A Pilot Study of Response-Driven Adaptive Radiation Therapy for Patients With Locally Advanced Non-Small Cell Lung Cancer

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This is a pilot study to improve local tumor control while maintaining the same rate of treatment toxicity by adapting therapy to the uninvolved lung and esophagus while continuing to adapt therapy to the tumor for patients with Stage II/III NSCLC. Lung cancer is the leading cause of cancer death in...

This is a pilot study to improve local tumor control while maintaining the same rate of treatment toxicity by adapting therapy to the uninvolved lung and esophagus while continuing to adapt therapy to the tumor for patients with Stage II/III NSCLC. Lung cancer is the leading cause of cancer death in the United States and worldwide. In 2012, there were 226,160 new cases and 160,340 deaths related to lung cancer in the United States. Approximately, 80-85% of lung cancers are NSCLC (Non-small Cell Lung Cancer), and 40% of these are locally advanced (stage II/III) at diagnosis. The current standard of care for these patients is "one size fits all" RT (Radiation Therapy) with concurrent chemotherapy in uniform regimens. Even after concurrent chemoradiation, however, the five year overall survival was still about 15%; almost one half of the patients failed locally. At the same time, intensification of both radiotherapy and concurrent chemotherapy may result in excessive toxicity or incomplete treatment. Therefore, it is critical to tailor the treatment to each individual's sensitivity in combination with functional imaging guided response-driven treatment and biomarker guided individualized dose prescription, thus taking into consideration both the tumor and toxicity profile. Evidence suggests that high-dose radiation has the potential to improve local-regional control and overall survival in patients treated with fractionated therapy with concurrent chemotherapy. However, it is challenging to deliver high dose RT in the majority of patients with locally advanced NSCLC without exceeding doses to organs at risk and causing significant side effects. Investigators hypothesized that they could develop safer and more effective therapy by adapting treatment to the individual patient's response. With respect to the tumor, investigators hypothesized, that they could improve outcome by redistributing dose to the more aggressive regions of the tumor, assessed using mid-treatment FDG-PET (Positron Emission Tomography) scanning. With respect to uninvolved organs, investigators need methods of estimating tolerable radiation doses for the individual patient rather than the population average. Such a strategy requires assessing both global and regional normal lung function and the technology to deliver dose in a manner that minimizes damage to functional lung and esophagus. During-RT FDG-PET/CT potentially can provide important benefits to individual patients by intensifying dose to more resistent tumor, allowing early changes to alternative, more efficacious treatment or by avoiding the unnecessary toxicity related to ineffective therapy. Patients will also undergo a during treatment V/Q SPECT (Single-photon Emission Computed Tomography) scan, as an adaptive plan based on during-treatment SPECT may further optimize PART (Personalized Adaptive Radiotherapy) to avoid high dose radiation to the well-functioning regions, and would thus decrease RILT (Radiation Induced Lung Toxicity). The combination of pre- and during V/Q SPECT can classify the lung into different functional regions, and a strategy to give differential priority to the regions has been developed to minimize lung damage. Investigators plan to continue to collect data on serum biomarkers to further refine their biophysical model with the ultimate goal of individualizing radiation dose prescription. By identifying high risk patients and adjusting OAR (Organs at Risk) dose limits to the threshold of tolerance, investigators anticipate a significant reduction in the incidence of toxicity from UMCC 2007.123 (NCT01190527) without compromised tumor control by applying the model to optimize radiation planning.

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