Classification of adrenal lesions through spatial Bayesian modeling of GLCM

Radiomics, an emerging field of quantitative imaging, encompasses a broad class of analytical techniques. Recent literature have interrogated associations between quantitatively derived GLCM-based texture features and clinical/pathology information using machine learning algorithms in many cancer settings, but often fail to elucidate the predictive power of these features. Moreover, for many cancers characterized by complex histopathological profiles, such as adrenocortical carcinoma, reducing the multivariate functional structure of GLCM to a set of summary statistics is potentially reductive, masking the patterns that distinguish malignancy from benignity. We develop a Bayesian probabilistic framework for predictive classification of lesion types, based on the entire GLCM. Our method, which uses a spatial Gaussian random field to model dependencies among neighboring cells of the GLCMs, was applied in a cancer detection context to discriminant malignant from benign adrenal lesions using GLCMs arising from non-contrast CT scans. Our method is shown to yield improved predictive power both in simulations as well as the adrenal CT application when compared to state-of-the-art diagnostic algorithms that use GLCM derived features.