RATIONALE AND OBJECTIVES: We sought to evaluate the diagnostic performance of an artificial neural network (ANN) and binary logistic regression (BLR) in differentiating malignant from benign thyroid nodules on ultrasonography. MATERIALS AND METHODS: Two experienced radiologists, who were unaware of the histopathological diagnosis, analyzed ultrasonographic (US) features of 109 pathologically proven thyroid lesions (49 malignant and 60 benign) in 96 patients. Each radiologist was asked to evaluate US findings and categorize nodules into one of the two groups (malignant vs. benign) in each case. The following 8 US parameters were assessed for each nodule: size, shape, margin, echogenicity, cystic change, microcalcification, macrocalcification, and halo sign. Statistically significant US findings were obtained with backward stepwise logistic regression and were used for training and testing of the ANN and the BLR. The performance of the ANN and BLR was compared to that of the radiologists using receiver-operating characteristic (ROC) analysis. RESULTS: Statistically significant US findings were size, margin, echogenicity, cystic change, and macrocalcification of the nodules. The area under the ROC curve (Az) values of ANN and BLR were 0.9492 +/- 0.0195 and 0.9046 +/- 0.0289, respectively. The Az value was 0.8300 +/- 0.0359 for reader 1 and 0.7600 +/- 0.0409 for reader 2. The Az values for ANN and BLR were significantly higher than those for both radiologists (all p < .05). CONCLUSION: The performance of the ANN and the BLR was better than that of the radiologists in the distinction of benign and malignant thyroid nodules.
RATIONALE AND OBJECTIVES: We sought to evaluate the diagnostic performance of an artificial neural network (ANN) and binary logistic regression (BLR) in differentiating malignant from benign thyroid nodules on ultrasonography. MATERIALS AND METHODS: Two experienced radiologists, who were unaware of the histopathological diagnosis, analyzed ultrasonographic (US) features of 109 pathologically proven thyroid lesions (49 malignant and 60 benign) in 96 patients. Each radiologist was asked to evaluate US findings and categorize nodules into one of the two groups (malignant vs. benign) in each case. The following 8 US parameters were assessed for each nodule: size, shape, margin, echogenicity, cystic change, microcalcification, macrocalcification, and halo sign. Statistically significant US findings were obtained with backward stepwise logistic regression and were used for training and testing of the ANN and the BLR. The performance of the ANN and BLR was compared to that of the radiologists using receiver-operating characteristic (ROC) analysis. RESULTS: Statistically significant US findings were size, margin, echogenicity, cystic change, and macrocalcification of the nodules. The area under the ROC curve (Az) values of ANN and BLR were 0.9492 +/- 0.0195 and 0.9046 +/- 0.0289, respectively. The Az value was 0.8300 +/- 0.0359 for reader 1 and 0.7600 +/- 0.0409 for reader 2. The Az values for ANN and BLR were significantly higher than those for both radiologists (all p < .05). CONCLUSION: The performance of the ANN and the BLR was better than that of the radiologists in the distinction of benign and malignant thyroid nodules.
Authors: David F Schneider; Linda M Cherney Stafford; Nicole Brys; Caprice C Greenberg; Courtney J Balentine; Dawn M Elfenbein; Susan C Pitt Journal: Endocr Pract Date: 2017-01-17 Impact factor: 3.443
Authors: Jeong-Kweon Seo; Young Jae Kim; Kwang Gi Kim; Ilah Shin; Jung Hee Shin; Jin Young Kwak Journal: Biomed Res Int Date: 2017-12-19 Impact factor: 3.411
Authors: Sae Rom Chung; Jung Hwan Baek; Min Kyoung Lee; Yura Ahn; Young Jun Choi; Tae Yon Sung; Dong Eun Song; Tae Yong Kim; Jeong Hyun Lee Journal: Korean J Radiol Date: 2020-03 Impact factor: 3.500