Discrimination of liver malignancies with 1064 nm dispersive Raman spectroscopy
Discrimination of liver malignancies with 1064 nm dispersive Raman spectroscopy
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DOI：
10.1364/boe.6.002724
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发表时间：
2015-08-01
影响因子：
3.4
通讯作者：
Mahadevan-Jansen, Anita
中科院分区：
文献类型：
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作者：
Pence, Isaac J.;Patil, Chetan A.;Mahadevan-Jansen, Anita
拉曼光谱已被广泛证明用于组织表征和疾病鉴别，然而目前使用785或830 nm近红外（NIR）激发的实现在具有强烈自身荧光的组织（如肝脏）中无效。在这里，我们报告了使用低噪声铟镓砷（InGaAs）阵列的1064 nm色散拉曼系统来区分健康肝脏、腺癌和肝细胞癌（每组N = 5）的高度自荧光的大块组织离体标本。将得到的光谱与多变量判别算法——稀疏多项式逻辑回归（SMLR）相结合，预测健康和病变组织的类隶属度，并提取出用于鲁棒分类的光谱波段。基于分类输出定义了一种称为特征重要性的定量度量，用于指导光谱特征与健康和病变肝组织的生物指标之间的关联。对健康和肝脏肿瘤标本具有高度特征重要性的光谱波段包括视黄醇、血红素、胆绿素或醌（1595 cm(-1)）；乳酸（838厘米（-1））；胶原蛋白（873厘米（-1））；核酸（1485厘米（-1））。两组病例（正常与肿瘤，100%敏感性和89%特异性）和三组病例（分类准确性：正常89%，腺癌74%，肝细胞癌64%）的分类表现表明了准确区分健康组织和癌组织的潜力，并提示了在肝切除术手术指导中使用拉曼技术的意义。(C) 2015美国光学学会
Raman spectroscopy has been widely demonstrated for tissue characterization and disease discrimination, however current implementations with either 785 or 830 nm near-infrared (NIR) excitation have been ineffectual in tissues with intense autofluorescence such as the liver. Here we report the use of a dispersive 1064 nm Raman system using a low-noise Indium-Gallium-Arsenide (InGaAs) array to discriminate highly autofluorescent bulk tissue ex vivo specimens from healthy liver, adenocarcinoma, and hepatocellular carcinoma (N = 5 per group). The resulting spectra have been combined with a multivariate discrimination algorithm, sparse multinomial logistic regression (SMLR), to predict class membership of healthy and diseased tissues, and spectral bands selected for robust classification have been extracted. A quantitative metric called feature importance is defined based on classification outputs and is used to guide the association of spectral features with biological indicators of healthy and diseased liver tissue. Spectral bands with high feature importance for healthy and liver tumor specimens include retinol, heme, biliverdin, or quinones (1595 cm(-1)); lactic acid (838 cm(-1)); collagen (873 cm(-1)); and nucleic acids (1485 cm(-1)). Classification performance in both binary (normal versus tumor, 100% sensitivity and 89% specificity) and three-group cases (classification accuracy: normal 89%, adenocarcinoma 74%, hepatocellular carcinoma 64%) indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing Raman techniques during surgical guidance in liver resection. (C) 2015 Optical Society of America