PixelLevel Clustering Reveals IntraTumor Heterogeneity in NonSmall Cell Lung Cancer Intratumor heterogeneity in nonsmall cell lung cancer (NSCLC) is a current topic of many genomic studies, but there is no effective technology for characterizing tumor heterogeneity before surgery or biopsy. Since computed tomography (CT) is a standard technology for the diagnosis of lung cancer, it will be of great scientific and clinical use if tumor heterogeneity information can be extracted from CT images. In this work, we proposed a strategy for studying intratumor heterogeneity using pixellevel multiresolution clustering on radiomics features of CT images. On a set of pretreatment CT images of NSCLC patients, we observed three highorder image patterns xe2x80x9cunicorexe2x80x9d, xe2x80x9cmulticorexe2x80x9d and xe2x80x9cdiffusedxe2x80x9d that can be associated with characteristics of intratumor heterogeneity. We checked the clinical records of the two groups of patterns: xe2x80x9cunicorexe2x80x9d and xe2x80x9cdiffusedxe2x80x9d patterns that have enough samples, and found significant difference in their survival time. The results showed the potential of using radiomic analysis on CT images to characterize intratumor heterogeneity and to predict patient prognosis based on highorder image patterns.

Texturebased Deep Learning for Effective Histopathological Cancer Image Classification Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques, since manual examination and diagnosis with WSIs are time and costconsuming. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. However, despite the success of the development, there are still opportunities for further enhancements. In this paper, we propose a novel cancer texturebased deep neural network (CATNet) that learns scalable morphological features from histopathological WSIs. The innovation of CATNet is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) improving predictive performance while reducing model complexity. Moreover, CATNet can provide discriminative morphological (texture) patterns formed on cancerous regions of histopathological images comparing to normal regions. We elucidated how our proposed method, CATNet, captures morphological patterns of interest in hierarchical levels in the model. The proposed method outperformed the current stateoftheart benchmark methods on accuracy, precision, recall, and F1 score.