From the Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China (J.L., J.Y., P.L.); Department of Ultrasound, The First Affiliated Hospital of Henan University of CM, Henan, China (C.L.); Department of Ultrasound Diagnostics, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Hunan, China (X.Z.); Department of Ultrasound, Peking University Third Hospital, Beijing, China (J.H.); Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China (P.Y.); Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China (Y.C.); Department of Ultrasound, Tianjin Medical University General Hospital, Tianjin, China (H.Z.); Department of Otolaryngology-Head & Neck Surgery, The Second Affiliated Hospital of Guilin Medical University, Guangxi, China (R.H.); Department of Ultrasound, Traditional Chinese Medical Hospital of Xinjiang, Xinjiang, China (Y.M.); Department of Pathology, First Medical Center of Chinese PLA General Hospital, Beijing, China (X.G.); and Department of Pathology, Affiliated Hospital of Hebei Engineering University, Hebei, China (Y.Z.).
Radiology. 2023 Nov;309(2):e230949. doi: 10.1148/radiol.230949.
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 < .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; = .02), K-TI-RADS (AUC, 0.69; 95% CI: 0.61, 0.76; = .002), and C-TI-RADS (AUC, 0.68; 95% CI: 0.60, 0.75; = .002). Conclusion F-TI-RADS outperformed existing RSSs for differentiating between FTC and FTA. © RSNA, 2023 See also the editorial by Baumgarten in this issue.
背景 当前适用于甲状腺乳头状瘤的美国放射学会（ACR）风险分层系统（RSS）在评估滤泡性甲状腺肿瘤时具有挑战性。目的 开发一种基于超声特征的 RSS，用于区分经活检证实的滤泡性肿瘤中的滤泡状甲状腺腺瘤（FTA）和滤泡状甲状腺癌（FTC），并与现有的 RSS 进行比较。材料与方法 本回顾性多中心研究纳入了 2018 年 1 月至 2022 年 12 月期间来自 7 个中心的连续成年患者，这些患者均接受了常规超声检查，并最终被诊断为滤泡性甲状腺肿瘤。预训练数据集的超声图像用于提高读者对 FTC 和 FTA 的超声特征的理解。单变量和多变量逻辑回归分析用于评估训练数据集中定性超声特征与 FTC 的相关性。具有 <.05 的特征用于构建预测模型（滤泡性肿瘤模型，称为 F 模型）和甲状腺影像报告和数据系统（TI-RADS）下的滤泡性肿瘤 RSS。在验证数据集中，比较了滤泡性 TI-RADS（以下简称 F-TI-RADS）与现有的 RSS（ACR TI-RADS、韩国甲状腺放射学会和韩国放射学会 TI-RADS[以下简称 K-TI-RADS]和中国 TI-RADS[以下简称 C-TI-RADS]）之间的受试者工作特征曲线下面积（AUC）。结果 在预训练、训练和验证数据集中，分别纳入了 30 例（平均年龄，47.6 岁 ± 16.0[标准差]；16 例男性；FTCs，30/60[50.0%]）、703 例（平均年龄，47.9 岁 ± 14.5；530 例女性；FTCs，188/703[26.7%]）和 155 例（平均年龄，49.9 岁 ± 13.3[标准差]；155 例女性；FTCs，43/155[27.7%]）患者。在验证数据集中，F-TI-RADS 与 ACR TI-RADS（AUC，0.74；95%CI：0.66，0.80； =.02）、K-TI-RADS（AUC，0.69；95%CI：0.61，0.76； =.002）和 C-TI-RADS（AUC，0.68；95%CI：0.60，0.75； =.002）相比，在区分 FTA 和 FTC 方面表现出更好的性能（AUC，0.81；95%CI：0.71，0.86）。结论 F-TI-RADS 在区分 FTC 和 FTA 方面优于现有的 RSS。©RSNA，2023 请参见本期社论中的 Baumgarten 的文章。
