Description of a New miRNA Signature for the Surgical Management of Thyroid Nodules.

Simple Summary: We present a new molecular signature, based on altered miRNA expressions and specific mutations, allowing for improving the screening of malignant thyroid nodules. This is a prospective non-interventional study, including all Bethesda categories, carried out on an FNAB sampled in suspicious nodule(s) during thyroidectomy. The reference diagnosis was the pathological assessment of the surgical specimen. miRNA quantification and mutations detection were performed. Different classification algorithms were trained with molecular data to correctly classify the samples. The random forest was the best algorithm. This classifier used mostly miRNAs to classify the nodules. This classifier is able to identify malignant nodules with a high PPV and NPV (both 90%), a high specificity (96%) and a competitive sensitivity (76%). Our data suggest that miRNA expressions emerge as more reliable first-line diagnostic markers. This signature could be efficient to improve the screening of thyroid cancer as a complementary test in clinical practice, to reduce the rate of unnecessary surgery. 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. [ABSTRACT FROM AUTHOR]