title: A model to discriminate malignant from benign thyroid nodules using artificial neural network.
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.
  
  
  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<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% (AURO  = 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.
  
  
  CONCLUSIONS
  ANN constructed by sonographic features can discriminate benign and malignant thyroid nodules with high diagnostic accuracy.
authors.affiliation: Department of Radiation Oncology and Chemotherapy, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, China.
authors.forename: Lu-Cheng
authors.initials: LC
authors.lastname: Zhu
authors.affiliation: Department of Oncology, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, China.
authors.forename: Yun-Liang
authors.initials: YL
authors.lastname: Ye
authors.affiliation: Department of Radiation Oncology and Chemotherapy, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, China.
authors.forename: Wen-Hua
authors.initials: WH
authors.lastname: Luo
authors.affiliation: Department of Radiation Oncology and Chemotherapy, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, China.
authors.forename: Meng
authors.initials: M
authors.lastname: Su
authors.affiliation: Department of Radiation Oncology and Chemotherapy, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, China.
authors.forename: Hang-Ping
authors.initials: HP
authors.lastname: Wei
authors.affiliation: Department of Radiation Oncology and Chemotherapy, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, China.
authors.forename: Xue-Bang
authors.initials: XB
authors.lastname: Zhang
authors.affiliation: Department of Radiation Oncology and Chemotherapy, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, China.
authors.forename: Juan
authors.initials: J
authors.lastname: Wei
authors.affiliation: Department of Radiation Oncology and Chemotherapy, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, China.
authors.forename: Chang-Lin
authors.initials: CL
authors.lastname: Zou
mesh_terms: D000328:Adult; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D013964:Thyroid Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology; D016606:Thyroid Nodule / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology; D014463:Ultrasonography