In this paper, we have proposed a framework for lung cancer survival prediction by integrating genetic data and pathological images. Since molecular profiles and pathological images reveal complementary information on tumor characteristics, the integration will benefit the survival analysis. The gene expression signatures are processed using Model-Based Background Correction method. A robust cell detection and segmentation method is applied to segment each individual cell from pathological images to extract the image features. Based on the cell detection results, a set of extensive features are extracted using efficient geometry and texture descriptors. The supervised principal component regression model is fitted to evaluate the proposed framework. Experimental results demonstrate strong prediction power of the statistical model built from the integration of genetic data and pathological images compared with using only one of the two types of data alone.
['Xinliang Zhu', 'Jiawen Yao', 'Xin Luo', 'Guanghua Xiao', 'Yang Xie', 'Adi F. Gazdar', 'Junzhou Huang']
Lung cancer survival prediction from pathological images and genetic data — An integration study