A Model to Discriminate Malignant from Benign Thyroid Nodules Using Artificial Neural Network

Lu-Cheng Zhu,Chang-Lin Zou,Juan Wei,Xue-Bang Zhang,Wen-Hua Luo,Yun-Liang Ye,Meng Su,Hang-Ping Wei,Alok Deoraj

doi:10.1371/journal.pone.0082211

Abstract

ObjectiveThis study aimed to construct a model for using in differentiating benign and malignant nodules with the artificial neural network and to increase the objective diagnostic accuracy of US.Materials and methods618 consecutive patients (528 women, 161 men) with 689 thyroid nodules (425 malignant and 264 benign nodules) were enrolled in the present study. The presence and absence of each sonographic feature was assessed for each nodule - shape, margin, echogenicity, internal composition, presence of calcifications, peripheral halo and vascularity on color Doppler. The variables meet the following criteria: important sonographic features and statistically significant difference were selected as the input layer to build the ANN for predicting the malignancy of nodules.ResultsSix sonographic features including shape (Taller than wide, p<0.001), margin (Not Well-circumscribed, p<0.001), echogenicity (Hypoechogenicity, p<0.001), internal composition (Solid, p<0.001), presence of calcifications (Microcalcification, p<0.001) and peripheral halo (Absent, p<0.001) were significantly associated with malignant nodules. A three-layer 6-8-1 feed-forward ANN model was built. In the training cohort, the accuracy of the ANN in predicting malignancy of thyroid nodules was 82.3% (AUROC = 0.818), the sensitivity and specificity was 84.5% and 79.1%, respectively. In the validation cohort, the accuracy, sensitivity and specificity was 83.1%, 83.8% and 81.8%, respectively. The AUROC was 0.828.ConclusionANN constructed by sonographic features can discriminate benign and malignant thyroid nodules with high diagnostic accuracy.

Highlights

Nodular thyroid disease is a common finding in the general population, in iodine-deficient areas
artificial neural network (ANN) constructed by sonographic features can discriminate benign and malignant thyroid nodules with high diagnostic accuracy
The diagnosis of thyroid cancer relies on cervical ultrasound and fineneedle aspiration (FNA) biopsy, which collects cells for cytological examination [6,7]

Summary

Introduction

Nodular thyroid disease is a common finding in the general population, in iodine-deficient areas. The diagnosis of thyroid cancer relies on cervical ultrasound and fineneedle aspiration (FNA) biopsy, which collects cells for cytological examination [6,7]. FNA cytology is currently the most reliable diagnostic tool for evaluation of thyroid nodules. It provides a definitive diagnosis of benign or malignant thyroid disease in most cases. In 20% to 30% of nodules, FNA cytology cannot reliably rule out cancer, and such cases are reported as indeterminate for malignancy [8,9]. To improve the diagnosis accuracy, new diagnostic approaches combined FNA cytology and molecular biomarkers were proposed in recent years [10,11,12]. CT and MRI have a limited role in the initial evaluation of solitary nodule and their indications include suspected tracheal involvement, either by invasion or compression, extension into the mediastinum, or recurrent disease[5,13,14,15]

Methods

Results

Conclusion