To improve the local control rate of lung radiation therapy, higher radiation dose is needed but limited by the risk of symptomatic radiation induced lung injury. Novel methods to predict radiation-induced lung toxicity have the potential to allow for dose escalation based on patient specific pre-radiation pulmonary biomechanical properties. In this proposal, we hypothesize that the ventilation is a biomechanical problem solvable by finite element analysis. We further hypothesize that the change in the ventilation can be predicted based on the increased rigidity of fibrotic lung tissue as a result of radiation therapy. We designed a prospective study to test these hypotheses.
Lung cancer is the leading cause of cancer death in the United States with 161,840 deaths attributed to lung cancer in 2008 (American Cancer Society). Radiation therapy in combination with chemotherapy is the standard of care for patients with locally advanced lung tumor. However, the combined therapy can result in severe side effects. The proposed project will help to predict and to minimize the risk by a patient specific approach.
|Gou, Shuiping; Wang, Yueyue; Wu, Jiaolong et al. (2015) Lung dynamic MRI deblurring using low-rank decomposition and dictionary learning. Med Phys 42:1917-25|
|Sarma, Manoj; Hu, Peng; Rapacchi, Stanislas et al. (2014) Accelerating dynamic magnetic resonance imaging (MRI) for lung tumor tracking based on low-rank decomposition in the spatial-temporal domain: a feasibility study based on simulation and preliminary prospective undersampled MRI. Int J Radiat Oncol Biol Phys 88:723-31|