Papillary Thyroid Cancer, PTC: Identifying the Values of the Risk Factors that Will Minimize the Malignant Tumor Size Through Desirability Function Approach

Abstract

Thyroid cancer is one of the major cancers in today’s society, and it is the fastest-growing cancer diagnosis in the United States. One of the most important aspects of Thyroid Cancer, TC, is to identify the risk factors that contribute to TC growth. TC is separated into four main types, Papillary, Follicular, Medullary, and Anaplastic. Papillary Thyroid Cancer, (PTC) is the most common thyroid cancer type; eight out of ten TC patients are classified with PTC. We have developed a real data-driven analytical model to predict the malignant tumor size of PTC patients with a high degree of accuracy using four of the nine individual risk factors that significantly contribute to PTC and most importantly the identified eleven bivariate interaction terms which are highly significant in contributing to the growth of the malignant tumor size of PTC patients, []. In the present study, we utilized the Desirability Function Process to optimize (minimize) the malignant tumor size of PTC patients. That is, we identify the values of the risk factors that will minimize the malignant tumor size of PTC patients with at least 95% accuracy. To achieve our objective, we utilized the desirability function process and validated the necessary conditions required for the optimization process of the analytical model which includes individual risk factors as well as interaction terms with a high desirability function of 1, implying that the risk factors are effective and robust in minimizing the malignant tumor size of PTC patients. This information is very important to medical professionals in identifying and implementing appropriate treatment strategies for PTC patients.