Using Imaging and Molecular Markers to Predict Tumor Response and Lung Toxicity in Lung Cancer
This trial is active, not recruiting.
|Condition||non-small cell lung cancer|
|Sponsor||University of Michigan Cancer Center|
|Start date||May 2007|
|End date||September 2013|
|Trial size||140 participants|
|Trial identifier||NCT00603057, HUM00002913, UMCC 2006.040|
Successful treatment of non-small cell lung cancer with radiation therapy requires that the physicians determine exactly where the tumor is in your body and protect your normal tissue. This study is designed to apply functional imaging, Fluorodeoxyglucose-Positron Emission Tomography (FDG-PET) and Ventilation/Perfusion Single Photon Emission Computerized Tomography (V/Q SPECT), before treatment and then again during treatment to see if it helps predict how well the treatment works for your cancer and how well your lung functions during treatment. A Computerized Tomography (CT) will also be performed along with both of these procedures to help the researchers see clearly where your cancer or your healthy lung is located.
The researchers are also doing blood tests in this study to look for markers in your blood and to see if it helps them in determining your risk of developing side effects from radiation to the lungs. The researchers hope that this study will help them in the future to design radiation treatment plans that provide the best treatment for each individual patient.
The primary aim of this study is to investigate predictive models for long-term tumor control and late treatment lung toxicity by using FDG-PET-CT, V/Q SPECT-CT and blood tests during the course of radiation therapy.
time frame: During treatment with radiation at 40-50 Gy and up to 5 yrs after radiation completed
All participants at least 18 years old.
Inclusion Criteria: - Histologically confirmed Non Small Cell Lung Cancer (NSCLC) or Small Cell Lung Cancer (SCLC) clinically diagnosed providing that FDG-PET is positive. - Stage I to III lung cancer requiring definitive irradiation with or without chemotherapy. - Patients with a locoregional tumor recurrence following surgery will be eligible provided they meet other eligibility criteria. - Patients must be 18 years of age or older. - Female patients with reproductive capability must be willing to use effective contraception - Patients must sign an informed consent form for study. Exclusion Criteria: - Malignant pleural or pericardial effusion. - Pregnancy - Lactation - Patients with diabetes mellitus, with uncontrolled fasting blood glucose level (above 200 mg/dl) - Inability to lie flat for the duration of PET/CT and V/Q SPECT (approximately 45 minutes for each study) - Prisoners are excluded from this study.
|Official title||Using Functional Image and Circulating Molecular Markers to Predict Tumor Response and Lung Toxicity in Treatment of Lung Cancer|
|Principal investigator||Shruti Jolly, M.D.|
|Description||Lung cancer is the leading cause of cancer deaths in the United States, of which 80% are lung cancer (NSCLC, including squamous cell lung cancer, and small cell lung cancer). Although surgery provides the best chance of cure, the majority of lung cancer require radiation for treatment. The current radiation recommendation, using modern techniques and a uniform radiation dose, generates an overall cure rate of less than 10-15%, and moderate toxicity in 10-30% of treated patients. Who can be cured and who will develop side effects? Computed tomography (CT) provides a useful tool to monitor, but a limited power to predict both tumor control and lung toxicity. Using [18F] fluorodeoxyglucose positron emission tomography (FDG-PET) and ventilation/perfusion single photon emission computed tomography (V/Q SPECT), we have recently shown changes in tumor activity and regional lung function during the course of radiation, which may be associated with long-term outcome. The general strategy of this project is to perform functional image and blood test during the course of radiation and correlate them with long-term outcomes. By completing this study, we expect to generate predictive models better than CT-based ones for both tumor control and lung toxicity.|
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