Abstract：
Thyroid nodules (TNs) have emerged as the most prevalent endocrine disorder in China. Fine-needle aspiration (FNA) remains the standard diagnostic method for assessing TN malignancy, although a majority of FNA results indicate benign conditions. Balancing diagnostic accuracy while mitigating overdiagnosis in patients with benign nodules poses a significant clinical challenge. Precise, noninvasive, and high-throughput screening methods for high-risk TN diagnosis are highly desired but remain less explored. Developing such approaches can improve the accuracy of noninvasive methods like ultrasound imaging and reduce overdiagnosis of benign nodule patients caused by invasive procedures. Herein, we investigate the application of gold-doped zirconium-based metal-organic framework (ZrMOF/Au) nanostructures for metabolic profiling of thyroid diseases. This approach enables the efficient extraction of urine metabolite fingerprints with high throughput, low background noise, and reproducibility. Utilizing partial least-squares discriminant analysis and four machine learning models, including neural network (NN), random forest (RF), logistic regression (LR), and support vector machine (SVM), we achieved an enhanced diagnostic accuracy (98.6%) for discriminating thyroid cancer (TC) from low-risk TNs by using a diagnostic panel. Through the analysis of metabolic differences, potential pathway changes between benign nodule and malignancy are identified. This work explores the potential of rapid thyroid disease screening using the ZrMOF/Au-assisted LDI-MS platform, providing a potential method for noninvasive screening of thyroid malignant tumors. Integrating this approach with imaging technologies such as ultrasound can enhance the reliability of noninvasive diagnostic methods for malignant tumor screening, helping to prevent unnecessary invasive procedures and reducing the risk of overdiagnosis and overtreatment in patients with benign nodules.
摘要：
甲状腺结节（TNs）已成为中国最常见的内分泌疾病。细针抽吸（FNA）仍然是评估TN恶性肿瘤的标准诊断方法，尽管大多数FNA结果表明是良性疾病。平衡诊断准确性，同时减轻良性结节患者的过度诊断带来了重大的临床挑战。精确,非侵入性，和高通量筛查方法用于高风险TN诊断是非常需要的，但仍未被探索。开发此类方法可以提高超声成像等非侵入性方法的准确性，并减少由侵入性程序引起的良性结节患者的过度诊断。在这里，我们研究了掺杂金的锆基金属有机骨架（ZrMOF/Au）纳米结构在甲状腺疾病代谢谱中的应用。这种方法能够以高通量高效提取尿液代谢物指纹,低背景噪声，和再现性。利用偏最小二乘判别分析和四种机器学习模型，包括神经网络(NN)，随机森林(RF)，逻辑回归(LR)，和支持向量机(SVM)，我们使用诊断小组对甲状腺癌(TC)和低危TNs进行鉴别诊断的准确率提高(98.6%).通过对代谢差异的分析,确定良性结节和恶性肿瘤之间的潜在通路变化。这项工作探索了使用ZrMOF/Au辅助LDI-MS平台快速筛查甲状腺疾病的潜力，为甲状腺恶性肿瘤的无创筛查提供了一种潜在的方法。将这种方法与超声等成像技术相结合，可以增强非侵入性诊断方法用于恶性肿瘤筛查的可靠性。有助于防止不必要的侵入性手术，并降低良性结节患者过度诊断和过度治疗的风险。
