Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest

作者：Dan Chen, Jun Hu, Mei Zhu, Niansheng Tang, Yang Yang‐Hartwich, Yuran Feng · 发表于：BioData Mining · 年份：2020 · DOI：10.1186/s13040-020-00223-w · 被引用次数：38 · 研究领域：Thyroid Cancer Diagnosis and Treatment、AI in cancer detection、Artificial Intelligence in Healthcare and Education

BACKGROUND: Various combinations of ultrasonographic (US) characteristics are increasingly utilized to classify thyroid nodules. But they lack theories, and heavily depend on radiologists' experience, and cannot correctly classify thyroid nodules. Hence, our main purpose of this manuscript is to select the US characteristics significantly associated with malignancy and to develop an efficient scoring system for facilitating ultrasonic clinicians to correctly identify thyroid malignancy. METHODS: A logistic regression (LR) model is utilized to identify the potential thyroid malignancy, and the least absolute shrinkage and selection operator (LASSO) method is adopted to simultaneously select US characteristics significantly associated with malignancy and estimate parameters in LR model. Based on the selected US characteristics, we calculate the probability for each of thyroid nodules via random forest (RF) and extreme learning machine (ELM), and develop a scoring system to classify thyroid nodules. For comparison, we also consider eight state-of-the-art methods such as support vector machine (SVM), neural network (NET), etc. The area under the receiver operating characteristic curve (AUC) is employed to measure the accuracy of various classifiers. RESULTS: The US characteristics: nodule size, AP/T≥1, solid component, micro-calcifications, hackly border, hypoechogenicity, presence of halo, unclear border, irregular margin, and central vascularity are selected as the significant ...