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Spatial Bayesian modeling of GLCM with application to malignant lesion characterization
The emerging field of cancer radiomics endeavors to characterize intrinsic patterns of tumor phenotypes and surrogate markers of response by transforming medical images into objects that yield quantifiable summary statistics to which regression and machine learning algorithms may be applied for statistical interrogation. Recent literature has identified clinicopathological association based on textural features deriving from gray-level co-occurrence matrices (GLCM) which facilitate evaluationsdoi:10.6084/m9.figshare.6275144 fatcat:ah6f2d4dyban7crk4uu5xek4jy