Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA.
Cancer Cytopathol. 2013 Mar;121(3):162-7. doi: 10.1002/cncy.21224. Epub 2012 Jul 25.
Thyroid fine-needle aspiration (FNA) biopsy, the preoperative diagnostic standard of care for patients with thyroid nodules, has limitations. Spectral imaging captures visible light information that is beyond the capability of the human eye, potentially increasing the accuracy of FNA biopsy. In the current study, the authors demonstrated the feasibility of using spectral imaging in combination with automated spatial analysis based on trainable pattern recognition as an adjunct test for thyroid FNA classification by developing an algorithm that distinguishes between images of papillary thyroid carcinoma (PTC) and benign goiter (BG).
A multispectral camera was used to capture spectral images representing 100 cases of PTC and BG. Used in conjunction with commercial software, 10 cases were used as a training set to develop a "classifier," a classification algorithm that segments digitized multispectral images into regions of PTC, BG, and "nonfeature." This algorithm was used to generate a screening test and a diagnostic test that were validated on an independent set of images representing 30 cases of PTC and 30 cases of BG.
The area under the receiver operating characteristic for the PTC/BG classifier was 0.90. The screening test had a sensitivity of 0.93 and a specificity of 0.73. The diagnostic test had a sensitivity of 0.70 and a specificity of 0.90.
The authors developed image classification tests that distinguish between FNAs of PTC and BG, demonstrating the potential value of spatial spectral imaging as an adjunct test for the classification of thyroid FNA samples. The data support prospective testing to determine the value of the PTC/BG classifier in routine clinical use.
甲状腺细针抽吸活检（FNA）是甲状腺结节患者术前的标准诊断方法，但存在一定的局限性。光谱成像是捕捉人眼无法识别的可见光信息，这有可能提高 FNA 活检的准确性。在本研究中，作者通过开发一种算法，证明了将光谱成像与基于可训练模式识别的自动空间分析相结合，作为甲状腺 FNA 分类的辅助检测方法的可行性，该算法可以区分甲状腺乳头状癌（PTC）和良性甲状腺肿（BG）的图像。
使用多光谱相机捕获代表 100 例 PTC 和 BG 的光谱图像。结合商业软件，使用 10 例作为训练集来开发“分类器”，即一种分类算法，将数字化的多光谱图像分割为 PTC、BG 和“非特征”区域。该算法用于生成筛选测试和诊断测试，并在代表 30 例 PTC 和 30 例 BG 的独立图像集上进行验证。
PTC/BG 分类器的受试者工作特征曲线下面积为 0.90。筛选测试的敏感性为 0.93，特异性为 0.73。诊断测试的敏感性为 0.70，特异性为 0.90。
作者开发了区分 PTC 和 BG 的 FNA 图像分类测试，证明了空间光谱成像作为甲状腺 FNA 样本分类的辅助检测方法具有潜在价值。这些数据支持前瞻性测试，以确定 PTC/BG 分类器在常规临床应用中的价值。
