[Applicational value of 2017 ACR TI-RADS stratification in diagnosing thyroid nodules]

To summarize and evaluate the value of applying the thyroid imaging reporting and data system (TI-RADS) released by American College of Radiology (ACR) in 2017 of the thyroid classification, and to propose an optimized classification method based on the result to facilitate more accurate and precise risk stratification of thyroid nodules.

In the study, 342 thyroid nodules assessed by 2017 ACR TI-RADS were retrospectively analyzed. Each nodule had a score, and all the scores of nodules were compared with the pathological results. The proportion of malignant nodules in different scoring ranges was obtained. The diagnostic efficacy of all nodules, nodules above 1 cm and less than or equal to 1 cm was evaluated by ROC curve, respectively.

The AUC of all nodules, nodules above 1 cm and less than or equal to 1 cm were 0.907, 0.936 and 0.717, respectively. With the increase of the scores, the proportion of benign nodules decreased gradually, and the proportion of malignant nodules increased, especially nodules of 4-6 scores increased significantly. Based on the proportion of malignant nodules with 3 scores, the proportion of malignant nodules with 4, 5 and 6 scores increased 1.6, 3.8 and 5.3 times, respectively. The proportion of malignant nodules with 6-8 scores was 81%-84%, while the proportion of malignant nodules with 9 scores or more was 93%-94%. According to the distribution characteristics of malignant nodules, the classification of TI-RADS was adjusted. TI-RADS 4 was divided into TI-RADS 4a, TI-RADS 4b and TI-RADS 4c, corresponding to 4, 5 and 6-8 scores respectively, while the nodules with 9 scores or more were divided into TI-RADS 5.

2017 ACR TI-RADS has high diagnostic value for thyroid nodules above 1 cm, but it is not so effective for the nodules less than or equal to 1 cm. According to the proportion distribution of malignant nodules in different scoring ranges, appropriate adjustment of classification will be more accurate and precisely predict the malignant risk of nodules.