Optimization of Treatment of Papillary Thyroid Cancer: A Risk-tailored Approach

甲状腺乳头状癌治疗的优化：针对风险的方法

• 批准号: 9116115
• 负责人: Carrie Elizabeth Cunningham
• 金额: $ 14.24万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9116115
• 关键词: Address; Affect; Algorithms; American; Award; BRAF gene; Biometry; Cancer Patient; Caring; Cervical lymph node group; Clinical; Clinical Trials; Computer Simulation; Conduct Clinical Trials; Consensus; Cost utility; Data; Decision Analysis; Decision Making; Diagnosis; Diagnostic; Disease; Disease-Free Survival; Endocrine Surgical Procedures; Fellowship; Fostering; Future; General Hospitals; Goals; Guidelines; Health; Incidence; Individual; Intervention; K-Series Research Career Programs; Knowledge; Length; Life Expectancy; Living Costs; Lymph Node Dissections; Malignant Neoplasms; Malignant neoplasm of thyroid; Massachusetts; Master of Public Health; Medical; Mentors; Modeling; Morbidity - disease rate; Natural History; Operative Surgical Procedures; Outcome; Outcomes Research; Papillary thyroid carcinoma; Patient Care; Patient-Focused Outcomes; Patients; Population; Prevalence; Public Health; Quality of Care; Quality of life; Recurrence; Recurrent disease; Reporting; Research; Research Priority; Research Training; Resources; Risk; Science; Stratification; Surgical Oncology; Surveys; Testing; Thyroid Gland; Time; Training; Work; base; clinical practice; clinically relevant; comparative effectiveness; cost; design; economic implication; economic outcome; effectiveness research; evidence base; experience; health economics; health related quality of life; improved; medical schools; models and simulation; molecular diagnostics; mortality; multidisciplinary; mutational status; personalized approach; prediction algorithm; professor; programs; prophylactic; skills; standard care; surveillance strategy; tool; tumor

DESCRIPTION (provided by applicant): Following completion of endocrine surgery fellowship, the candidate joined the surgical oncology staff at the Massachusetts General Hospital and was appointed at Harvard Medical School in 2010. Since 2010, she achieved a Master of Public Health degree, established productive relationships with her mentors through the Program in Cancer Outcomes Research Training fellowship, and was promoted to Assistant Professor. This Career Development Award is motivated by the public health importance of thyroid cancer, which affects over 1/2 million Americans and is increasing in incidence and prevalence. Substantial morbidity and societal cost are attributable to papillary thyroid carcinoma (PTC). While the incidence of PTC is increasing faster than any other cancer, the majority of new diagnoses is patients with small tumors in a low-risk population, and disease-specific mortality remains low and unchanged. Given the relatively low disease- specific mortality, the length and cost of clinical trials with survival as the primary endpoint in this population are prohibitive. Te majority of clinical practices are, therefore, based on consensus alone. The candidate seeks to address these knowledge gaps by constructing a comprehensive computer model to simulate individuals in the U.S. population. Specifically, the candidate proposes to: 1) develop a decision- analytic model to simulate the natural history of papillary thyroid cancer in both untreated and treated patients; 2) conduct studies to inform key model parameters where data are currently lacking using primary patient data to develop a prediction algorithm for recurrence of PTC integrating knowledge of BRAF mutational status as well as patient-reported HRQoL data; and 3) evaluate and compare effects of standard versus alternate management strategies on outcomes, including risk-stratified approaches to treatment and surveillance on health and economic outcomes. The central hypothesis is that integrating improved risk-stratification and a tailored approach to treatment and surveillance will optimize quality of care and resource utilization while minimizing over-diagnosis and over-treatment for patients with PTC. This K07 is designed to foster independence through advanced training in decision analysis, survey science, and biostatistics as well as effective and productive mentoring relationships. The application draws upon strengths of a diverse group of experienced mentors in a multidisciplinary fashion. The aims will inform our understanding of the health and economic consequences of our current `clinical practices in the treatment of patients with PTC. Ultimately, we will identify individuals likely to benefit from more aggressive diagnostic and surgical approaches and reduce morbidity both from recurrent disease and ineffective medical and surgical interventions. With the skills and groundwork obtained during the K07 award period, the candidate will be prepared to submit a competitive R01 application.

描述（由申请人提供）：在完成内分泌外科奖学金后，候选人加入了马萨诸塞州总医院的外科肿瘤科工作人员，并于2010年被任命为哈佛医学院的医生。自2010年以来，她获得了公共卫生硕士学位，通过癌症成果研究培训奖学金计划与导师建立了富有成效的关系，并晋升为助理教授。 这个职业发展奖的动机是甲状腺癌的公共卫生重要性，影响超过1/5万美国人，发病率和患病率正在增加。甲状腺乳头状癌（PTC）的发病率和社会成本都很高。虽然PTC的发病率比任何其他癌症都增长得更快，但大多数新诊断是低风险人群中的小肿瘤患者，疾病特异性死亡率仍然很低且没有变化。鉴于特定疾病的死亡率相对较低，在该人群中以生存作为主要终点的临床试验的长度和成本令人望而却步。因此，大多数临床实践仅基于共识。 候选人试图通过构建一个全面的计算机模型来模拟美国人口中的个体来解决这些知识差距。具体而言，候选人建议：1）制定一项决定- 分析模型，以模拟未经治疗和经治疗患者中乳头状甲状腺癌的自然史; 2）进行研究，以告知目前缺乏数据的关键模型参数，使用主要患者数据开发PTC复发的预测算法，整合BRAF突变状态的知识以及患者报告的HRQoL数据;和3）评估和比较标准与替代管理策略对结果的影响，包括风险分层的治疗方法和对健康和经济结果的监测。中心假设是，将改进的风险分层和定制的治疗和监测方法相结合，将优化护理质量和资源利用，同时最大限度地减少PTC患者的过度诊断和过度治疗。 该K 07旨在通过决策分析，调查科学和生物统计学的高级培训以及有效和富有成效的指导关系来培养独立性。该应用程序以多学科的方式借鉴了经验丰富的导师的不同群体的优势。这些目标将使我们了解我们目前治疗PTC患者的“临床实践”的健康和经济后果。最终，我们将确定可能受益于更积极的诊断和手术方法的个体，并降低复发性疾病和无效的医疗和手术干预的发病率。凭借K 07奖励期间获得的技能和基础知识，候选人将准备提交具有竞争力的R 01申请。