Fourier transform infrared (FT-IR) microscopy was used to image tissue samples from twenty patients diagnosed with thyroid carcinoma. The spectral data were then used to differentiate between follicular thyroid carcinoma and follicular variant of papillary thyroid carcinoma using principle component analysis coupled with linear discriminant analysis and a Naïve Bayesian classifier operating on a set of computed spectral metrics. Classification of patients' disease type was accomplished by using average spectra from a wide region containing follicular cells, colloid, and fibrosis; however, classification of disease state at the pixel level was only possible when the extracted spectra were limited to follicular epithelial cells in the samples, excluding the relatively uninformative areas of fibrosis. The results demonstrate the potential of FT-IR microscopy as a tool to assist in the difficult diagnosis of these subtypes of thyroid cancer, and also highlights the importance of selectively and separately analyzing spectral information from different features of a tissue of interest.

中文翻译：

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在红外光谱成像中考虑组织异质性以准确诊断甲状腺癌亚型

傅里叶变换红外 (FT-IR) 显微镜用于对 20 名诊断为甲状腺癌的患者的组织样本进行成像。然后使用主成分分析结合线性判别分析和基于一组计算光谱指标的朴素贝叶斯分类器，将光谱数据用于区分滤泡性甲状腺癌和滤泡状甲状腺癌的变体。患者疾病类型的分类是通过使用来自包含滤泡细胞、胶体和纤维化的广泛区域的平均光谱来完成的；然而，只有当提取的光谱仅限于样本中的滤泡上皮细胞时，才能在像素级别对疾病状态进行分类，排除相对无信息的纤维化区域。