ACR TI-RADS classification combined with number of nodules, halo features optimizes diagnosis and prediction of follicular thyroid cancer

Abstract

Objectives: To investigate the application value of The American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) category combined with other ultrasound features of nodules in distinguishing follicular thyroid carcinoma (FTC) from thyroid follicular adenoma (FTA).

Methods: We collected and retrospectively analyzed clinical and ultrasound data for 118 and 459 patients with FTCs and FTAs, respectively, at our hospital. Next, we used ACR TI-RADS classification combined with other ultrasound features of nodules to distinguish FTC from FTA. Multivariate Logistic regression was used to screen independent risk factors for FTC, which were subsequently used to construct a nomogram for predicting FTC.

Results: ACR TI-RADS categories 4 and 5, unilateral multiple nodules, and halo thickness≥2 mm were independent risk factors for FTC. ACR TI-RADS category combined with number of nodules, halo features of the nodule was a significantly better prediction model for FTC diagnosis (AUC = 0.869) than that of ACR TI-RADS classification alone (AUC = 0.756).

Conclutions: Clinicians need to pay attention to the halo of nodules when distinguishing FTA from FTC. Notably, ACR TI-RADS combined with other nodule ultrasound features has superior predictive performance in diagnosis of FTC compared to ACR TI-RADS classification alone, thus can provide an important reference value for preoperative diagnosis of FTC.

Keywords: ACR TI-RADS; thyroid follicular adenoma; thyroid follicular carcinoma; ultrasound.