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首页> 外文期刊>Chemistry: A European journal >Multivariate statistical identification of human bladder carcinomas using ambient ionization imaging mass spectrometry
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Multivariate statistical identification of human bladder carcinomas using ambient ionization imaging mass spectrometry

机译：环境电离成像质谱法对人膀胱癌的多变量统计鉴定

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摘要

Diagnosis of human bladder cancer in untreated tissue sections is achieved by using imaging data from desorption electrospray ionization mass spectrometry (DESI-MS) combined with multivariate statistical analysis. We use the distinctive DESI-MS glycerophospholipid (GP) mass spectral profiles to visually characterize and formally classify twenty pairs (40 tissue samples) of human cancerous and adjacent normal bladder tissue samples. The individual ion images derived from the acquired profiles correlate with standard histological hematoxylin and eosin (H&E)-stained serial sections. The profiles allow us to classify the disease status of the tissue samples with high accuracy as judged by reference histological data. To achieve this, the data from the twenty pairs were divided into a training set and a validation set. Spectra from the tumor and normal regions of each of the tissue sections in the training set were used for orthogonal projection to latent structures (O-PLS) treated partial least-square discriminate analysis (PLS-DA). This predictive model was then validated by using the validation set and showed a 5% error rate for classification and a misclassification rate of 12%. It was also used to create synthetic images of the tissue sections showing pixel-by-pixel disease classification of the tissue and these data agreed well with the independent classification that uses histological data by a certified pathologist. This represents the first application of multivariate statistical methods for classification by ambient ionization although these methods have been applied previously to other MS imaging methods. The results are encouraging in terms of the development of a method that could be utilized in a clinical setting through visualization and diagnosis of intact tissue.
机译：通过使用来自解吸电喷雾电离质谱（DESI-MS）的成像数据与多变量统计分析相结合，可以实现未治疗组织切片中人膀胱癌的诊断。我们使用独特的DESI-MS甘油磷脂（GP）质谱图对人类癌症和邻近正常膀胱组织样品的二十对（40个组织样品）进行视觉表征和形式分类。从获得的轮廓中得出的单个离子图像与标准的组织学苏木精和曙红（H＆E）染色的连续切片相关。根据参考组织学数据判断，这些资料可以使我们对组织样品的疾病状态进行高精度分类。为此，将这二十对数据分为训练集和验证集。来自训练组中每个组织切片的肿瘤和正常区域的光谱用于正交投影到潜结构（O-PLS）处理的局部最小二乘判别分析（PLS-DA）。然后，通过使用验证集验证此预测模型，该模型显示出5％的分类错误率和12％的错误分类率。它还用于创建组织切片的合成图像，显示组织的逐像素疾病分类，并且这些数据与使用经过认证的病理学家的组织学数据的独立分类非常吻合。这代表了多元统计方法在环境电离分类中的首次应用，尽管这些方法先前已应用于其他MS成像方法。在开发可以通过可视化和诊断完整组织的临床环境中使用的方法方面，结果令人鼓舞。

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