Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules. Issue 113 (April 2019)

• Record Type: Journal Article
• Authors: Ouyang, Fu-sheng; Guo, Bao-liang; Ouyang, Li-zhu; Liu, Zi-wei; Lin, Shao-jia; Meng, Wei; Huang, Xi-yi; Chen, Hai-xiong; Qiu-gen, Hu; Yang, Shao-ming
• Is Part Of: European journal of radiology. Issue 113(2019)
• Page Start: 251
• Page End: 257
• Publication Date: 2019-04
• Subjects: US ultrasonography -- Lasso east absolute shrinkage and selection operator -- RF random forest -- k-SVM Kernel Support Vector Machines -- Nnet neural network -- KNN K-nearest neighborhood -- NB naive bayes -- EN elastic net -- AUC area under the curve -- CI confidence interval -- FNA fine-needle aspiration; Thyroid nodule -- Ultrasonography -- Diagnosis -- Machine learning -- Area under the curve; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2019.02.029
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Ultrasound accuracy in the classification of thyroid nodules depends heavily on examiner experience. Machine-learning algorithms may improve the medical prediction process. Random forest outperformed other machine-learning algorithms for evaluating thyroid nodules. Abstract: Background: A key challenge in thyroid carcinoma is preoperatively diagnosing malignant thyroid nodules. The purpose of this study was to compare the classification performance of linear and nonlinear machine-learning algorithms for the evaluation of thyroid nodules using pathological reports as reference standard. Methods: Ethical approval was obtained for this retrospective analysis, and the informed consent requirement was waived. A total of 1179 thyroid nodules (training cohort, n = 700; validation cohort, n = 479) were confirmed by pathological reports or fine-needle aspiration (FNA) biopsy. The following ultrasonography (US) featu res were measured for each nodule: size (maximum diameter), margins, shape, aspect ratio, capsule, hypoechoic halo, composition, echogenicity, calcification pattern, vascularity, and cervical lymph node status. We analyzed five nonlinear and three linear machine-learning algorithms. The diagnostic performance of each algorithm was compared by using the area under the curve (AUC) of the receiver operating characteristic curve. We repeated this process 1000 times to obtain the mean AUC and 95% confidence interval (CI). Results: Overall, nonlinear machine-learning algorithms demonstrated similar AUCs compared with linear algorithms. The Random Forest and Kernel Support Vector Machines algorithms achieved slightly greater AUCs in the validation cohort (0.954, 95% CI: 0.939–0.969; 0.954 95%CI: 0.939–0.969, respectively) than other algorithms. Conclusions: Overall, nonlinear machine-learning algorithms share similar performance compared with linear algorithms for the evaluation the malignancy risk of thyroid nodules.