Thyroid imaging reporting and data system (TIRADS) for ultrasound features of nodules: multicentric retrospective study in China

Song, Yanyan; Wei, Xi; Zhang, Sheng; Zhang, Ruifang; Gu, Ying; Xia, Chen; Luo, Xiaomao; Yang, Lichun; Zhao, Hong; Zhang, Hua; Jia, Xiaohong; Dong, Yijie; Zhang, Jingwen; Yang, Zhifang; Zhang, Huiting; Zheng, Yi; Xu, Wenwen; Lai, Limei; Luo, Bao Ming; Huang, Bei; Wen, Chaoyang; Mi, Chengrong; Dao-zhong, Huang; Xue, Ensheng; Wu, Gang; Du, Guoqing; Ran, Haitao; Xiang, Huijuan; Zhu, Jiaan; Wang, Jian; Li, JianChu; Tang, Jie; Li, Jing; Yang, Jingchun; Zhang, Lei; Cui, Ligang; Bao, Liang; Yin, Long; Lu, Man; Zhu, Mei; Chen, Min; Yuzhen, Nima; Zhang, Peng-Fei; Wu, Rong; Guo, Renhua; Hao, Shaoyun; Fang, Shibao; Chen, Tao; Zhan, Wei; Zhu, Ying; Li, YingJia; Lu, Yongping; Deng, Yansha; Zheng, Yuanyi; Chen, Yue; Luo, Yukun; Peng, Yulan; Bai, Bin; YuanJin, Cai; Chang, Xin; Guihua, Che; Fu, Chen; Chen, Hongtian; Chen, Hongyan; Hui-pin, Chen; Chen, Jiehuan; nianQiao, Chen; Wu, Chen; Chen, Xinguang; Chen, Xiuping; Guanghe, Cui; LiPing, Dai; Deng, Xuedong; Dong, Lili; Du, Gang; Chao, Fang; Fang, Fengkai; Fei, Zhengdong; Li, Feng; Jian, Fu; Guan, Lina; Guo, Jun; Wen, Huaxuan; He, Na; He, Shaozheng; He, Xiaofeng; AiQin, Hou; Hu, Jie; LiYan, Hu; DingWei, Huang; JianYuan, Huang; Huang, Li; PeiPei, Huang; Huang, Weiwei; Jia, Ling; Jiang, Xinhui; Hj, Kang; XiangChong, Kong; XiaoQing, Lei; AnYang, Li; Chen, Li; Cheng, Li; Li, Chuanyin; Liu, Dong; Li, Haiyan; Li, Hongmei; Li, Huiwen; Li, Jianxin; Li, Ning; Li, Qiaoying; Li, Qinying; Li, Tao; Li, Wendong; XingYun, Li; Liang, Guosheng; Lin, Jie; Liu, Aihua; HongMin, Liu; Liu, Jia; K, Liu; Liu, Yanchao; Lou, Kexin; Lu, Yehu; Mao, Feng; Juan, Miao; Ni, Xuejun; Pan, Xiaojie; Pang, Yun; Mei, Peng; ZhenYi, Peng; YanMin, Pi; Qi, Tingyue; QianMiao, Qin; Shunhua, Qing; Jian-feng, QU; Jinhe, Ren; Li.aiSha, Renagu; RongRong, Ru; Shen, Tao; HongWei, Shi; Shi, Jie; Shi, Lei; JinDuo, Shou; Liu, Song; DeMin, Su; AnYi, Sun; Zhuogui, Sun; Binhui, Tang; Na, Tang Li; Wan, Qing; Wang, Fang; Wang, Jing; Wang, Jinping; Li, Wang; Wang, Wei; Wang, Xinfang; Wang, Yali; Wang, Yanbin; Wang, Yanqing; Wang, Yanzhen; Wang, Yingchun; YuanSheng, Wang; ZhaoRui, Wang; Wu, Changjun; HaiYan, Wu; Wu, Jianli; Wu, Jian; Wu, Liang; Wu, Linsong; Qing, Wu; Wu, Tao; Ting, Wu; WenJing, Wu; ZhiLing, Wu; ZhongQiu, Wu; LiFang, Xiao; ChuanWen, Xie; Xie, Xianhe; Xu, Qiang; Dong, Xue; Yan, Jingbin; Yan, Jiping; JianQing, Yang; Yang, Jie; Yang, Qingya; Yang, Xiaoqing; XueWen, Yang; Yang, Yan; YingMei, Yang; Yinguang, Yang; Ye, Xinhua; Ye, Yuquan; You, Tao; Liang, Yu; Yu, Xiaoling; Hui, Yuan; Zhihong, Yuan; Liang, Ze; Zeng, Shu-E; Zhang, Hui; Zhang, Jian; Zhang, Jianlei; Zhang, Lijuan; Zhang, Lili; Na, Zhang; Zhang, Panpan; QunXia, Zhang; Zhang, Tong; Zhang, Wenjun; Zhang, Xiaodong; Zhang, Yan; Zhang, Yuhong; Zhang, Yuhua; Zhang, Yun‐Fei; Zhang, Zizhen; Zhao, Feng; Li, Zhao; Zhao, Yu; Zhou, Hong; Zhou, Jianqiao; Zhou, Na; Zhou, Peng; Zhou, Ruhai; Zhou, Xian‐Li; Zhou, Yibo; Zhu, Bin; LiSha, Zhu; Zheng, Zhu; Bao, Zou

doi:10.1007/s12020-020-02442-x

Cited by 34 publications

(41 citation statements)

References 37 publications

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“…In the weighting method, the ORs were standardized using rounding to ensure appropriate application. In the counting method, the number of features with OR >1 was counted ( 21 ). The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and 95% CI of different variables were calculated, with histologic diagnosis as the gold standard.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study

et al. 2022

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PurposeTo develop a risk stratification system that can predict axillary lymph node (LN) metastasis in invasive breast cancer based on the combination of shear wave elastography (SWE) and conventional ultrasound.Materials and MethodsA total of 619 participants pathologically diagnosed with invasive breast cancer underwent breast ultrasound examinations were recruited from a multicenter of 17 hospitals in China from August 2016 to August 2017. Conventional ultrasound and SWE features were compared between positive and negative LN metastasis groups. The regression equation, the weighting, and the counting methods were used to predict axillary LN metastasis. The sensitivity, specificity, and the areas under the receiver operating characteristic curve (AUC) were calculated.ResultsA significant difference was found in the Breast Imaging Reporting and Data System (BI-RADS) category, the “stiff rim” sign, minimum elastic modulusof the internal tumor and peritumor region of 3 mm between positive and negative LN groups (p < 0.05 for all). There was no significant difference in the diagnostic performance of the regression equation, the weighting, and the counting methods (p > 0.05 for all). Using the counting method, a 0–4 grade risk stratification system based on the four characteristics was established, which yielded an AUC of 0.656 (95% CI, 0.617–0.693, p < 0.001), a sensitivity of 54.60% (95% CI, 46.9%–62.1%), and a specificity of 68.99% (95% CI, 64.5%–73.3%) in predicting axillary LN metastasis.ConclusionA 0–4 grade risk stratification system was developed based on SWE characteristics and BI-RADS categories, and this system has the potential to predict axillary LN metastases in invasive breast cancer.

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Section: Methodsmentioning

confidence: 99%

Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study

et al. 2022

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“…The C-TIRADS features of the thyroid lesions were regarded as suspicious US features for malignancy: vertical orientation, ill-defined or irregular margins (including extrathyroidal extension), microcalcifications, solid, and markedly hypoechoic [ 7 ].…”

Section: Methodsmentioning

confidence: 99%

Clinicopathological and ultrasound features as risk stratification predictors of clinical and pathological nodal status in papillary thyroid carcinoma: a study of 748 patients

Zhang

et al. 2022

BMC Cancer

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Background Papillary thyroid carcinoma (PTC) is the most common histological type of thyroid malignancy that tends to metastasize to cervical lymph nodes. In the present study, we aimed to investigate which clinicopathologic and ultrasound features of PTC are associated with clinical lymph node metastasis (LNM) and numbers of pathological LNM. Methods From January 2016 to December 2018, we identified a cohort of patients with PTC who underwent cervical ultrasonography and were diagnosed through operation and pathology. Clinical N1(cN1) and > 5 pathologic N1(pN1) were considered in the postoperative stratification to have an intermediate risk according to the 2015 ATA guidelines. Clinicopathological and ultrasound features in PTC patients were performed in accordance with the independent risk factors of cN1 and > 5pN1 respectively by using the univariate and multivariate analyses. Results We collected 748 PTC patients in the final inclusion criteria. There were 688 cN0 cases and 60 cN1 cases. From the analyses, primary tumor size > 2 cm, capsule contact, extrathyroidal extensions (ETE) and central LNM remained independent risk factors for cN1 in PTC patients. In the 748 PTC patients, 707 cases had ≤ 5 pN1, and 41 cases had > 5 pN1. Multifocality, primary tumor size > 2 cm, capsule contact and ETE are significant independent risk factors for > 5 pN1. Conclusions We concluded that multifocality, primary tumor size > 2 cm, capsule contact, ETE and central LNM were independent risk factors for the intermediate risk stratification in patients with PTC. Ultrasonography is a good technique for the preoperative lymph node staging of PTC and is helpful for detecting LNM.

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“…A multicentric retrospective validation study on 2141 thyroid nodules that were neither cystic nor spongiform was simultaneously published [21]. It was designed to determine which of three methods, namely regression equation, weighting, and counting would be the most suitable to determine the malignant risk of thyroid nodules.…”

Section: C-tirads (Chinese-tirads) (2020)mentioning

confidence: 99%

The New Era of TIRADSs to Stratify the Risk of Malignancy of Thyroid Nodules: Strengths, Weaknesses and Pitfalls

Russ

Trimboli

Buffet

2021

Cancers

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Since 2009, thyroid imaging reporting and data systems (TI-RADS) have been playing an increasing role in the field of thyroid nodules (TN) imaging. Their common aims are to provide sonologists of varied medical specialties and clinicians with an ultrasound (US) based malignancy risk stratification score and to guide decision making of fine-needle aspiration (FNA). Schematically, all TI-RADSs scores can be classified as either pattern-based or point-based approaches. The main strengths of these systems are their ability (i) to homogenize US TN descriptions among operators, (ii) to facilitate and shorten communication on the malignancy risk of TN between sonologists and clinicians, (iii) to provide quantitative ranges of malignancy risk assessment with high sensitivity and negative predictive values, and (iv) to reduce the number of unnecessary FNAs. Their weaknesses are (i) the remaining inter-observer discrepancies and (ii) their insufficient sensitivity for the diagnosis of follicular cancers and follicular variant of papillary cancers. Most common pitfalls are degenerating shrinking nodules and confusion between individual and coalescent nodules. The benefits of all TI-RADSs far outweigh their shortcomings, explaining their rising use, but the necessity to improve and merge the different existing systems remains.

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Thyroid imaging reporting and data system (TIRADS) for ultrasound features of nodules: multicentric retrospective study in China

Cited by 34 publications

References 37 publications

Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study

Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study

Clinicopathological and ultrasound features as risk stratification predictors of clinical and pathological nodal status in papillary thyroid carcinoma: a study of 748 patients

The New Era of TIRADSs to Stratify the Risk of Malignancy of Thyroid Nodules: Strengths, Weaknesses and Pitfalls

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