Cam-type femoroacetabular impingement (FAI) results in altered biomechanics and acetabular pathology that has been associated with osteoarthritis of the hip. These early changes can be difficult to detect with routine clinical imaging. Texture analysis offers a more quantitative approach for characterizing gray-level patterns. The purpose of this study was to determine the MRI texture profile of acetabular subchondral bone in normal, asymptomatic cam positive and symptomatic cam-FAI hips with the assistance of gradient-boosted decision trees. This work demonstrates that MRI textural features can be used to generate machine learning models that can identify cam positive hips, regardless of symptom status.