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

质谱成像检测代谢标志物诊断甲状腺细针抽吸活检的临床验证

• 批准号: 10360336
• 负责人: Rongrong Huang
• 金额: $ 18.66万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10360336
• 关键词: Address; Benign; Biological Assay; Biopsy; Caring; Chemistry; Classification; Clinical; Clinical Chemistry; Clinical Pathology; Clinical Research; Collaborations; Cytology; Cytopathology; Detection; Diagnosis; Diagnostic; Effectiveness; Evaluation; Fine needle aspiration biopsy; Follicular Adenoma; Follicular thyroid carcinoma; Genetic; Genomics; Gold; Guidelines; Healthcare Systems; Hormone replacement therapy; Human; Hypothyroidism; Imaging technology; Incidence; Industrialization; Laboratories; Life; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of thyroid; Measures; Medical center; Medicine; Metabolic; Metabolic Marker; Methods; Modeling; Modernization; Molecular; Nodule; Operative Surgical Procedures; Pain; Papillary thyroid carcinoma; Pathologic; Pathology; Patient Care; Patients; Pattern; Performance; Phase; Practice Guidelines; Procedures; Quality Control; Quality of life; Research; Risk; Sample Size; Sampling; Sensitivity and Specificity; Specimen; Standardization; Statistical Data Interpretation; Surgical Pathology; Technology; Testing; Texas; Thyroid Gland; Thyroid Nodule; Thyroidectomy; Tissues; Training; Universities; Unnecessary Surgery; Validation; accurate diagnosis; austin; base; biomedical data science; cancer diagnosis; cancer surgery; care costs; clinical diagnostics; clinical implementation; clinical material; college; cost; diagnostic assay; diagnostic biomarker; expectation; experimental study; genetic testing; good laboratory practice; high throughput technology; improved; innovation; interdisciplinary collaboration; mass spectrometric imaging; member; predictive marker; prospective; side effect; validation studies

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.

精确诊断通过细针抽吸物（FNA）活检对甲状腺结节进行诊断，对现代最佳至关重要 患有甲状腺癌风险的患者练习护理。可疑甲状腺结节的当前诊断依赖 关于细胞学研究结果的解释。不幸的是，由于 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 约20％的病例诊断。临床指南建议不确定FNA的患者进一步接受 测试包括重复活检（疼痛，可能产生相同的不确定结果），基因组分析（昂贵，不是 始终可用）或诊断性甲状腺手术（非常昂贵，痛苦，侵入性，许多改变生活 并发症）。令人震惊的是，发现接受诊断手术的患者中有70-90％表现出良性 通过手术病理学结节，这意味着手术是完全不必要的。不必要的手术 主要的负面后果。对于患者，诊断手术甲状腺功能减退症导致质量降低 生命和终生需要激素替代疗法。对于医疗保健系统，不必要的成本 手术是巨大的。尽管在基因组分析和细胞学分类方面做出了最大的努力，但仍有 仍然是巨大的诊断差距，需要改进甲状腺癌术前诊断的技术。 为了满足这种关键的临床需求，我们将甲状腺癌/手术方面的专业知识结合在一起（博士 BCM贝勒医学学院外科手术系James Suliburk），质谱成像（博士 德克萨斯大学奥斯汀分校化学系化学系利维亚·埃伯林（Livia S. Eberlin），统计分析（Rob博士 斯坦福大学生物医学数据科学系Tibshirani），临床化学（Rongrong博士） Huang，BCM临床化学科学总监和临床病理学（Thomas Wheeler博士，部门 病理学，BCM），并使用质谱成像和机器学习开发了一种测定 基于直接从临床中检测到数百个代谢标记的特征，诊断FNA活检 标本。现在，我们建议使用FNA活检进行关键的分析和临床验证研究 前瞻性地从BCM接受治疗的患者中收集，以严格验证该方法的临床方法 执行。在UH2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，以评估我们的测定的性能和 代谢标记在其使用的临床背景下。在UH3研究阶段，我们将验证临床 与黄金标准病理评估相比，FNA诊断的诊断性能和诊断性能。我们的 前提是，提出的严格研究将完成验证所需的分析和临床任务 我们对甲状腺FNA诊断的测定和预测标记，从而证明了其有效性作为诊断 测定。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 进入强大的技术，用于高通量和准确诊断甲状腺FNA材料。