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Published on: August 23, 2019
Clinical decision support analysis of a microRNA-based thyroid molecular classifier: A real-world, prospective and
Marcos Tadeu Santos1, Bruna Moretto Rodrigues2, Satye Shizukuda2
1Research and Development (R&D), Onkos Molecular Diagnostics, Ribeirão Preto, SP, Brazil; Molecular Oncology Research Centre, Barretos Cancer Hospital, Barretos, SP, Brazil.
Background:
The 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.
Methods:
We 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.
Findings:
A 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.
Interpretation:
The 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.
Funding:
The São Paulo Research-Foundation (FAPESP) and Onkos.
