The adoption of LI-RADS: a survey of non-academic radiologists
- PMID: 37233747
- DOI: 10.1007/s00261-023-03951-9
The adoption of LI-RADS: a survey of non-academic radiologists
Abstract
Purpose: To understand the practice and determinants of non-academic radiologists regarding LI-RADS and the four current LI-RADS algorithms: CT/MRI, contrast-enhanced ultrasound (CEUS), ultrasound (US), and CT/MRI Treatment Response.
Materials and methods: Seven themes were covered in this international survey, as follows: (1) demographics of participants and sub-specialty, (2) HCC practice and interpretation, (3) reporting practice, (4) screening and surveillance, (5) HCC imaging diagnosis, (6) treatment response, and (7) CT and MRI technique.
Results: Of the 232 participants, 69.4% were from the United States, 25.0% from Canada, and 5.6% from other countries and 45.9% were abdominal/body imagers. During their radiology training or fellowship, no formal HCC diagnostic system was used by 48.7% and LI-RADS was used by 44.4% of participants. In their current practice, 73.6% used LI-RADS, 24.7% no formal system, 6.5% UNOS-OPTN, and 1.3% AASLD. Barriers to LI-RADS adoption included lack of familiarity (25.1%), not used by referring clinicians (21.6%), perceived complexity (14.5%), and personal preference (5.3%). The US LI-RADS algorithm was used routinely by 9.9% of respondents and CEUS LI-RADS was used by 3.9% of the respondents. The LI-RADS treatment response algorithm was used by 43.5% of the respondents. 60.9% of respondents thought that webinars/workshops on LI-RADS Technical Recommendations would help them implement these recommendations in their practice.
Conclusion: A majority of the non-academic radiologists surveyed use the LI-RADS CT/MR algorithm for HCC diagnosis, while nearly half use the LI-RADS TR algorithm for assessment of treatment response. Less than 10% of the participants routinely use the LI-RADS US and CEUS algorithms.
Keywords: Clinical practice; Diagnosis; Hepatocellular carcinoma (HCC); Imaging; LI-RADS; Standardization.
© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
References
-
- Rao AA, Feneis J, Lalonde C, Ojeda-Fournier H. A Pictorial Review of Changes in the BI-RADS Fifth Edition. Radiographics. 2016;36(3):623-39. https://doi.org/10.1148/rg.2016150178 .. - DOI - PubMed
-
- American College of Radiology. Reporting and Data Systems (RADS) [cited 2023 April 27, 2023]. https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems .
-
- Horvath E, Majlis S, Rossi R, Franco C, Niedmann JP, Castro A, et al. An ultrasonogram reporting system for thyroid nodules stratifying cancer risk for clinical management. J Clin Endocrinol Metab. 2009;94(5):1748-51. https://doi.org/10.1210/jc.2008-1724 .. - DOI - PubMed
-
- Li W, Wang Y, Wen J, Zhang L, Sun Y. Diagnostic Performance of American College of Radiology TI-RADS: A Systematic Review and Meta-Analysis. AJR Am J Roentgenol. 2021;216(1):38-47. https://doi.org/10.2214/AJR.19.22691 . - DOI - PubMed
-
- Pooler BD, Kim DH, Lam VP, Burnside ES, Pickhardt PJ. CT Colonography Reporting and Data System (C-RADS): benchmark values from a clinical screening program. AJR Am J Roentgenol. 2014;202(6):1232-7. https://doi.org/10.2214/AJR.13.11272 . - DOI - PubMed - PMC
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