Artificial neural network analysis for evaluating cancer risk in multinodular goiter

Abstract

Background: The aim of this study was to create a diagnostic model using the artificial neural networks (ANNs) to predict malignancy in multinodular goiter patients with an indeterminate cytology. Materials and Methods: Out of 623 patients, 411 evaluated for multinodular goiter between July 2004 and March 2010 had a fine-needle aspiration biopsy. All patients underwent total thyroidectomy. The interpretation was consistent with an indeterminate lesion in 116 (18.6%) patients. Patient′s medical records including age, sex, dominant nodule size, pre-operative serum thyroid-stimulating hormone level, thyroid hormone therapy and final pathologic diagnosis were collected retrospectively. Results: The mean age of the patients was 44.6 years (range, 17-78 years). About 104 (89.7%) were female and 12 (10.3%) were male patients. Final pathology revealed 24 malignant diseases (20.7%) and 92 (79.3%) benign diseases. After the completion of training, the ANN model was able to predict diagnosis of malignancy with a high degree of accuracy. The area under the curve of ANNs was 0.824. Conclusion: The ANNs technique is a useful aid in diagnosing malignancy and may help reduce unnecessary thyroidectomies in multinodular goiter patients with an indeterminate cytology. Further studies are needed to construct the optimal diagnostic model and to apply it in the clinical practice.

Keywords

• Artificial neural network
• fine-needle aspiration
• ındeterminate cytology
• multinodular goiter
• thyroid