Clinical Validation of Metabolic Markers Detected by Mass Spectrometry Imaging for Diagnosis of Thyroid Fine Needle Aspiration Biopsies

通过细针穿刺（FNA）活检准确诊断甲状腺结节是现代最佳诊断方法的关键。 对有患甲状腺癌风险的患者进行护理。目前诊断可疑甲状腺结节依赖于 细胞病理学对细胞学结果的解释。不幸的是，由于FNA诊断的困难， 重叠的细胞学特征，样本量不足或缺乏清晰的模式，导致不确定的 约20%的病例确诊。临床指南建议不确定FNA的患者接受进一步的 包括重复活检（痛苦，可能产生相同的不确定结果），基因组分析（昂贵，不 总是可用的），或诊断性甲状腺手术（非常昂贵，痛苦，侵入性，许多生活改变 并发症）。令人震惊的是，70-90%接受诊断性手术的患者被发现是良性的， 结节的手术病理，这意味着手术是完全不必要的。不必要的手术 严重的负面后果。对于患者来说，诊断性手术甲状腺功能减退会导致 生活和终身需要激素替代疗法。对于医疗保健系统来说， 手术量巨大尽管在基因组分析和改进的细胞学分类方面做出了最大的努力， 仍然存在很大的诊断差距，需要改进甲状腺癌术前诊断的技术。 为了满足这一关键的临床需求，我们结合了我们在甲状腺癌/手术方面的专业知识（Dr. James Suliburk，外科，贝勒医学院，马里兰州），质谱成像（博士。 Livia S. Eberlin，Department of Chemistry，The University of Texas at Austin），statistical analysis（Dr. Rob Tibshirani，斯坦福大学生物医学数据科学系）、临床化学（荣荣博士 Huang，临床化学部科学总监）和临床病理学（托马斯惠勒博士， 的病理学，1999年），并开发了一种使用质谱成像和机器学习的测定方法， 基于直接来自临床的数百种代谢标志物的检测来诊断FNA活检 标本现在，我们建议对FNA活检进行关键的分析和临床验证研究， 前瞻性地从在TNM接受治疗的患者中收集，以严格验证临床应用的方法。 实施.在UH 2研究阶段，我们将建立关键的分析性能指标、质量 控制措施和方法标准化程序，以评估我们的检测方法的性能， 代谢标志物在其临床应用中的应用。在UH 3研究阶段，我们将验证临床 与金标准病理评价相比，FNA诊断的诊断性能。我们 前提是所提出的严格研究将完成验证所需的分析和临床任务 我们的甲状腺FNA诊断的测定和预测标志物，从而证明其作为诊断的有效性， 比色法在商业合作伙伴的支持下，我们的最终目标是开发这种创新的代谢测试 成为一种可靠的技术，用于甲状腺FNA材料的高通量和准确诊断。

Accurate diagnosis of thyroid nodules by fine needle aspirate (FNA) biopsy is essential to modern day best practice care in patients who are at risk of thyroid cancer. Current diagnosis of suspicious thyroid nodules relies on the interpretation of cytology findings by cytopathology. Unfortunately, difficulties in FNA diagnosis due to overlapping cytological features, inadequate sample size, or lack of clear pattern result in an indeterminate diagnosis in ~ 20% of cases. Clinical guidelines recommend that patients with indeterminate FNA undergo further testing including repeat biopsy (painful, may yield same indeterminate result), genomic analysis (expensive, not always available), or diagnostic thyroid surgery (very expensive, painful, invasive, with many life altering complications). Shockingly, 70-90% of patients that undergo diagnostic surgery are found to present benign nodules by surgical pathology, meaning that surgery was completely unnecessary. Unnecessary surgeries have major negative consequences. For patients, diagnostic surgery hypothyroidism results in decreased quality of life and lifelong need for hormone replacement therapy. For the healthcare system, the cost from unnecessary surgeries is enormous. Despite best efforts in genomic analysis and improved cytologic classification, there still remains a large diagnostic gap and need for improved technology for preoperative diagnosis of thyroid cancers. To address this critical clinical need, we have combined our expertise in thyroid cancer/surgery (Dr. James Suliburk, Department of Surgery, Baylor College of Medicine, BCM), mass spectrometry imaging (Dr. Livia S. Eberlin, Department of Chemistry, The University of Texas at Austin), statistical analysis (Dr. Rob Tibshirani, Department of Biomedical Data Science, Stanford University), clinical chemistry (Dr. Rongrong Huang, Scientific Director of Clinical Chemistry, BCM), and clinical pathology (Dr. Thomas Wheeler, Department of Pathology, BCM), and developed an assay using mass spectrometry imaging and machine learning to diagnose FNA biopsies based on the detection of a profile of hundreds of metabolic markers directly from clinical specimens. Now, we propose to conduct critical analytical and clinical validation studies with FNA biopsies prospectively collected from patients undergoing treatment at BCM to rigorously validate the method for clinical implementation. During the UH2 research phase, we will establish key analytical performance metrics, quality control measures, and method standardization procedures to evaluate the performance of our assay and metabolic markers within its clinical context of use. During the UH3 research phase, we will validate the clinical and diagnostic performance for FNA diagnosis in comparison to gold standard pathologic evaluation. Our premise is that the rigorous studies proposed will complete the analytical and clinical tasks needed to validate our assay and predictive markers for thyroid FNA diagnosis, thus demonstrating its effectiveness as a diagnostic assay. With support from commercial partners, our ultimate objective is to develop this innovative metabolic test into a robust technology for high-throughput and accurate diagnosis of thyroid FNA material.