Optical Bioimaging Laboratory, Department of Bioengineering, Faculty of Engineering, National University of Singapore, Singapore.
Int J Cancer. 2011 Jun 1;128(11):2673-80. doi: 10.1002/ijc.25618. Epub 2010 Oct 8.
This study aims to evaluate the clinical utility of image-guided Raman endoscopy for in vivo diagnosis of neoplastic lesions in the stomach at gastroscopy. A rapid-acquisition image-guided Raman endoscopy system with 785-nm excitation has been developed to acquire in vivo gastric tissue Raman spectra within 0.5 sec during clinical gastroscopic examinations. A total of 1,063 in vivo Raman spectra were acquired from 238 tissue sites of 67 gastric patients, in which 934 Raman spectra were from normal tissue whereas 129 Raman spectra were from neoplastic gastric tissue. The swarm intelligence-based algorithm (i.e., ant colony optimization (ACO) integrated with linear discriminant analysis (LDA)) was developed for spectral variables selection to identify the biochemical important Raman bands for differentiation between normal and neoplastic gastric tissue. The ACO-LDA algorithms together with the leave-one tissue site-out, cross validation method identified seven diagnostically important Raman bands in the regions of 850-875, 1,090-1,110, 1,120-1,130, 1,170-1,190, 1,320-1,340, 1,655-1,665 and 1,730-1,745 cm(-1) related to proteins, nucleic acids and lipids of tissue and provided a diagnostic sensitivity of 94.6% and specificity of 94.6% for distinction of gastric neoplasia. The predictive sensitivity of 89.3% and specificity of 97.8% were also achieved for an independent test validation dataset (20% of total dataset). This work demonstrates for the first time that the real-time image-guided Raman endoscopy associated with ACO-LDA diagnostic algorithms has potential for the noninvasive, in vivo diagnosis and detection of gastric neoplasia during clinical gastroscopy.
本研究旨在评估图像引导的拉曼内镜在胃镜检查中对胃内肿瘤性病变进行体内诊断的临床实用性。我们开发了一种快速采集的图像引导的拉曼内镜系统，该系统采用 785nm 激发光，可在临床胃镜检查过程中 0.5 秒内采集胃组织的拉曼光谱。共从 67 名胃癌患者的 238 个组织部位采集了 1063 个活体拉曼光谱，其中 934 个光谱来自正常组织，而 129 个光谱来自肿瘤性胃组织。基于群体智能的算法（即蚁群优化（ACO）与线性判别分析（LDA）的集成）被开发用于光谱变量选择，以识别区分正常和肿瘤性胃组织的生物化学重要拉曼带。ACO-LDA 算法以及组织的留一组织位点外交叉验证方法，在 850-875、1090-1110、1120-1130、1170-1190、1320-1340、1655-1665 和 1730-1745cm(-1) 区域中确定了七个具有诊断意义的拉曼带，这些拉曼带与组织中的蛋白质、核酸和脂质有关，为区分胃癌提供了 94.6%的敏感性和 94.6%的特异性。在一个独立的测试验证数据集（总数据集的 20%）中，也实现了 89.3%的预测敏感性和 97.8%的特异性。本研究首次证明，实时图像引导的拉曼内镜与 ACO-LDA 诊断算法相结合，具有在临床胃镜检查中进行非侵入性、体内诊断和检测胃肿瘤的潜力。
