Predictive Model Based on Neural Networks to Assist the Diagnosis of Malignancy of Thyroid Nodules

The thyroid nodule is an abnormal growth that forms lumps in the thyroid gland. Image processing opened a new era in the identification and diagnosis of nodules thyroids in which many of them are being found incidentally. It is estimated that between 4 to 10 % can be felt on physical examination, but the rest of them is determinate by ultrasound or another sophisticate image’s technique. The thyroid nodule is the most common endocrine problem in the United States, and the 10% of them contain cancer. The objective is to determine as soon as possible how bad is the nodule found, with the less invasive procedure. A model based in a neural network is presented in this conference that minimizes the impact of an invasive method with a probability of success. A database of 1097 patients with an identified nodule is used in this study, where 703 are benign, 238 with follicular lesions and 156 was identified as carcinoma. Each data set has fifteen (15) characteristics plenty identified as input and two (2) outputs, the TIRADS (Thyroid Imaging Reporting and Data System) classification and the nodule biopsy results. The actual Decision Support System (DSS) assists medical doctors to identify nodules at risk for being malignant; however, this platform is based on a static model with no learning capability. Every day more data from new patient can be used to increase the accuracy of the output model. The main focus of this research is to include learning capabilities to the DSS static model and converting it to a dynamic model with a learning engine and algorithm based on neural networks.