Zhao Hai-Na, Liu Jing-Yan, Lin Qi-Zhong, He Yu-Shuang, Luo Hong-Hao, Peng Yu-Lan, Ma Bu-Yun
Department of Ultrasound, West China Hospital of Sichuan University, Chengdu 610041, China.
Philips Research China.
Ann Transl Med. 2020 Apr;8(7):495. doi: 10.21037/atm.2020.03.211.
Thyroid carcinoma constitutes the vast majority of all thyroid cancer, most of which is the solid nodule type. No previous studies have examined combining both conventional and elastic sonography to evaluate the diagnostic performance of partially cystic thyroid cancer (PCTC). This retrospective study was designed to evaluate differentiation of PCTC from benign partially cystic nodules with a machine learning-assisted system based on ultrasound (US) and elastography.
Patients with suspicious partially cystic nodules and finally confirmed were included in the study. We performed conventional US and real-time elastography (RTE). The US features of nodules were recorded. The data set was entered into 6 machine-learning algorithms. Sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated.
A total of 177 nodules were included in this study. Among these nodules, 81 were malignant and 96 were benign. Wreath-shaped feature, micro-calcification, and strain ratio (SR) value were the most important imaging features in differential diagnosis. The random forest classifier was the best diagnostic model.
US features of PCTC exhibited unique characteristics. Wreath-shaped partially cystic nodules, especially with the appearance of micro-calcifications and larger SR value, are more likely to be malignant. The random forest classifier might be useful to diagnose PCTC.
甲状腺癌占所有甲状腺癌的绝大多数，其中大部分为实性结节型。以往尚无研究探讨联合应用传统超声和弹性超声来评估部分囊性甲状腺癌（PCTC）的诊断性能。本回顾性研究旨在通过基于超声（US）和弹性成像的机器学习辅助系统，评估PCTC与良性部分囊性结节的鉴别。
纳入可疑部分囊性结节并最终确诊的患者。我们进行了传统超声和实时弹性成像（RTE）检查。记录结节的超声特征。将数据集输入6种机器学习算法。计算敏感性、特异性、准确性和曲线下面积（AUC）。
本研究共纳入177个结节。其中，81个为恶性，96个为良性。环状特征、微钙化和应变率（SR）值是鉴别诊断中最重要的影像学特征。随机森林分类器是最佳诊断模型。
PCTC的超声特征具有独特性。环状部分囊性结节，尤其是伴有微钙化且SR值较大时，更有可能为恶性。随机森林分类器可能有助于PCTC的诊断。
