Combined use of sonographic and elastosonographic parameters can improve the diagnostic accuracy in thyroid nodules at risk of malignancy at cytological examination.

BACKGROUND: Thyroid nodules diagnosed as Thy3B at fine-needle aspiration biopsy have a relevant risk of malignancy (15-30%) and are usually addressed to surgery. However surgery will result unnecessary in most cases. The present study aims at evaluating the possible increase of diagnostic accuracy for predicting malignancy using novel sonographic and elastographic parameters. METHODS: In fifty patients undergoing thyroidectomy because of a Thy3B thyroid nodule, sonographic and elastosonographic evaluation was carried out by single operator before surgery. Five sonographic parameters (echogenicity, irregular margins, microcalcifications, intra-nodule blood flow and its irregularity) and two elastosonographic parameters (intra-nodule stiffness and its extension to adjacent tissue) were considered. After obtaining histological diagnosis, diagnostic accuracy was calculated. RESULTS: When the two procedures were analyzed separately, sensitivity, specificity, positive (PPV) and negative (NPV) predictive values were 100%, 85%, 63% and 100% for ultrasonography and 60%, 92.5%, 67%, 90% for elastrosonography, respectively. The newly introduced evaluation procedures increased sensitivity. When a combined sonographic and elastosonographic evaluation was introduced, diagnostic accuracy was significantly improved: when ≥4 out of the seven parameters indicated were present, the risk of malignancy was very high (sensitivity 100%, specificity 92.55%, PPV 77%, NPV 100%). CONCLUSIONS: A novel combined sonographic and elastosonographic parameter evaluation improved diagnostic accuracy for identifying thyroid nodules suspicious of malignancy.

Neural network analysis for evaluating cancer risk in thyroid nodules with an indeterminate diagnosis at aspiration cytology: identification of a low-risk subgroup.

Thyroid nodules with a predominant follicular structure are often diagnosed as indeterminate at fine-needle aspiration biopsy (FNAB). We studied 453 patients with a thyroid nodule diagnosed as indeterminate at FNAB by using a feed-forward artificial neural network (ANN) analysis to integrate cytologic and clinical data, with the goal of subgrouping patients into a high-risk and in a low-risk category. Three hundred seventy-one patients were used to train the network and 82 patients were used to validate the model. The cytologic smears were blindly reviewed and classified in a high-risk and a low-risk subgroup on the basis of standard criteria. Neural network analysis subdivided the 371 lesions of the first series into a high-risk group (cancer rate of approximately 33% at histology) and a low-risk group (cancer rate of 3%). Only cytologic parameters contributed to this classification. Analysis of the receiver operating characteristic (ROC) curves demonstrated that the ANN model discriminated with higher sensitivity and specificity between benign and malignant nodules compared to standard cytologic criteria (p < 0.001). This value did not show degradation when ANN predictions were applied to the validation series of 82 nodules. In conclusion, neural network analysis of cytologic data may be a useful tool to refine the risk of cancer in patients with lesions diagnosed as indeterminate by FNAB.