Clinical decision support analysis of a microRNA-based thyroid molecular classifier: A real-world, prospective and multicentre validation study.

Abstract

SummaryBackgroundThe diagnosis of cancer in Bethesda III/IV thyroid nodules is challenging as fine-needle aspiration (FNA) has limitations, and these cases usually require diagnostic surgery. As approximately 77% of these nodules are not malignant, a diagnostic test accurately identifying benign thyroid nodules can reduce “potentially unnecessary” surgery rates. We have previously reported the development and validation of a microRNA-based thyroid classifier (mir-THYpe) with high sensitivity and specificity, which could be performed directly from FNA smear slides. We sought to evaluate the performance of this test in real-world clinical routine to support clinical decisions and to reduce surgery rates.MethodsWe designed a real-world, prospective, multicentre study. Molecular tests were performed with FNA samples prepared at 128 cytopathology laboratories. Patients were followed-up from March 2018 until surgery or until March 2020 (patients with no indication for surgery). The final diagnosis of thyroid tissue samples was retrieved from postsurgical anatomopathological reports.FindingsA total of 435 patients (440 nodules) classified as Bethesda III/IV were followed-up. The rate of avoided surgeries was 52·5% for all surgeries and 74·6% for “potentially unnecessary” surgeries. The test achieved 89·3% sensitivity, 81·65% specificity, 66·2% positive predictive value, and 95% negative predictive value. The test supported 92·3% of clinical decisions.InterpretationThe reported data demonstrate that the use of the microRNA-based classifier in the real-world can reduce the rate of thyroid surgeries with robust performance and support clinical decision-making.FundingThe São Paulo Research-Foundation (FAPESP) and Onkos.