Malignancy Risk Assessment in Patients with Thyroid Nodules Using Classification and Regression Trees

Abstract

Purpose.We sought to investigate the utility of classification and regression trees (CART) classifier to differentiate benign from malignant nodules in patients referred for thyroid surgery.Methods.Clinical and demographic data of 271 patients referred to the Sadoughi Hospital during 2006–2011 were collected. In a two-step approach, a CART classifier was employed to differentiate patients with a high versus low risk of thyroid malignancy. The first step served as the screening procedure and was tailored to produce as few false negatives as possible. The second step identified those with the lowest risk of malignancy, chosen from a high risk population. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) of the optimal tree were calculated.Results.In the first step, age, sex, and nodule size contributed to the optimal tree. Ultrasonographic features were employed in the second step with hypoechogenicity and/or microcalcifications yielding the highest discriminatory ability. The combined tree produced a sensitivity and specificity of 80.0% (95% CI: 29.9–98.9) and 94.1% (95% CI: 78.9–99.0), respectively. NPV and PPV were 66.7% (41.1–85.6) and 97.0% (82.5–99.8), respectively.Conclusion.CART classifier reliably identifies patients with a low risk of malignancy who can avoid unnecessary surgery.