The overall goal of this research program is to develop a novel magnetic resonance (MR) image analysis method that can distinguish between malignant and benign lung nodules in patients who are undergoing diagnostic evaluation for lung cancer. 3TP Imaging Sciences has developed MR image analysis software that can be used to characterize the status of small tumors by analyzing the pharmacokinetics of contrast agent that has been introduced into target tissues. The key to this approach is the use of computational modeling to identify three time points (hence, 3TP) that best discriminate between malignant and normal or benign tissue. Two products have been cleared by the FDA and commercialized to aid in the diagnosis of breast cancer and prostate cancer. This proposal, in response to PA-04-094, proposes a strategy to optimize this novel in vivo image enhancement method to help diagnose lung cancer. The objective of this Fast-Track Phase I project is to overcome the technical hurdles to establishing the feasibility of the 3TP approach for lung cancer. Aim 1 will establish an electronic work environment for data acquisition, analysis and sharing between the collaborative sites. Aim 2 will optimize a motion correction algorithm to reduce respiratory artifacts during the scanning process. Aim 3 will optimize data acquisition parameters for Dynamic Contrast-Enhanced MRI (DCE-MRI) of the lung. Aim 4 will identify three time points that best characterize the status of previously identified pulmonary nodules in patients who are under- going surgical resection. Image analysis results will be compared with the "gold standard" for diagnosis of lung cancer: surgical histopathology. Successful completion of this Phase I study will lead to a Phase II study, described in the accompanying proposal, to validate the optimized MR image analysis platform with a multi-site clinical study in patients who have been scheduled for surgical resection of suspicious lung nodules. Relevance: Lung cancer is the most common cause of cancer, but early detection is problematic because the current radiographic screening methods are unreliable for very small tumors and require exposure to ionizing radiation. Surgical follow-up of suspicious lung nodules reveals that half of the nodules are benign, which means that many patients are exposed to excess morbidity, mortality and cost from the surgery itself. Successful completion of this Fast-Track project would result in the commercialization of a robust, minimally invasive MR imaging platform to help diagnose early-stage lung cancer.
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