ACR TI-RADS 2017: Decoding Thyroid Nodule Categories and Management
General Information
ACR TI-RADS (Thyroid Imaging Reporting and Data System) is presented in the white paper of the ACR TI-RADS Committee: Tessler FN, Middleton WD, Grant EG et al. ACR thyroid imaging reporting and data system (TI-RADS): white paper of the ACR TI-RADS Committee. J Am Coll Radiol 2017; 14:587-593. The system is part of ultrasound-based risk stratification systems for thyroid nodule malignancy, used to standardize protocols and determine management strategies (Trimboli P, Durante C. Endocrine 2020).
Principle of Categorization
ACR TI-RADS is based on a scoring assessment of ultrasound features of the nodule, followed by forming a final risk category. Specific scores for each feature (composition, echogenicity, shape, margin, echogenic foci), cumulative ranges, and category assignments are not provided in the given excerpts [clarify].
FNA and Observation Thresholds
Indications for fine-needle aspiration (FNA) and dynamic observation in ACR TI-RADS depend on the final category and the maximum size of the nodule. Numerical thresholds for nodule sizes for each category are not provided in the given excerpts [clarify]; they should be verified from the primary source (Tessler FN et al., J Am Coll Radiol 2017).
Position Among Other Systems
Besides ACR TI-RADS, other stratification systems are used in clinical practice, notably EU-TIRADS of the European Thyroid Association (Russ G, Bonnema SJ, Erdogan MF et al. Eur Thyroid J 2017; 6(5): 225-237). Systematic reviews confirm the diagnostic value of TI-RADS systems for evaluating thyroid nodules (Wei X, Li Y, Zhang S et al. Tumour Biol 2014). There is a noted limited ability of ultrasound stratification systems to detect follicular carcinoma (Castellana M, Piccardo A, Virili C et al. Cancer Cytopathol 2020).
Prospects
The application of artificial intelligence is being studied to revise ACR TI-RADS risk stratification with an assessment of diagnostic accuracy and clinical benefit (Wildman-Tobriner B, Buda M, Hoang JK et al. Radiology 2019; 292(1): 112-119; Sorrenti S, Dolcetti V, Radzina M et al. Cancers 2022).
Frequently asked questions
What is the primary source for ACR TI-RADS 2017?
Tessler FN, Middleton WD, Grant EG et al. ACR thyroid imaging reporting and data system (TI-RADS): white paper of the ACR TI-RADS Committee. J Am Coll Radiol 2017; 14:587-593.
Are there specific scores and FNA thresholds in these materials?
No. The provided excerpts do not include numerical scores for features, category ranges, or nodule size thresholds for FNA [clarify]; they should be obtained from the primary ACR 2017 source.
What alternative stratification systems are mentioned?
EU-TIRADS of the European Thyroid Association (Russ G et al., Eur Thyroid J 2017).
Are there limitations to TI-RADS systems?
Yes, there is a noted limited ability of ultrasound-based stratification systems to detect follicular carcinoma (Castellana M et al., Cancer Cytopathol 2020).
Is AI applied in ACR TI-RADS?
Yes, the use of artificial intelligence is being studied to revise risk stratification with an assessment of accuracy and benefit (Wildman-Tobriner B et al., Radiology 2019; Sorrenti S et al., Cancers 2022).