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

评估和量化大肠炎症对于实现对慢性炎症性肠病的个体化治疗至关重要。虽然粘膜损伤的愈合与良好的预后相关，并可能导致当前治疗的停止，但检测到炎性损伤可能会促使强化治疗。然而，评估结肠中的炎症活动是困难的，而且是基于研究人员的评估。因此，迫切需要对炎性粘膜损伤进行独立于研究者的量化。根据我们以前的研究结果，我们在这里建议使用拉曼光谱来识别溃疡性结肠炎患者肠细胞中的炎症模式。我们将通过模式识别分析对炎症进行量化，并应用分类模型来区分不同级别的炎症变化，以预测粘膜愈合。治疗前和药物治疗3个月后患者活检的影像数据将被评估他们预测临床缓解的能力。为了更快地评估更大面积的粘膜，我们将应用结合双光子激发显微镜(TPEM)、二次谐波产生(SHG)和相干反斯托克斯拉曼光谱(CARS)的多模式成像，为体内实时炎症性肠病的内窥镜分子成像铺平道路。

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.