US Risk Stratification System for Follicular Thyroid Neoplasms

Background Preoperative assessment of follicular thyroid neoplasms is challenging using the current US risk stratification systems (RSSs) that are applicable to papillary thyroid neoplasms. Purpose To develop a US feature-based RSS for differentiating between follicular thyroid adenoma (FTA) and follicular thyroid carcinoma (FTC) in biopsy-proven follicular neoplasm and compare it with existing RSSs. Materials and Methods This retrospective multicenter study included consecutive adult patients who underwent conventional US and received a final diagnosis of follicular thyroid neoplasm from seven centers between January 2018 and December 2022. US images from a pretraining data set were used to improve readers' understanding of the US characteristics of the FTC and FTA. Univariable and multivariable logistic regression analyses were used to assess the association of qualitative US features with FTC in a training data set. Features with P < .05 were used to construct a prediction model (follicular tumor model, referred to as F model) and RSS for follicular neoplasms using the Thyroid Imaging Reporting and Data System (TI-RADS). Area under the receiver operating characteristic curve (AUC) was compared between follicular TI-RADS (hereafter, F-TI-RADS) and existing RSS (American College of Radiology [ACR] TI-RADS, Korean Society of Thyroid Radiology and Korean Society of Radiology TI-RADS [hereafter, referred to as K-TI-RADS], and Chinese TI-RADS [hereafter, referred to as C-TI-RADS]) in a validation data set. Results The pretraining, training, and validation data sets included 30 (mean age, 47.6 years ± 16.0 [SD]; 16 male patients; FTCs, 30 of 60 [50.0%]), 703 (mean age, 47.9 years ± 14.5; 530 female patients; FTCs, 188 of 703 [26.7%]), and 155 (mean age, 49.9 years ± 13.3 [SD]; 155 female patients; FTCs, 43 of 155 [27.7%]) patients. In the validation data set, the F-TI-RADS showed improved performance for differentiating between FTA and FTC (AUC, 0.81; 95% CI: 0.71, 0.86) compared with ACR TI-RADS (AUC, 0.74; 95% CI: 0.66, 0.80; P = .02), K-TI-RADS (AUC, 0.69; 95% CI: 0.61, 0.76; P = .002), and C-TI-RADS (AUC, 0.68; 95% CI: 0.60, 0.75; P = .002). Conclusion F-TI-RADS outperformed existing RSSs for differentiating between FTC and FTA. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Baumgarten in this issue.