Pixel-Level Clustering Reveals Intra-Tumor Heterogeneity in Non-Small Cell Lung Cancer

摘要

Intra-tumor heterogeneity in non-small 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 intra-tumor heterogeneity using pixel-level multi-resolution clustering on radiomics features of CT images. On a set of pretreatment CT images of NSCLC patients, we observed three high-order image patterns “uni-core”, “multi-core” and “diffused” that can be associated with characteristics of intra-tumor heterogeneity. We checked the clinical records of the two groups of patterns: “uni-core” and “diffused” 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 intra-tumor heterogeneity and to predict patient prognosis based on high-order image patterns.