Background: The diagnosis of malignant thyroid nodules is mainly based on the fine-needle aspiration biopsy (FNAB). To improve the detection of malignant nodules, different molecular tests have been developed. We present a new molecular signature based on altered miRNA expressions and specific mutations. Methods: This is a prospective non-interventional study, including all Bethesda categories, carried out on an FNAB sampled in suspicious nodule(s) during thyroidectomy. miRNA quantification and mutations detection were performed. The reference diagnosis was the pathological assessment of the surgical specimen. Different classification algorithms were trained with molecular data to correctly classify the samples. Results: A total of 294 samples were recorded and randomly divided in two equal groups. The random forest algorithm showed the highest accuracy and used mostly miRNAs to classify the nodules. The sensitivity and the specificity of our signature were, respectively, 76% and 96%, and the positive and negative predictive values were both 90% (disease prevalence of 30%). Conclusions: We have identified a molecular classifier that combines miRNA expressions with mutations detection. This signature could potentially help clinicians, as complementary to the Bethesda classification, to discriminate indeterminate FNABs.
背景：恶性甲状腺结节的诊断主要依赖于细针穿刺活检（FNAB）。为提高恶性结节的检出率，多种分子检测方法已得到开发。本研究提出一种基于miRNA表达变化与特定突变的新型分子标志物组合。 方法：本研究为一项前瞻性非干预性研究，涵盖所有Bethesda分类类别，研究对象为甲状腺切除术中对可疑结节进行FNAB取样获得的样本。研究进行了miRNA定量分析与突变检测，以手术标本的病理学评估作为金标准诊断。采用不同分类算法对分子数据进行训练，以实现对样本的准确分类。 结果：共纳入294例样本，随机均分为两组。随机森林算法显示出最高的分类准确度，且主要依赖miRNA特征进行结节分类。该分子标志物组合的敏感性与特异性分别为76%和96%，阳性预测值与阴性预测值均为90%（疾病患病率为30%）。 结论：我们开发了一种融合miRNA表达与突变检测的分子分类器。该标志物组合可作为Bethesda分类系统的补充手段，有望帮助临床医生鉴别诊断性质不确定的FNAB样本。
Description of a New miRNA Signature for the Surgical Management of Thyroid Nodules
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