Fu-Sheng Ouyang1, Bao-Liang Guo1, Li-Zhu Ouyang2, Zi-Wei Liu1, Shao-Jia Lin1, Wei Meng1, Xi-Yi Huang3, Hai-Xiong Chen1, Hu Qiu-Gen4, Shao-Ming Yang5. 1. Department of Radiology, Shunde Hospital of Southern Medical University, Foshan, Guangdong, PR China. 2. Department of Ultrasound, Shunde Hospital, Southern Medical University, Foshan, Guangdong, PR China. 3. Department of Laboratory, Lecong Hospital of Shunde, Foshan, Guangdong, PR China. 4. Department of Radiology, Shunde Hospital of Southern Medical University, Foshan, Guangdong, PR China. Electronic address: qiugenhu@126.com. 5. Department of Radiology, Shunde Hospital of Southern Medical University, Foshan, Guangdong, PR China. Electronic address: ysmsin@aliyun.com.
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.
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.
Authors: A Pontecorvi; S Nanni; C Possieri; P Locantore; C Salis; L Bacci; A Aiello; G Fadda; C De Crea; M Raffaelli; R Bellantone; C Grassi; L Strigari; A Farsetti Journal: Endocrine Date: 2020-10-08 Impact factor: 3.633
Authors: Dat Tien Nguyen; Jin Kyu Kang; Tuyen Danh Pham; Ganbayar Batchuluun; Kang Ryoung Park Journal: Sensors (Basel) Date: 2020-03-25 Impact factor: 3.576