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

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

Abstract

Objective: This 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 methods: 618 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. Results: Six sonographic features including shape (Taller than wide, p

Suggested Citation

  • Lu-Cheng Zhu & Yun-Liang Ye & Wen-Hua Luo & Meng Su & Hang-Ping Wei & Xue-Bang Zhang & Juan Wei & Chang-Lin Zou, 2013. "A Model to Discriminate Malignant from Benign Thyroid Nodules Using Artificial Neural Network," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-6, December.
    • Handle: RePEc:plo:pone00:0082211
    DOI: 10.1371/journal.pone.0082211
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    1. Oecd, 2009. "Executive Summary and Recommendations," OECD Journal on Budgeting, OECD Publishing, vol. 9(1), pages 13-22.
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