In treating patients diagnosed with Stage I non-small-cell lung cancer, doctors must choose between surgery and Adjuvant Cisplatin-Based Chemotherapy (ACT). For patients with resected stages IB to IIIA, clinical trials have shown a survival advantage from 4-15% with the adoption of ACT. However, due to the inherent toxicity of chemotherapy, it is necessary for doctors to identify patients whose chance of success with ACT is sufficient to justify the risks. This project seeks to use gene expression profiling in the development of a statistical decision-making algorithm to identify patients whose survival rates will improve from ACT treatment. Using data from the National Cancer Institute, the Cox-Proportional-Hazards regression model will be used to determine a feasible number of genes that are strongly associated with the treatment-related patient survival. Considering treatment groups separately, patients are assigned a risk category determined by survival time. These risk categories are used to develop a random forest classification model to identify patients who are likely to benefit from chemotherapy treatment. The probability of significant benefit from chemotherapy is then predicted using a regression survival tree. This model allows the prediction of a new patient's prognosis and the likelihood of survival benefit from ACT treatment based on a small number of gene expression levels.
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|Commitee:||Kim, Sung E., Kim-Park, Yong-Hee|
|School:||California State University, Long Beach|
|Department:||Mathematics and Statistics|
|School Location:||United States -- California|
|Source:||MAI 54/05M(E), Masters Abstracts International|
|Keywords:||Gene signature, Lung cancer, Random forest|
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