Gerber, David E.2021-09-172021-09-172019-082019-07-29August 201https://hdl.handle.net/2152.5/9624Lung cancer is the leading cause of death from cancer. It is remarkably heterogeneous in histopathological features and highly variable in prognosis. Analysis of prognostic factor is anticipated to guide clinicians for treatment selection, enhance patient care, and help understanding biological mechanism of tumor progression. To extend current knowledge about lung cancer prognosis, this dissertation analyzed lung cancer prognostic factors in three levels. First, in tumor level, deep learning aided pathology image analysis was used to extract tumor geometry and microenvironment features, upon which an image-based survival prediction model was built and independently validated for lung adenocarcinoma. Second, in patient level, a nomogram was built with demographic and clinical variables for patients with small cell lung cancer. The nomogram was implemented online for public usage. Third, in population level, how facility type and volume affect survival outcome and surgery selection for early stage non-small cell lung cancer was analyzed.application/pdfenAlgorithmsDeep LearningDiagnostic ImagingImage Processing, Computer-AssistedLung NeoplasmsPathology, ClinicalThesis2021-09-171268338267