Lung cancer is the leading cause of death from cancer for both men and women, in the United States as well as worldwide. Each year in the United States, more people die of lung cancer than of colon, breast, and prostate cancers combined. The most important problem for lung cancer patient care is to develop practical methods to identify the best available therapy for each individual patient. Histopathological classifications of lung cancer have been one of the major criteria guiding lung cancer treatment. For example, the molecular mechanisms and treatment plans are totally different for small-cell and non-small-cell lung cancer. Recent studies show that a more refined classification of non-s...

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Lung cancer is the leading cause of death from cancer for both men and women, in the United States as well as worldwide. Each year in the United States, more people die of lung cancer than of colon, breast, and prostate cancers combined. The most important problem for lung cancer patient care is to develop practical methods to identify the best available therapy for each individual patient. Histopathological classifications of lung cancer have been one of the major criteria guiding lung cancer treatment. For example, the molecular mechanisms and treatment plans are totally different for small-cell and non-small-cell lung cancer. Recent studies show that a more refined classification of non-small-cell lung cancer is significantly associated with patients’ survival outcomes. With the advance of technology, tumor tissue histology slide scanning is already becoming a routine clinical procedure, which produces massive digital pathological images that capture histological details in high resolution. In this study, we will develop computational approaches to predict prognosis and response to chemotherapy for individual lung cancer patients using patient tumor tissue images. We will integrate the patient pathology imaging data with molecular profiling data to further improve prediction accuracy. The prediction models resulting from this project will allow clinicians to test a patient's tumor before therapy in order to identify the therapeutic regimens that would be most effective for that individual patient's tumor. This project will have a large impact on improving the treatment plans for lung cancer patients.

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