Abstract: Embodiments discussed herein facilitate predicting response to therapy in Crohn's disease. A first set of embodiments discussed herein relates to accessing a radiological image of a region of tissue demonstrating Crohn's disease associated with a patient; defining a mesenteric fat region by segmenting mesenteric fat represented in the radiological image; extracting a set of radiomic features from the mesenteric fat region; providing the set of radiomic features to a machine learning classifier configured to compute a probability of response to therapy in Crohn's disease based, at least in part, on the set of radiomic features; receiving, from the machine learning classifier, a probability that the region of tissue will respond to therapy; generating a classification of the patient as a responder or non-responder based, at least in part, on the probability; and displaying the classification.

Abstract: Embodiments discussed herein facilitate predicting response to therapy in Crohn's disease. A first set of embodiments discussed herein relates to accessing a radiological image of a region of tissue demonstrating Crohn's disease associated with a patient; defining a mesenteric fat region by segmenting mesenteric fat represented in the radiological image; extracting a set of radiomic features from the mesenteric fat region; providing the set of radiomic features to a machine learning classifier configured to compute a probability of response to therapy in Crohn's disease based, at least in part, on the set of radiomic features; receiving, from the machine learning classifier, a probability that the region of tissue will respond to therapy; generating a classification of the patient as a responder or non-responder based, at least in part, on the probability; and displaying the classification.