Thyroid Nodule Treatment Optimization: A Personalized Approach

甲状腺结节治疗优化：个性化方法

• 批准号: 10667526
• 负责人: Carrie Elizabeth Cunningham
• 金额: $ 49.28万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10667526
• 关键词: Affect; American; Area; BRAF gene; Benign; Biopsy; Calcitonin; Calibration; Carcinoma; Caring; Clinical; Clinical stratification; Computer Models; Conduct Clinical Trials; Consumption; Cost utility; Data; Databases; Development; Diagnosis; Disease; Disease Reservoirs; Economics; Effectiveness; Evaluation; Fine needle aspiration biopsy; Frequencies; Future; Goals; Guidelines; Harm Reduction; Health; Health Care Costs; Health Resources; Incidence; Individual; Indolent; Interdisciplinary Study; Intervention; Length; Lobectomy; Localized Disease; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of thyroid; Mathematics; Medical; Minority; Modeling; Molecular; Molecular Diagnostic Testing; Morbidity - disease rate; Natural History; Nodule; Operative Surgical Procedures; Outcome; Papillary; Papillary thyroid carcinoma; Patient Care; Patient-Focused Outcomes; Patients; Population; Practice Guidelines; Prevalence; Prospective Studies; Public Health; Quality of Care; Quality of life; Recurrent disease; Reporting; Research; Research Personnel; Risk; Scientist; Serum; Technology; Testing; Thyroid Diseases; Thyroid Gland; Thyroid Nodule; Thyroidectomy; Time; Tumor Subtype; Uncertainty; Visualization; Work; cancer subtypes; clinical practice; comparative cost effectiveness; cost; cost effective; data registry; diagnostic biomarker; diagnostic strategy; diagnostic technologies; effectiveness evaluation; health care service utilization; health related quality of life; improved; improved outcome; individual patient; insight; mathematical model; medullary thyroid carcinoma; models and simulation; molecular diagnostics; mortality; multidisciplinary; neoplasm registry; online version; overtreatment; personalized approach; personalized medicine; personalized strategies; pre-clinical; predictive marker; research study; risk stratification; screening; surveillance strategy; treatment optimization; treatment strategy

Project Summary / Abstract Thyroid nodules, present in over 30% of the U.S. population, are associated with significant morbidity and health resource utilization. Thyroid cancer currently affects over ½ million Americans. A reported increasing incidence is primarily attributed to the diagnosis of indolent papillary thyroid carcinoma (PTC) that has led to changes in clinical guidelines to less aggressive interventions. There is a critical need to identify the minority of patients with in increasing incidence of aggressive disease, while minimizing over-treatment in those patients with indolent PTC. Despite the public health impact thyroid nodule disease, few data exist on the impact of our clinical strategies and conducting clinical trials in this population is prohibitively time-consuming and costly. Our overarching goals are to enhance the health and minimize harm to the large number of patients with thyroid nodules. The specific objective of this proposal is to harness a comprehensive computer model to simulate individuals with benign and malignant nodules in the U.S. population to identify optimal personalized treatment approaches. This proposal builds on three tenets: First, contemporary research considers thyroid cancer in isolation from the more common benign nodular disease, missing the impact of the morbidity and cost associated with identifying those cancers. Secondly, there is a direct correlation between screening and diagnosis of thyroid cancer. It is imperative to consider the underlying reservoir of disease when assessing the effects of potential diagnostic and surveillance strategies. Lastly, while recent shifts in practice have been towards less aggressive management to minimize over- treatment, there is the potential to miss small, potentially lethal, thyroid cancer subtypes. The applicant is an Early Stage Investigator with expertise in thyroid nodular disease. Our team of a multidisciplinary group of experts, including decision scientists and statisticians will refine and expand on our mathematical model that simulates the pre-clinical course of both benign and malignant thyroid nodules (Aim 1) to identify the effectiveness of diagnostic biomarkers to guide treatment strategies (Aim 2) and assess the impact of risk-stratified surveillance approaches to patients with both thyroid nodular disease (Aim 3). Successful completion of the aims will inform our understanding of the health and economic consequences of our current clinical practices in the treatment of patients with thyroid nodular disease. Ultimately, we will identify patient management strategies that will identify aggressive disease while reducing morbidity both from recurrent disease and ineffective medical and surgical interventions.

项目摘要/摘要 甲状腺结节存在于超过30%的美国人口中，与显著的发病率和 卫生资源利用。甲状腺癌目前影响着50多万美国人。据报道正在增加 发病率主要归因于对惰性乳头状甲状腺癌(PTC)的诊断，这导致了 将临床指南改为不那么激进的干预措施。迫切需要确定少数人 患者侵袭性疾病的发病率增加，同时尽量减少这些患者的过度治疗 带着懒惰的PTC。尽管甲状腺结节疾病对公众健康有影响，但关于我们的 临床策略和在这一人群中进行临床试验既费时又昂贵，令人望而却步。 我们的首要目标是增进健康，最大限度地减少对大量 甲状腺结节。这项提议的具体目标是利用一个全面的计算机模型 在美国人群中模拟患有良性和恶性结节的个体，以确定最佳 个性化的治疗方法。这项建议建立在三个原则之上： 首先，当代研究认为甲状腺癌与更常见的良性结节是分开的。 疾病，忽略了与识别这些癌症相关的发病率和成本的影响。第二， 甲状腺癌的筛查和诊断之间有直接的相关性。必须考虑到 在评估潜在的诊断和监测战略的效果时，应考虑潜在的疾病蓄积者。 最后，虽然最近实践中的转变是转向不那么激进的管理，以尽量减少过度- 在治疗过程中，可能会漏掉小的、可能致命的甲状腺癌亚型。 申请人是一名具有甲状腺结节疾病专业知识的早期研究人员。我们的团队 包括决策科学家和统计学家在内的多学科专家小组将完善和扩大我们的 模拟良性和恶性甲状腺结节临床前病程的数学模型(AIM 1)确定诊断生物标记物的有效性，以指导治疗策略(目标2)并评估 风险分层监测方法对两种甲状腺结节疾病患者的影响(目标3)。 这些目标的成功完成将有助于我们对健康和经济后果的理解 我们目前在治疗甲状腺结节病患者方面的临床实践。最终，我们会 确定患者管理策略，以识别侵袭性疾病，同时降低发病率 复发的疾病和无效的内科和外科干预。