Radiology utilizes various imaging technologies such as X-ray radiography, ultrasound, computed tomography, positron emission tomography, and magnetic resonance, to diagnose and treat diseases. It is an extremely valuable clinical tool, especially when integrated with pathology, which examines tissue samples obtained through surgical procedures. Radiology and pathology offer two slightly different perspectives, and this can result in very interesting discoveries. Recently, a new type of liver cancer called Hepatocellular Carcinoma (HCC) was identified in pathology, but has yet to be described in radiology. Radiologists do not know what specific image features distinguish this new type of HCC, and clinical imaging tests have completely missed it in the past. This presents a serious concern, as these distinct varieties of HCC require unique treatments. Moreover, HCC is the most common liver cancer, and is the 3rd leading cause of cancer deaths worldwide. The objective of this project is to isolate and characterize this newly-discovered type of HCC through the use of texture analysis and machine learning on certain regions of interest gathered from the radiological scans of cancer patients in the Mayo Clinic database. The results of this research will allow medical professionals to apply more targeted and effective treatments.