Current management of Crohn?s disease (CD) relies on monitoring objective endpoints of mucosal inflammation. While structural bowel damage drives surgery in more than half of patients with CD, assessments of structural bowel damage are challenging to quantify and incorporate into treatment decision- making. Cross-sectional imaging can survey deep bowel damage and fibrostenotic changes, but the time and expertise needed, and the susceptibility of qualitative features to interobserver variation, pose challenges in the broader use of imaging data to personalize care. The long-term goal of this research is to develop methods to objectively measure structural bowel damage and individualize predictions of clinical outcomes in CD. The overall objectives in this application are to test (i) the ability of computational image analysis methods to collect traditional and novel characterizations of bowel damage using common enterography imaging studies and (ii) to evaluate these measures? ability to improve predictions of CD outcomes. The central hypothesis is that bowel damage features collected by computational image analysis methods will improve the accuracy of models predicting therapeutic and clinical outcomes in CD. This central hypothesis will be tested through three specific aims: (1) Determine the performance of computational analysis of enterography studies capturing bowel damage measurements for predicting CD clinical outcomes in the regular course of care, (2) Prospectively compare the performance of enterography image analysis for predicting therapeutic response to existing laboratory and endoscopic measures, and (3) Evaluate image analysis capacity to determine underlying tissue histology in CD using conventional imaging. In the first aim, enterography studies in a national prospective CD natural history dataset will undergo image analysis to extract measurements used to model surgical, hospitalization, and steroid use outcomes. Further work in this aim will test the agreement between expert radiologists and computer-derived bowel measurements. In the second aim, subjects starting new biologic therapies will undergo scheduled enterography to compare the prognostic capabilities of computationally derived bowel features to inflammatory biomarkers and endoscopy for predicting therapeutic response. Finally, in the third aim, patients undergoing elective surgical resection of intestine for CD will have pre-operative enterography. High dimensional image features will be used to model histologic grading of inflammation and fibrosis. The proposed research is innovative in approaching structural bowel damage as a related, but independent and equally important, companion assessment to inflammation in the prognosis and treatment of CD. Further, computational image analysis opens new horizons not only in objectivity and reproducibility, but also concepts of how to measure CD burden. The proposed research is significant because it will demonstrate the indispensable importance of structural intestinal damage features for the most accurate predictions of CD course and therapeutic responsiveness in both clinical care and therapeutic trials.