On the Feasibility of Predicting Radiological Observations from Computational Imaging Features of Liver Lesions in CT Scans

We aim to predict radiological observations using computationally-derived imaging features extracted from CT images. Our dataset consists of 79 portal venous phase liver CT images containing lesions identified and annotated by a radiologist using a controlled vocabulary of 76 semantic terms. Computationally-derived features were extracted describing intensity, texture, shape, and edge sharpness. Linear discriminative analysis, logistic regression and LASSO were explored to predict the radiological observations using computational features. The approach was evaluated by leave one out cross-validation. Informative radiological observations such as lesion enhancement, hyper vascular attenuation, and homogeneous retention were discovered to be well-predicted by computational features. By exploiting relationships between computable and semantic features, this approach could lead to more accurate and efficient radiology reporting.