A pilot study for identifying at risk thyroid lesions by means of a Decision Tree run on clinicocytological variables

Fine-needle aspiration biopsy (FNAB) is safe, inexpensive, minimally invasi ve, and highly accurate in the diagnosis of nodular diseases of the thyroid . However, FNAB does not provide a reliable benign versus malignant diagnos is for 100% of the cases analysed. It is possible to increase the accuracy of the cytological diagnosis by means of information contributed by differe nt clinical variables. In the present study we evaluate the diagnostic valu e of 10 variables in addition to FNAB on a series of 218 specimens for whic h we obtained histological diagnoses including 37 cancers (17%). The diagno stic information contributed by these variables was analyzed by means of th e Decision Tree technique, an artificial intelligence-related method which forms part of the Supervised Learning algorithms. The results show that Dec ision Trees enable some subpopulations of patients with uncertain FNAB resu lts to be characterized.