Advances in medical imaging and screening tests have made possible the detection and diagnosis of many diseases in their early stages. Those advances have enabled more effective planning, execution, and monitoring of a treatment plan. However, early detection has also resulted in an increase of the number of longitudinal radiographs requested for most patients, thus increasing the risk for potential long-term effects of ionizing radiation exposure and increasing the cost associated with a specific treatment plan. The aim of this paper is to study the associations between clinical measurements and quantitative image features in patients with pulmonary fibrosis. The association between these multi-modal features could be used to more accurately determine the state of the disease and could potentially be used to predict many of the longitudinal image features when CT images are not available. Our results show how textural image features are highly correlated with the severity of fibrosis, how clinical variables can be combined to monitor progression, and how simple blood features can be used to predict statistical image attributes of the lungs.