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

描述溃疡性结肠炎患者炎症特征的多模式模式分析 - 疾病活动成像和预测临床缓解

• 批准号: 289488758
• 负责人: Professor Dr. Thomas Bocklitz
• 金额: --
• 依托单位: Klinik für Innere Medizin IV: Gastroenterologie, Hepatologie, Infektiologie, Interdisziplinäre Endoskopie)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/289488758?language=en
• 关键词: Multimodal; pattern; analysis; characterize; inflammation

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.

评估和量化大肠的炎症是至关重要的，以允许个性化治疗慢性炎症性肠病。尽管粘膜病变的愈合与良好的预后相关，并可能导致当前治疗的停止，但炎性病变的检测可能会促使强化治疗。然而，评估结肠中的炎症活性是困难的，并且基于研究者的评估。因此，这里是一个迫切需要的炎症粘膜病变的非依赖性定量。根据我们以前的研究结果，我们在此提出使用拉曼光谱来识别溃疡性结肠炎患者肠上皮细胞中的炎症模式。我们将通过模式识别分析量化炎症，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。将评估治疗前和药物治疗3个月后获得的患者活检影像学数据预测临床缓解的能力。为了以更快的方式评估更大面积的粘膜，我们将应用多模态成像结合双光子激发显微镜（TPEM），二次谐波发生（SHG）和相干反斯托克斯拉曼光谱（汽车），以铺平道路，炎症性肠病的内窥镜分子成像在体内实时。