Multimodal pattern analysis to characterize inflammation in patients with ulcerative colitis - Imaging disease activity and predicting clinical remission

点击翻译按钮获取中文摘要

Assessing and quantifying inflammation of the large intestine is of critical importance in order to allow personalized treatment for chronic inflammatory bowel disease. Whereas the healing of mucosal lesions is associated with a good prognosis and may result in the cessation of current therapies, detection of inflammatory lesions may prompt intensified therapies. However, assessing inflammatory activity in the colon is difficult and based on the investigator's evaluation. Thus, here is an urgent need for investigator-independent quantification of inflammatory mucosal lesions. Following the results of our previous studies, we herein propose to identify inflammatory patterns in enterocytes from patients with ulcerative colitis using Raman spectroscopy. We will quantify inflammation by pattern recognition analysis and apply classification models in order to distinguish different grades of inflammatory changes to predict mucosal healing. Imaging data from patients' biopsies obtained before treatment and after 3 months of medical therapy will be assessed regarding their capability to predict clinical remission. To assess larger areas of the mucosa in a faster fashion, we will apply multimodal imaging combining two-photon excitation microscopy (TPEM), second harmonic generation (SHG) and coherent anti-Stokes Raman spectroscopy (CARS) in order to pave the way for endoscopic molecular imaging of inflammatory bowel diseases in real-time in vivo.