extra costs per ELS were assessed in a sensitivity analysis., Results: The health gain from screening was 592 QALY's or the equivalent of 23.68 lives saved, costing 1,265,229 dollars, meaning that one ELS costed 53,430 dollars. Increase of cancer-treatment costs will significantly decrease the costs per ELS. When the MTR is lower than 0.4% per year, extra costs per ELS will increase exponentially. The effect of sensitivity and specificity of an oral examination in detecting oral cancer on cost-effectiveness seems to be substantial. When the proportion of cancers found in stage I can be increased from 40% (without screening) up to at least 60% after screening, extra costs per ELS will decrease exponentially., Conclusions: Screening for oral cancer in OLP patients, based on the presently used model, seems attractive. However, varying the several variables in the decision model has a significant impact on the final costs and effectiveness. Only, when additional information about these variables will become available, a more precise and realistic calculation can be performed.