عنوان انگلیسی مقاله:
Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules
ترجمه فارسی عنوان مقاله:
مقایسه الگوریتمهای یادگیری ماشین خطی و غیرخطی برای طبقه بندی ندولهای تیروئید
Sciencedirect - Elsevier - European Journal of Radiology, 113 (2019) 251-257: doi:10:1016/j:ejrad:2019:02:029
Fu-sheng Ouyanga,1, Bao-liang Guoa,1, Li-zhu Ouyangb,1, Zi-wei Liua, Shao-jia Lina, Wei Menga, Xi-yi Huangc, Hai-xiong Chena, Qiu-gen Hua,⁎, Shao-ming Yanga,⁎
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.
Keywords: Thyroid nodule | Ultrasonography | Diagnosis | Machine learning | Area under the curve