PROJECT SUMMARY The proposed study concerns radiation therapy for lung cancer. Lung tumors are usually surrounded, at least partially, by lung tissue that has a much lower density than the tumor. In external photon-beam radiotherapy, the lung-tumor interface is characterized by disequilibrium of secondary electrons that results in dose build-up and build-down regions near the tumor surface, where the beam enters and exits it. This translates into underdosage of tumor surface, and potentially compromises tumor control. In routine treatment planning this underdosage is largely undetected. Even though presently 4DCT images are acquired for most lung cancer patients, these images are used primarily to determine the extent of tumor motion. Dose calculations, on the other hand, are typically performed on a CT image averaged over the respiratory cycle. Because the tumor moves, such averaging smears the lung-tumor interface, leading to dose calculation errors in superficial regions of the tumor that may compromise treatment. The proposed study will first evaluate the magnitude of the underdosage and its clinical significance in terms of tumor control probability. This will be accomplished by utilizing a methodology that offers two important improvements over the standard treatment planning techniques: (a) dose distributions will be calculated separately for each of 10 phases of the respiratory cycle, and then combined so as to evaluate the spatial distribution of the dose delivered to the tumor; (b) calculations will be performed with Monte Carlo, the most advanced dose calculation algorithm available. Second, we will test the hypothesis that the use of flattening filter-free beams improves tumor control probability. Previous treatment planning studies have demonstrated that such treatments are feasible and that dose in build-up regions is higher owing to a softer photon spectrum. Our research strategy has three specific aims: (1) evaluate the magnitude of the underdosage in a simple model of a lung tumor, namely a spherical static tumor surrounded by lung tissue, calculating doses with Monte Carlo; (2) evaluate the magnitude of the underdosage for a cohort of 15 previously treated patients, calculating dose distributions retrospectively with Monte Carlo for each of the 10 respiratory phases separately, and then combining them; (3) compare predicted tumor control probabilities between treatments with conventional photon beams and flattening filter free beams. For this purpose, for each patient in the cohort a new treatment plan will be generated using flattening filter free beams.