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-needleaspiration biopsy (FNAB). We studied 453 patients with a thyroid nodule diagnosed as indeterminate at FNABby using a feed-forward artificial neural network (ANN) analysis to integrate cytologic and clinical data, withthe goal of subgrouping patients into a high-risk and in a low-risk category. Three hundred seventy-one patientswere used to train the network and 82 patients were used to validate the model. The cytologic smearswere 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 approximately33% at histology) and a low-risk group (cancer rate of 3%). Only cytologic parameters contributedto this classification. Analysis of the receiver operating characteristic (ROC) curves demonstrated that the ANNmodel discriminated with higher sensitivity and specificity between benign and malignant nodules comparedto standard cytologic criteria (p 0.001). This value did not show degradation when ANN predictions wereapplied to the validation series of 82 nodules. In conclusion, neural network analysis of cytologic data may bea useful tool to refine the risk of cancer in patients with lesions diagnosed as indeterminate by FNAB.