Thyroid nodules are common clinical entities, with a significant proportion being malignant. Early, accurate, and non-invasive tools to differentiate benign and malignant nodules can optimize patient management and reduce unnecessary surgery. This study aimed to evaluate the efficacy and accuracy of near-infrared spectroscopy (NIRS) in distinguishing benign from malignant thyroid nodules. A diffuse reflectance spectrum for a total of 20 thyroid nodule samples (10 samples as colloid goiter and 10 samples as thyroid cancer), were acquired in the wavelength range from 1000 to 2500 nm. Spectral data from NIRS were analyzed by means of principal component analysis (PCA), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) to classify and differentiate thyroid nodule samples. The present study found that NIRS effectively distinguished colloid goiter and thyroid cancer using the first two principal components (PCs), explaining 90% and 10% of the variance, respectively. QDA discrimination plot displayed a clear separation between colloid goiter and thyroid cancer with minimal overlap, aligning with reported 95% accuracy. Additionally, applying LDA to seven PCs from PCA achieved a 100% accuracy rate in classifying colloid goiter and thyroid cancer from near-infrared spectral data. In conclusion, NIRS offers a promising, non-invasive complementing diagnostic tool for differentiating benign from malignant thyroid nodules with high accuracy. Future work should integrate these results into predictive model development, emphasizing external validation, alternative performance metrics, and protecting against potential overfitting translation of a machine learning model to a clinical setting.

中文翻译：

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近红外光谱技术用于区分甲状腺结节的恶性程度

甲状腺结节是常见的临床疾病，其中很大一部分是恶性的。早期、准确和非侵入性的工具来区分良性和恶性结节可以优化患者管理并减少不必要的手术。本研究旨在评估近红外光谱（NIRS）在区分良性和恶性甲状腺结节方面的功效和准确性。在 1000 至 2500 nm 的波长范围内采集了总共 20 个甲状腺结节样本（10 个胶体甲状腺肿样本和 10 个甲状腺癌样本）的漫反射光谱。通过主成分分析（PCA）、二次判别分析（QDA）和线性判别分析（LDA）对近红外光谱（NIRS）的光谱数据进行分析，以对甲状腺结节样本进行分类和区分。本研究发现，NIRS 使用前两个主成分 (PC) 有效区分胶体甲状腺肿和甲状腺癌，分别解释了 90% 和 10% 的方差。QDA 判别图显示胶体甲状腺肿和甲状腺癌之间有明显区分，重叠极小，与报道的 95% 准确度一致。此外，将 LDA 应用到 PCA 的 7 台 PC 上，根据近红外光谱数据对胶体甲状腺肿和甲状腺癌进行分类的准确率达到 100%。总之，NIRS 提供了一种有前途的、非侵入性的补充诊断工具，可以高精度区分良性和恶性甲状腺结节。未来的工作应该将这些结果整合到预测模型开发中，强调外部验证、替代性能指标，并防止机器学习模型到临床环境的潜在过度拟合。