Rationale and Objectives
Determine the objective benefits of structured reporting of brain tumors through Brain
tumor-RADS (BT-RADS) by analyzing discrete quantifiable metrics of the reports themselves.
Materials and Methods
Following Institutional Review Board approval, post-treatment glioma reports were
acquired from two matched 3-month time periods for pre- and postimplementation of
BT-RADS. The reports were analyzed for presence of history words, such as “Avastin”
and “methylguanine-DNA methyltransferase,” as well as hedge words, such as “Possibly”
and “Likely.” The word counts of the total report and of the impression section were
also assessed, as well as whether or not the report contained addenda.
Results
In total, 211 pre-BT-RADS and 172 post-BT-RADS reports were analyzed. Post-BT-RADS
reports demonstrated greater reporting of history words, including “Avastin” (7.6%
vs. 20.9%, p < 0.001) and “methylguanine-DNA methyltransferase” (10.9% vs. 31.4%, p < 0.0001). They also demonstrated reduced usage of hedge words, including “Possibly”
(3.8% vs. 0.6%, p < 0.05) and “Likely” (49.8% vs. 28.5%, p < 0.01). Furthermore, post-BT-RADS reports possessed fewer words in total report
length (389 vs. 245.2, p < 0.001), as well as in the impression section (53.7 vs. 42.6, p < 0.01). Finally, fewer post-BT-RADS reports contained addenda (10% vs. 1.2%, p < 0.01).
Conclusion
Following implementation of BT-RADS, glioma reports demonstrated greater consistency
and completeness of clinical history, less ambiguity, and more conciseness.
Key Words
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Academic RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
REFERENCES
- Implementation of a novel surveillance template for head and neck cancer: Neck Imaging Reporting and Data System (NI-RADS).J Am Coll Radiol. 2016; 13 (e1): 743-746
- LI-RADS: a case-based review of the new categorization of liver findings in patients with end-stage liver disease.Radiographics. 2012; 32: 1977-1995
- PI-RADS classification: structured reporting for MRI of the prostate.Rofo. 2013; 185: 253-261
- Coronary artery disease-reporting and data system (CAD-RADS): an expert consensus document of SCCT, ACR and NASCI: endorsed by the ACC.JACC Cardiovasc Imaging. 2016; 9: 1099-1113
- Management-based structured reporting of posttreatment glioma response with the brain tumor reporting and data system.J Am Coll Radiol. 2018; 15: 767-771
- CGCG clinical practice guidelines for the management of adult diffuse gliomas.Cancer letters. 2016; 375: 263-273
- Genetics and immunotherapy: using the genetic landscape of gliomas to inform management strategies.J Neuro-oncology. 2015; 123: 373-383
- Response assessment in neuro-oncology criteria and clinical endpoints.Magn Reson Imaging Clin. 2016; 24: 705-718
- Reporting results of cancer treatment.Cancer. 1981; 47: 207-214
- Institutional implementation of a structured reporting system: our experience with the brain tumor reporting and data system.Acad Radiol. 2019; 26: 974-980
- Communication errors in radiology: a liability cost analysis.J Am Coll Radiol. 2005; 2: 428-431
- Improving communication of diagnostic radiology findings through structured reporting.Radiology. 2011; 260: 174-181
- Imaging 3.0: what is it?.J Am Coll Radiol. 2013; 10: 229
- Structured reporting: patient care enhancement or productivity nightmare?.Radiology. 2008; 249: 739-747
- Improving consistency in radiology reporting through the use of department-wide standardized structured reporting.Radiology. 2013; 267: 240-250
Wildman-Tobriner B, Buda M, Hoang JK, et al. Using artificial intelligence to revise ACR TI-RADS risk stratification of thyroid nodules: diagnostic accuracy and utility. Radiology 2019:182128.
- Natural language processing in radiology: a systematic review.Radiology. 2016; 279: 329-343
Article info
Publication history
Published online: August 27, 2019
Accepted:
July 29,
2019
Received in revised form:
July 28,
2019
Received:
July 4,
2019
Footnotes
Abstract previously presented at: 57th Annual Meeting and Symposium of the American Society of Neuroradiology, May 18–23, 2019; Boston, MA.
Identification
Copyright
© 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
