Pence Isaac J, Beaulieu Dawn B, Horst Sara N, Bi Xiaohong, Herline Alan J, Schwartz David A, Mahadevan-Jansen Anita
Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37235, USA.
Division of Gastroenterology, Hepatology & Nutrition, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee 37235, USA.
Biomed Opt Express. 2017 Jan 4;8(2):524-535. doi: 10.1364/BOE.8.000524. eCollection 2017 Feb 1.
Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), affects over 1 million Americans and 2 million Europeans, and the incidence is increasing worldwide. While these diseases require unique medical care, the differentiation between UC and CD lacks a gold standard, and therefore relies on long term follow up, success or failure of existing treatment, and recurrence of the disease. Here, we present colonoscopy-coupled fiber optic probe-based Raman spectroscopy as a minimally-invasive diagnostic tool for IBD of the colon (UC and Crohn's colitis). This pilot study of subjects with existing IBD diagnoses of UC (n = 8), CD (n = 15), and normal control (n = 8) aimed to characterize spectral signatures of UC and CD. Samples were correlated with tissue pathology markers and endoscopic evaluation. The collected spectra were processed and analyzed using multivariate statistical techniques to identify spectral markers and discriminate IBD and disease classes. Confounding factors including the presence of active inflammation and the particular colon segment measured were investigated and integrated into the devised prediction algorithm, reaching 90% sensitivity and 75% specificity to CD from this data set. These results represent significant progress towards improved real-time classification for accurate and automated detection and discrimination of IBD during colonoscopy procedures.
炎症性肠病(IBD),包括溃疡性结肠炎(UC)和克罗恩病(CD),影响着超过100万美国人以及200万欧洲人,且全球发病率正在上升。虽然这些疾病需要独特的医疗护理,但UC和CD之间的鉴别缺乏金标准,因此依赖于长期随访、现有治疗的成败以及疾病的复发情况。在此,我们展示了基于结肠镜耦合光纤探头的拉曼光谱技术,作为一种用于结肠IBD(UC和克罗恩结肠炎)的微创诊断工具。这项针对已确诊为UC(n = 8)、CD(n = 15)的IBD患者以及正常对照(n = 8)的初步研究旨在表征UC和CD的光谱特征。样本与组织病理学标志物及内镜评估相关联。使用多元统计技术对收集到的光谱进行处理和分析,以识别光谱标志物并区分IBD及疾病类别。对包括存在活动性炎症和所测结肠特定节段等混杂因素进行了研究,并将其纳入所设计的预测算法中,从该数据集中对CD的检测灵敏度达到90%,特异性达到75%。这些结果代表了在结肠镜检查过程中朝着改进实时分类以实现IBD的准确自动检测和鉴别取得的重大进展。