The incidence of thyroid cancer is on the rise, and it now represents the fourth most common cancer in women in Italy. In particular, differentiated thyroid cancer have an excellent prognosis, with a five-year survival rate of 98.1% and low rates of recurrence. The challenge today is to develop increasingly individualized treatment strategies with due emphasis on quality of life. A first step toward this goal involves classification of cases according to the risk of recurrence.
A three-tiered risk system (low, intermediate, high) was proposed for structural disease recurrence in patients without structurally identifiable disease after initial therapy. In this system, the risk of structural disease recurrence has been associated with selected clinico-pathological features and reflects an estimate based on the published literature. However, large multicenter and prospective studies on proper evaluation of risk of recurrence in thyroid cancer patients are missing.
The overall aim of the project is to reliably predict thyroid cancer recurrence after initial patient treatment. Secondary aim is the identification of predictors of the outcome treatment. Novel technologies such as machine learning and artificial intelligence algorithms will be applied to data from the web-based thyroid cancer database of the Italian Thyroid Cancer Observatory (ITCO). ITCO dataset includes prospective and observational data from consecutive patients diagnosed with thyroid cancer since 2013.
This project will provide follow-up data on the largest thyroid cancer dataset, thus allowing an accurate risk assessment to be used to guide initial prognostication, disease management, and proper follow-up strategies. The proper risk assessment will minimize overtreatment in the majority of at low-risk patients and will appropriately allow for treating and monitoring those patients at higher risk. Employment of innovative technologies will allow for identification of unknown risk predictors
