Risk stratification of indeterminate thyroid nodules using ultrasound and machine learning algorithms.

Background: Indeterminate thyroid nodules (Bethesda III) are challenging to characterize without diagnostic surgery. Auxiliary strategies including molecular analysis, machine learning models, and ultrasound grading with Thyroid Imaging, Reporting and Data System (TI-RADS) can help to triage accordingly, but further refinement is needed to prevent unnecessary surgeries and increase positive predictive values.

Design: Retrospective review of 88 patients with Bethesda III nodules who had diagnostic surgery with final pathological diagnosis.