Improved Quality of Thyroid Ultrasound Reports After Implementation of the ACR Thyroid Imaging Reporting and Data System Nodule Lexicon and Risk Stratification System

2018 ◽  
Vol 15 (5) ◽  
pp. 743-748 ◽  
Author(s):  
Andrew S. Griffin ◽  
Jason Mitsky ◽  
Upma Rawal ◽  
Abraham J. Bronner ◽  
Franklin N. Tessler ◽  
...  
2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Fei Chen ◽  
Yungang Sun ◽  
Guanqi Chen ◽  
Yuqian Luo ◽  
Guifang Xue ◽  
...  

Background. This study is aimed at evaluating the diagnostic efficacy of ultrasound-based risk stratification for thyroid nodules in the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) and the American Thyroid Association (ATA) risk stratification systems. Methods. 286 patients with thyroid cancer were included in the tumor group, with 259 nontumor cases included in the nontumor group. The ACR TI-RADS and ATA risk stratification systems assessed all thyroid nodules for malignant risks. The diagnostic effect of ACR and ATA risk stratification system for thyroid nodules was evaluated by receiver operating characteristic (ROC) analysis using postoperative pathological diagnosis as the gold standard. Results. The distributions and mean scores of ACR and ATA rating risk stratification were significantly different between the tumor and nontumor groups. The lesion diameter > 1  cm subgroup had higher malignant ultrasound feature rates detected and ACR and ATA scores. A significant difference was not found in the ACR and ATA scores between patients with or without Hashimoto’s disease. The area under the receiver operating curve (AUC) for the ACR TI-RADS and the ATA systems was 0.891 and 0.896, respectively. The ACR had better specificity (0.90) while the ATA system had higher sensitivity (0.92), with both scenarios having almost the same overall diagnostic accuracy (0.84). Conclusion. Both the ACR TI-RADS and the ATA risk stratification systems provide a clinically feasible thyroid malignant risk classification, with high thyroid nodule malignant risk diagnostic efficacy.


2019 ◽  
Vol 8 (3) ◽  
pp. 1024-1033 ◽  
Author(s):  
Yun‐xia Huang ◽  
Yan‐zong Lin ◽  
Jin‐luan Li ◽  
Xue‐qing Zhang ◽  
Li‐rui Tang ◽  
...  

2012 ◽  
Vol 187 (4S) ◽  
Author(s):  
Jessica Lubahn ◽  
Nicholas Cost ◽  
Mehrad Adibi ◽  
Adam Romman ◽  
Ganesh Raj ◽  
...  

2021 ◽  
Author(s):  
Evert F.s. van Velsen ◽  
Robin P. Peeters ◽  
Merel T. Stegenga ◽  
F.j. van Kemenade ◽  
Tessa M. van Ginhoven ◽  
...  

Objective Recent research suggests that the addition of age improves the 2015 American Thyroid Association (ATA) Risk Stratification System for differentiated thyroid cancer (DTC). The aim of our study was to investigate the influence of age on disease outcome in ATA High Risk patients with a focus on differences between patients with papillary (PTC) and follicular thyroid cancer (FTC). Methods We retrospectively studied adult patients with High Risk DTC from a Dutch university hospital. Logistic regression and Cox proportional hazards models were used to estimate the effects of age (at diagnosis) and several age cutoffs (per five years increment between 20 and 80 years) on (i) response to therapy, (ii) developing no evidence of disease (NED), (iii) recurrence, and (iv) disease specific mortality (DSM). Results We included 236 ATA High Risk patients (32% FTC) with a median follow-up of 6 years. Age, either continuously or dichotomously, had a significant influence on having an excellent response after initial therapy, developing NED, recurrence, and DSM for PTC and FTC. For FTC, an age cutoff of 65 or 70 years showed the best statistical model performance, while this was 50 or 60 years for PTC. Conclusions In a population of patients with High Risk DTC, older age has a significant negative influence on disease outcomes. Slightly different optimal age cutoffs were identified for the different outcomes, and these cutoffs differed between PTC and FTC. Therefore, the ATA Risk Stratification System may further improve should age be incorporated as an additional risk factor.


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