Grant Number:	1R01CA281976-01A1 Interpret this number
Primary Investigator:	Haymart, Megan
Organization:	University Of Michigan At Ann Arbor
Project Title:	Risk-Stratified Care for Thyroid Cancer Survivors
Fiscal Year:	2024

Project Summary There has been an increase in the number of thyroid cancer survivors due to the rise in thyroid cancer incidence and young age at diagnosis. Although a substantial proportion of these survivors were likely cured with initial treatment, due to limited data and subsequent inability of physicians to confidently risk-stratify, surveillance for recurrence continues for many years, often lifelong. Unnecessary surveillance results in cancer-related worry, additional testing leading to false positive test results, and in some instances, complications from additional treatments. Through our collaboration with SEER sites Georgia (includes Atlanta, Greater Georgia, Rural Georgia) and Los Angeles County, we will have access to diverse, population-based cohorts of patients, in whom we can determine recurrence risk as well as current and optimal long-term surveillance. Our multipronged data collection strategy will allow us to obtain recurrence details from pathology reports, augmented with mortality data, patient report, and physician chart review and reporting. External validation will occur with use of the comprehensive University of Michigan Precision Health data. We will determine characteristics of cohorts at lower and higher risk of cancer recurrence, use these data to inform more tailored surveillance care pathways, and then refine surveillance care pathways after assessing trade- offs and net benefit. The ability to categorize thyroid cancer survivors into meaningful recurrence risk-stratified groups, tailor surveillance care pathways to recurrence risk, and then refine surveillance care pathways after assessing trade-offs will inform appropriate long-term follow-up, reduce patient harm, and improve healthcare costs. This study will dramatically change the care of thyroid cancer patients and will serve as a model for risk- stratified surveillance for other cancer types.





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