In vivo diagnosis of gastric cancer using Raman endoscopy and ant colony optimization techniques
In vivo diagnosis of gastric cancer using Raman endoscopy and ant colony optimization techniques
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DOI：
10.1002/ijc.25618
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发表时间：
2011-06-01
影响因子：
6.4
通讯作者：
Huang, Zhiwei
中科院分区：
文献类型：
--
作者：
Bergholt, Mads Sylvest;Zheng, Wei;Huang, Zhiwei
本研究旨在评估图像引导拉曼内窥镜在胃镜检查中体内诊断胃肿瘤病变的临床实用性。开发了一种具有 785 nm 激发的快速采集图像引导拉曼内窥镜系统，可在临床胃镜检查期间在 0.5 秒内获取体内胃组织拉曼光谱。从67名胃病患者的238个组织部位总共获得了1063个体内拉曼光谱，其中934个拉曼光谱来自正常组织，129个拉曼光谱来自肿瘤胃组织。基于群体智能的算法（即蚁群优化（ACO）与线性判别分析（LDA）相结合）被开发用于光谱变量选择，以识别生化重要的拉曼谱带，以区分正常胃组织和肿瘤性胃组织。 ACO-LDA 算法与留一组织位点排除、交叉验证方法一起在 850-875、1,090-1,110、1,120-1,130、1,170-1,190、1,320-1,340、1,655-1,665 和1,730-1,745 cm(-1) 与组织的蛋白质、核酸和脂质相关，对胃肿瘤的诊断敏感性为94.6%，特异性为94.6%。独立测试验证数据集（总数据集的 20%）的预测灵敏度为 89.3%，特异性为 97.8%。这项工作首次证明，与 ACO-LDA 诊断算法相关的实时图像引导拉曼内窥镜检查具有在临床胃镜检查过程中对胃肿瘤进行无创体内诊断和检测的潜力。
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.