Lung cancer is the leading cause of cancer death in the United States and worldwide. Majority of lung cancer patients require radiation as part of their treatment. Despite advances in radiation technology, treatment outcomes remain poor, with an overall cure rate of less than 10-15% and moderate toxicity in 10-30% of treated patients. Despite individual difference in their treatment outcome, the treatment is lack of individualization as there is no reliable way to predict which patient will have the cancer controlled and which patient will develop toxicity as a result of receiving a particular dose of radiation therapy to treat their lung cancer. Our preliminary results demonstrated that the during treatment PET scan was correlated with post-treatment response and long-term survival, and V/Q SPECT changed significantly during treatment, and the changes of SPECT and blood markers were predictive of late response. In the proposed clinical trial, we will validate above findings and use them during PET to guide individualized adaptive radiation dose escalation. We will enroll patients who have been diagnosed with non-small-cell lung cancers and are planning to receive radiation therapy as a part of their treatment. At midway during their course of radiation treatments, each patient will undergo PET/CT scan for measuring tumor activity/size and V/Q SPECT scan for mapping lung function. At three points during their course of radiation treatments, each patient will have blood drawn for markers. The results of these tests will be analyzed to determine their ability to accurately predict both the chance of long-term tumor control and the risk for developing lung toxicity. In the late phase of the grant period, we will further adapt the radiation treatment by the use of V/Q SPECT and blood marker during treatment so that the treatment gain could be maximized in each patient.
This study will identify markers to predict treatment outcome for patients with lung cancer. This project will also study the potential for personalized care to improve treatment outcome.
|Luo, Yi; McShan, Daniel L; Matuszak, Martha M et al. (2018) A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy. Med Phys :|
|Wang, Shulian; Campbell, Jeff; Stenmark, Matthew H et al. (2018) A model combining age, equivalent uniform dose and IL-8 may predict radiation esophagitis in patients with non-small cell lung cancer. Radiother Oncol 126:506-510|
|Hawkins, Peter G; Boonstra, Philip S; Hobson, Stephen T et al. (2018) Prediction of Radiation Esophagitis in Non-Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels. Transl Oncol 11:102-108|
|Sun, Yilun; Hawkins, Peter G; Bi, Nan et al. (2018) Serum MicroRNA Signature Predicts Response to High-Dose Radiation Therapy in Locally Advanced Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys 100:107-114|
|Kong, Feng-Ming Spring; Li, Ling; Wang, Weili et al. (2018) Greater reduction in mid-treatment FDG-PET volume may be associated with worse survival in non-small cell lung cancer. Radiother Oncol :|
|Wang, Weili; Huang, Lei; Jin, Jian-Yue et al. (2018) IDO Immune Status after Chemoradiation May Predict Survival in Lung Cancer Patients. Cancer Res 78:809-816|
|Owen, Daniel Rocky; Boonstra, Phillip S; Viglianti, Benjamin L et al. (2018) Modeling Patient-Specific Dose-Function Response for Enhanced Characterization of Personalized Functional Damage. Int J Radiat Oncol Biol Phys 102:1265-1275|
|Kong, Feng-Ming Spring; Zhao, Lujun; Wang, Luhua et al. (2017) Ensuring sample quality for blood biomarker studies in clinical trials: a multicenter international study for plasma and serum sample preparation. Transl Lung Cancer Res 6:625-634|
|Soni, Payal D; Boonstra, Philip S; Schipper, Matthew J et al. (2017) Lower Incidence of Esophagitis in the Elderly Undergoing Definitive Radiation Therapy for Lung Cancer. J Thorac Oncol 12:539-546|
|Luo, Yi; El Naqa, Issam; McShan, Daniel L et al. (2017) Unraveling biophysical interactions of radiation pneumonitis in non-small-cell lung cancer via Bayesian network analysis. Radiother Oncol 123:85-92|
Showing the most recent 10 out of 38 publications