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

描述（由申请人提供）：在完成内分泌外科奖学金后，候选人加入马萨诸塞州总医院外科肿瘤科工作，并于2010年被任命为哈佛医学院。自2010年以来，她获得了公共卫生硕士学位，通过癌症结果研究培训奖学金项目与她的导师建立了富有成效的关系，并被提升为助理教授。这个职业发展奖是由甲状腺癌对公共卫生的重要性所激发的，甲状腺癌影响着超过50万美国人，并且发病率和患病率都在增加。大量的发病率和社会成本可归因于甲状腺乳头状癌（PTC）。虽然PTC的发病率增长速度比其他任何癌症都快，但大多数新诊断的患者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。鉴于相对较低的疾病特异性死亡率，在这一人群中以生存为主要终点的临床试验的时间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人来解决这些知识差距。具体来说，候选人建议：1)制定一个决策

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.