Thyroid Nodule Treatment Optimization: A Personalized Approach

甲状腺结节治疗优化：个性化方法

• 批准号: 10475609
• 负责人: Carrie Elizabeth Cunningham
• 金额: $ 50.28万
• 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/10475609
• 关键词: Address; Affect; American; Area; BRAF gene; Benign; Biopsy; Calcitonin; Clinical; Computer Models; Conduct Clinical Trials; Consumption; Cost utility; Data; Databases; Development; Diagnosis; Disease; Disease Reservoirs; 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; Work; cancer subtypes; care outcomes; clinical practice; comparative cost effectiveness; cost; cost effective; data registry; diagnostic biomarker; diagnostic strategy; diagnostic technologies; effectiveness evaluation; health care service utilization; health economics; 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; 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.

项目总结/摘要 甲状腺结节存在于超过30%的美国人口中，与显著的发病率相关， 卫生资源利用甲状腺癌目前影响超过50万美国人。据报道， 发病率主要归因于惰性甲状腺乳头状癌（PTC）的诊断， 将临床指南改为不太积极的干预措施。目前迫切需要查明 患者侵袭性疾病的发生率增加，同时尽量减少这些患者的过度治疗 无痛性PTC尽管甲状腺结节疾病对公共卫生有影响，但关于我们的影响的数据很少。 临床策略和在这一人群中进行临床试验是极其耗时和昂贵的。 我们的首要目标是提高健康和尽量减少对大量患者的伤害， 甲状腺结节这项建议的具体目标是利用一个全面的计算机模型 模拟美国人群中患有良性和恶性结节的个体， 个性化的治疗方法。这项建议基于三项原则： 首先，当代研究认为甲状腺癌与更常见的良性结节性甲状腺癌是分开的。 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联系。必须考虑到 在评估潜在诊断和监测策略的影响时，了解潜在的疾病来源。 最后，虽然最近在实践中的转变是朝着不那么积极的管理，以尽量减少过度- 治疗，有可能错过小的，潜在致命的，甲状腺癌亚型。 申请人是具有甲状腺结节性疾病专业知识的早期研究者。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 数学模型，模拟良性和恶性甲状腺结节的临床前过程（目的 1)确定诊断生物标志物的有效性，以指导治疗策略（目标2），并评估 风险分层监测方法对甲状腺结节性疾病患者的影响（目标3）。 成功完成这些目标将使我们了解健康和经济后果 我们目前治疗甲状腺结节性疾病患者的临床实践。最终，我们将 确定患者管理策略，以识别侵袭性疾病，同时降低发病率， 复发性疾病和无效的医疗和手术干预。