AI TI-RADS (Artificial Intelligence Thyroid Imaging Reporting and Data System) is a data-driven analysis and revision of the 2017 ACR TI-RADS 1. Published in May 2019 2, this had the intention of simplifying categorization and improving specificity while maintaining high sensitivity. This system used a training set of 1325 nodules with known cytology and a genetic learning algorithm to optimize the current lexicon and scoring structure. This system was then applied by expert and nonexpert radiologists and compared with known ACR TI-RADS categories.
Changes to ACR TI-RADS
The five imaging characteristic categories were maintained with the following changes in each:
composition - only solid nodules receive points
- cystic and spongiform nodules again receive 0 points, but AI TI-RADS found this sufficient to assign benignity regardless of findings in any of the other categories
- mixed cystic / solid nodules received 0 points instead of 1 point
- solid or near completely solid nodules received 3 points instead of 2 points
- can't classify 0 points instead of 2 points
echogenicity - only hypoechoic nodules receive points
- iso/hyperechoic and cannot classify nodules 0 points instead of 1 point
- taller-than-wide nodules only 1 point instead of 3 points
- no change
- macrocalcification receive 0 points instead of 1 point
This has streamlined the imaging criteria, with points being awarded for solid, hypoechoic, taller-than-wide, irregular margined nodules with microcalcifications and no other significant findings.
The point level for each TR category and recommendation for FNA by size remained the same.
Outcome comparison with ACR TI-RADS
Tested against 100 nodules, sensitivity was identical between AI and ACR TI-RADS (93%). Specificity was increased for AI TI-RADS at 65% compared with 47% with a single expert reader, and also an increase of 55% from 47% in a non-expert reader group.
43 nodules were down categorized, with 15 nodules not meeting requirement for FNA (all of which were benign). No extra malignancies were missed using the AI TI-RADS scoring algorithm.
- 1. Tessler FN, Middleton WD, Grant EG, Hoang JK, Berland LL, et al. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. (2017) Journal of the American College of Radiology : JACR. 14 (5): 587-595. doi:10.1016/j.jacr.2017.01.046 - Pubmed
- 2. Benjamin Wildman-Tobriner, Mateusz Buda, Jenny K. Hoang, William D. Middleton, David Thayer, Ryan G. Short, Franklin N. Tessler, Maciej A. Mazurowski. Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility. (2019) Radiology. doi:10.1148/radiol.2019182128 - Pubmed
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