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Lung cancer is one of the most common causes of cancer-related death in men and women throughout the world. An appropriate
statistical model for survival analysis can provide assistance for treatment for lung cancer. The previous prognostic decision
usually comes from manual pathologic interpretation of whole-slide microscopic image by skilled pathologist, which is obviously
time consuming and biased. Currently, there is no standard way in which the feature information of microscopic image is applied
into the diagnosis and therapy. In this paper, we propose an integrated framework for so-called image-based unbiased system that
get prognostic decisions as output automatically when we input patients? images, which includes cell detection, segmentation, and
statistical model for survival analysis. 121 patients information from TCGA has been used. We present a robust seed detection-
based cell segmentation algorithm, which can accurately segment out individual cells. Based on cell segmentation, a set of
cellular features are extracted using efficient feature descriptors. Due to the existence of high dimension of the data, we apply L1
regularization Cox model and surely independent screening (SIS) to reduce dimension in survival, then we chose the cut off of
risk score by minimize AUC. Patients were classified into two groups (low-risk and high risk) and achieve significant difference
of survival. Testing data set from UK clinical hospital was used to observe model performance on independent data.
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