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Lexicon for renal mass terms at CT and MRI: a consensus of the society of abdominal radiology disease-focused panel on renal cell carcinoma

  • Kidneys, Ureters, Bladder, Retroperitoneum
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Abstract

Purpose

There is substantial variation in the radiologic terms used to characterize renal masses, leading to ambiguity and inconsistency in clinical radiology reports and research studies. The purpose of this study was to develop a standardized lexicon to describe renal masses at CT and MRI.

Materials and methods

This multi-institutional, prospective, quality improvement project was exempt from IRB oversight. Thirteen radiologists belonging to the Society of Abdominal Radiology (SAR) disease-focused panel on renal cell carcinoma representing nine academic institutions participated in a modified Delphi process to create a lexicon of terms used to describe imaging features of renal masses at CT and MRI. In the first round, members voted on terms to be included and proposed definitions; subsequent voting rounds and a teleconference established consensus. One non-voting member developed the questionnaire and consolidated responses. Consensus was defined as ≥ 80% agreement.

Results

Of 37 proposed terms, 6 had consensus to be excluded. Consensus for inclusion was reached for 30 of 31 terms (13/14 basic imaging terms, 8/8 CT terms, 6/6 MRI terms and 3/3 miscellaneous terms). Despite substantial initial disagreement about definitions of ‘renal mass,’ ‘necrosis,’ ‘fat,’ and ‘restricted diffusion’ in the first round, consensus for all was eventually reached. Disagreement remained for the definition of ‘solid mass.’

Conclusions

A modified Delphi method produced a lexicon of preferred terms and definitions to be used in the description of renal masses at CT and MRI. This lexicon should improve clarity and consistency of radiology reports and research related to renal masses.

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References

  1. Baron RL. The radiologist as interpreter and translator. Radiology. 2014 Jul;272(1):4–8.

    Article  Google Scholar 

  2. Breast Imaging Reporting & Data System [Internet]. [cited 2018 Jul 2]. Available from: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Bi-Rads

  3. Liberman L, Menell JH. Breast imaging reporting and data system (BI-RADS). Radiol Clin North Am. 2002 May;40(3):409–30, v.

  4. Langlotz CP. RadLex: a new method for indexing online educational materials. Radiogr Rev Publ Radiol Soc N Am Inc. 2006 Dec;26(6):1595–7.

  5. Bertaud V, Lasbleiz J, Mougin F, Burgun A, Duvauferrier R. A unified representation of findings in clinical radiology using the UMLS and DICOM. Int J Med Inf. 2008 Sep;77(9):621–9.

    Article  Google Scholar 

  6. Filice RW, Kahn CE. Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT. J Digit Imaging. 2019 Jan 31;

  7. American College of Radiology (ACR) Illustrated Breast Imaging Reporting and Data System (BIRADS TM) American College of Radiology (ACR). Reston, VA (1998).

  8. LI-RADS [Internet]. [cited 2018 Jul 2]. Available from: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/LI-RADS

  9. Lung Rads [Internet]. [cited 2018 Jul 2]. Available from: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Lung-Rads

  10. PI-RADS [Internet]. [cited 2018 Jul 2]. Available from: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/PI-RADS

  11. Boland GW, Duszak R. Structured Reporting and Communication. J Am Coll Radiol JACR. 2015 Dec;12(12 Pt A):1286–8.

  12. Schwartz LH, Panicek DM, Berk AR, Li Y, Hricak H. Improving communication of diagnostic radiology findings through structured reporting. Radiology. 2011 Jul;260(1):174–81.

    Article  Google Scholar 

  13. Kahn CE, Heilbrun ME, Applegate KE. From guidelines to practice: how reporting templates promote the use of radiology practice guidelines. J Am Coll Radiol JACR. 2013 Apr;10(4):268–73.

    Article  Google Scholar 

  14. Herts BR, Gandhi NS, Schneider E, Coppa CP, Mody RN, Baker ME, et al. How We Do It: Creating Consistent Structure and Content in Abdominal Radiology Report Templates. AJR Am J Roentgenol. 2019 Jan 8;1–7.

    Google Scholar 

  15. Brook OR, Brook A, Vollmer CM, Kent TS, Sanchez N, Pedrosa I. Structured reporting of multiphasic CT for pancreatic cancer: potential effect on staging and surgical planning. Radiology. 2015 Feb;274(2):464–72.

    Article  Google Scholar 

  16. Khorasani R, Bates DW, Teeger S, Rothschild JM, Adams DF, Seltzer SE. Is terminology used effectively to convey diagnostic certainty in radiology reports? Acad Radiol. 2003 Jun;10(6):685–8.

    Article  Google Scholar 

  17. Mityul MI, Gilcrease-Garcia B, Searleman A, Demertzis JL, Gunn AJ. Interpretive Differences Between Patients and Radiologists Regarding the Diagnostic Confidence Associated With Commonly Used Phrases in the Radiology Report. AJR Am J Roentgenol. 2017 Oct 12;1–4.

    Google Scholar 

  18. Bosmans JML, Weyler JJ, De Schepper AM, Parizel PM. The radiology report as seen by radiologists and referring clinicians: results of the COVER and ROVER surveys. Radiology. 2011 Apr;259(1):184–95.

    Article  Google Scholar 

  19. Gunn AJ, Tuttle MC, Flores EJ, Mangano MD, Bennett SE, Sahani DV, et al. Differing Interpretations of Report Terminology Between Primary Care Physicians and Radiologists. J Am Coll Radiol JACR. 2016 Dec;13(12 Pt A):1525-1529.e1.

  20. Rosenkrantz AB, Kiritsy M, Kim S. How “consistent” is “consistent”? A clinician-based assessment of the reliability of expressions used by radiologists to communicate diagnostic confidence. Clin Radiol. 2014 Jul;69(7):745–9.

    Article  CAS  Google Scholar 

  21. Shinagare AB, Lacson R, Boland GW, Wang A, Silverman SG, Mayo-Smith WW, et al. Radiologist Preferences, Agreement, and Variability in Phrases Used to Convey Diagnostic Certainty in Radiology Reports. J Am Coll Radiol JACR. 2018 Dec 22;

  22. Egbert ND, Caoili EM, Cohan RH, Davenport MS, Francis IR, Kunju LP, et al. Differentiation of papillary renal cell carcinoma subtypes on CT and MRI. AJR Am J Roentgenol. 2013 Aug;201(2):347–55.

    Article  Google Scholar 

  23. Karlo CA, Di Paolo PL, Chaim J, Hakimi AA, Ostrovnaya I, Russo P, et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology. 2014 Feb;270(2):464–71.

    Article  Google Scholar 

  24. Shinagare AB, Vikram R, Jaffe C, Akin O, Kirby J, Huang E, et al. Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group. Abdom Imaging. 2015 Aug;40(6):1684–92.

    Article  Google Scholar 

  25. Jamshidi N, Jonasch E, Zapala M, Korn RL, Aganovic L, Zhao H, et al. The Radiogenomic Risk Score: Construction of a Prognostic Quantitative, Noninvasive Image-based Molecular Assay for Renal Cell Carcinoma. Radiology. 2015 Oct;277(1):114–23.

    Article  Google Scholar 

  26. Jinzaki M, Silverman SG, Akita H, Nagashima Y, Mikami S, Oya M. Renal angiomyolipoma: a radiological classification and update on recent developments in diagnosis and management. Abdom Imaging. 2014 Jun;39(3):588–604.

    Article  Google Scholar 

  27. Hindman N, Ngo L, Genega EM, Melamed J, Wei J, Braza JM, et al. Angiomyolipoma with minimal fat: can it be differentiated from clear cell renal cell carcinoma by using standard MR techniques? Radiology. 2012 Nov;265(2):468–77.

    Article  Google Scholar 

  28. Lim RS, Flood TA, McInnes MDF, Lavallee LT, Schieda N. Renal angiomyolipoma without visible fat: Can we make the diagnosis using CT and MRI? Eur Radiol. 2018 Feb;28(2):542–53.

    Article  Google Scholar 

  29. Pokharel SS, Macura KJ, Kamel IR, Zaheer A. Current MR imaging lipid detection techniques for diagnosis of lesions in the abdomen and pelvis. Radiogr Rev Publ Radiol Soc N Am Inc. 2013 May;33(3):681–702.

  30. Silverman SG, Pedrosa I, Ellis JH, Hindman NM, Schieda N, Smith AD, et al. Bosniak Classification of Cystic Renal Masses, Version 2019: An Update Proposal and Needs Assessment. Radiology. 2019;292(2):475–88.

    Article  Google Scholar 

  31. Hasson F, Keeney S, McKenna H. Research guidelines for the Delphi survey technique. J Adv Nurs. 2000 Oct;32(4):1008–15.

    CAS  PubMed  Google Scholar 

  32. Powell C. The Delphi technique: myths and realities. J Adv Nurs. 2003 Feb;41(4):376–82.

    Article  Google Scholar 

  33. Boulkedid R, Abdoul H, Loustau M, Sibony O, Alberti C. Using and reporting the Delphi method for selecting healthcare quality indicators: a systematic review. PloS One. 2011;6(6):e20476.

    Article  CAS  Google Scholar 

  34. Humphrey-Murto S, Varpio L, Wood TJ, Gonsalves C, Ufholz L-A, Mascioli K, et al. The Use of the Delphi and Other Consensus Group Methods in Medical Education Research: A Review. Acad Med J Assoc Am Med Coll. 2017;92(10):1491–8.

    Article  Google Scholar 

  35. Sun BC, Thiruganasambandamoorthy V, Cruz JD, Consortium to Standardize ED Syncope Risk Stratification Reporting. Standardized reporting guidelines for emergency department syncope risk-stratification research. Acad Emerg Med Off J Soc Acad Emerg Med. 2012 Jun;19(6):694–702.

  36. Berk L, Jorm AF, Kelly CM, Dodd S, Berk M. Development of guidelines for caregivers of people with bipolar disorder: a Delphi expert consensus study. Bipolar Disord. 2011 Sep;13(5–6):556–70.

    Article  Google Scholar 

  37. Eubank BH, Mohtadi NG, Lafave MR, Wiley JP, Bois AJ, Boorman RS, et al. Using the modified Delphi method to establish clinical consensus for the diagnosis and treatment of patients with rotator cuff pathology. BMC Med Res Methodol. 2016 20;16:56.

    Article  Google Scholar 

  38. Nair R, Aggarwal R, Khanna D. Methods of formal consensus in classification/diagnostic criteria and guideline development. Semin Arthritis Rheum. 2011 Oct;41(2):95–105.

    Article  Google Scholar 

  39. Schieda N, Davenport MS, Pedrosa I, Shinagare A, Chandarana H, Curci N, et al. Renal and adrenal masses containing fat at MRI: Proposed nomenclature by the society of abdominal radiology disease-focused panel on renal cell carcinoma. J Magn Reson Imaging JMRI. 2019 Apr;49(4):917–26.

    Article  Google Scholar 

  40. Al Harbi F, Tabatabaeefar L, Jewett MA, Finelli A, O’Malley M, Atri M. Enhancement Threshold of Small (< 4 cm) Solid Renal Masses on CT. AJR Am J Roentgenol. 2016 Mar;206(3):554–8.

    Article  Google Scholar 

  41. Pallwein-Prettner L, Flöry D, Rotter CR, Pogner K, Syré G, Fellner C, et al. Assessment and characterisation of common renal masses with CT and MRI. Insights Imaging. 2011 Oct;2(5):543–56.

    Article  Google Scholar 

  42. Kay FU, Canvasser NE, Xi Y, Pinho DF, Costa DN, Diaz de Leon A, et al. Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology. Radiology. 2018;287(2):543–53.

  43. Kutikov A, Uzzo RG. The R.E.N.A.L. nephrometry score: a comprehensive standardized system for quantitating renal tumor size, location and depth. J Urol. 2009 Sep;182(3):844–53.

  44. Davenport MS, Hu EM, Smith AD, Chandarana H, Hafez K, Palapattu GS, et al. Reporting standards for the imaging-based diagnosis of renal masses on CT and MRI: a national survey of academic abdominal radiologists and urologists. Abdom Radiol N Y. 2017;42(4):1229–40.

    Article  Google Scholar 

  45. Hu EM, Zhang A, Silverman SG, Pedrosa I, Wang ZJ, Smith AD, et al. Multi-institutional analysis of CT and MRI reports evaluating indeterminate renal masses: comparison to a national survey investigating desired report elements. Abdom Radiol N Y. 2018 Dec;43(12):3493–502.

    Article  Google Scholar 

  46. Davenport MS, Hu EM, Zhang A, Shinagare AB, Smith AD, Pedrosa I, et al. Standardized report template for indeterminate renal masses at CT and MRI: a collaborative product of the SAR Disease-Focused Panel on Renal Cell Carcinoma. Abdom Radiol N Y. 2018 Dec 3;

  47. RadLex [Internet]. [cited 2017 Mar 9]. Available from: https://www.rsna.org/RadLex.aspx

  48. Ozier W. The Delphi/Modified Delphi Technique: A Consensus Approach to Information Valuation. :16.

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Acknowledgements

We thank the Society of Abdominal Radiology Board of Directors for officially endorsing this lexicon.

Author information

Authors and Affiliations

  1. Department of Radiology, Brigham and Women’s Hospital/Harvard Medical School, 75 Francis Street, Boston, MA, 02215, USA

    Atul B. Shinagare, Hyesun Park & Stuart G. Silverman

  2. Dana-Farber Cancer Institute, Boston, USA

    Atul B. Shinagare & Hyesun Park

  3. Society of Abdominal Radiology Disease-Focused Panel on Renal Cell Carcinoma, Houston, TX, USA

    Atul B. Shinagare, Matthew S. Davenport, Ivan Pedrosa, Erick M. Remer, Hersh Chandarana, Ankur M. Doshi, Nicola Schieda, Andrew D. Smith, Raghunandan Vikram, Zhen J. Wang & Stuart G. Silverman

  4. Departments of Radiology & Urology, Michigan Medicine, Ann Arbor, MI, USA

    Matthew S. Davenport

  5. University of Texas Southwestern, Dallas, TX, USA

    Ivan Pedrosa

  6. Cleveland Clinic, Cleveland, OH, USA

    Erick M. Remer

  7. NYU Langone Health, New York, NY, USA

    Hersh Chandarana & Ankur M. Doshi

  8. University of Ottawa, Ottawa, ON, Canada

    Nicola Schieda

  9. University of Alabama at Birmingham, Birmingham, AL, USA

    Andrew D. Smith

  10. The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA

    Raghunandan Vikram

  11. UCSF, San Francisco, CA, USA

    Zhen J. Wang

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  1. Atul B. Shinagare
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Corresponding author

Correspondence to Atul B. Shinagare.

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Conflict of interest

Relevant to this work: None. Not relevant: Atul Shinagare: Consultant, Arog Pharmaceuticals, Virtualscopics. Matthew Davenport: Royalties from Wolters Kluwer. Hyesun Park: No disclosures listed. Hersh Chandarana: Research support in form of hardware and software from Siemens. Healthcare, Patent and provisional patents: MR technique GRASP and automated assessment of Image Quality with Deep Learning. Ankur M. Doshi: No disclosures listed. Ivan Pedrosa: Honorarium for a Bayer Scientific Advisory Board, co-inventor of patents with Philips Healthcare. Erick M. Remer: No disclosures listed. Nicola Schieda: No disclosures listed. Andrew Smith: President of eMASS LLC, patents pending, and President of Radiostics LLC. Raghunandan Vikram: No disclosures listed. Zhen J. Wang: Consultant, GE Healthcare; Shareholder, Nextrast, Inc. Stuart Silverman: Grant support NIH 1R21CA216796-01A1.

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Appendix 1: Detailed study design

Appendix 1: Detailed study design

Study design: modified Delphi method

This multi-institutional, prospective, quality improvement project was exempt from IRB oversight. A modified Delphi method was used [31,32,33,34]. The Delphi technique is a structured process that relies on expert opinion and uses a series of questionnaires or ‘rounds’ to gather the required information from a selected group of experts (panelists), in this case the SAR RCC DFP. Delphi technique involves iterative, sequential, one-on-one panelist interviews. Modified Delphi technique (used in this study) involves reaching consensus by simultaneously collecting information from all panelists [48]. One radiologist member of the panel (AS, name blinded for review) served as the ‘coordinator’ who compiled the initial list of terms, prepared the questionnaires, and collected and analyzed the data. A radiology clinical fellow (HP, name blinded for review) helped create the questionnaires used in each round. To avoid bias, the coordinator and clinical fellow did not participate in voting.

The remaining twelve radiologist members of the RCC DFP were invited to participate in the creation of the lexicon. All members are fellowship-trained abdominal radiologists with mean 13 years of experience as attending radiologist (range 5–30 years). Figure 1 outlines the process that was followed. Three rounds of questionnaires and one teleconference (after the second round) were conducted. Following the third round of questionnaires, a manuscript was created. Blinded edits to the manuscript were made and final consensus was reached. Based on prior literature, ≥ 80% agreement at the end of three rounds was considered sufficient ‘consensus’ regarding the inclusion or exclusion of each term and its definition [35,36,37,38]. Individual panelist responses remained anonymous in all three rounds.

Initial selection of terms

The coordinator created an initial list of renal mass imaging features based on prior clinical and research experience and a literature search. A PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) search through recent literature over a period of 2 years was performed from January 2016 to December 2017 using the following search string: ‘(renal or kidney) and (imaging or computed or CT or magnetic or MRI) and (features or findings).’ This yielded 60 publications. The coordinator screened the full text of these publications for imaging terms used to describe renal masses. This literature search was performed mainly to ensure that no commonly used imaging terms were missed; the actual selection of imaging terms occurred in Rounds 1 and 2 of the Delphi process during which the panelists voted on inclusion of each term and also suggested additional imaging terms if needed. The selected imaging terms were categorized into four categories: ‘basic imaging terms,’ ‘CT terms,’ ‘MRI terms’ and ‘miscellaneous terms.’

Round 1 questionnaire

The Round 1 questionnaire containing the list of the selected terms was administered using REDCap (https://redcap.partners.org/redcap/index.php), a secure web application for building and managing online questionnaires. Each panelist had a unique link to access their questionnaire. Each panelist was emailed up to 4 automated reminders, 1 week apart, to complete the questionnaire. All responses were submitted anonymously while blinded to the responses of the other participants.

For each term, the panelists were asked if the term should be included (Options: ‘include’ or ‘exclude’). If they selected ‘include,’ they were asked to suggest a definition for that term in the form of free text without a word limit. During Round 1, panelists also were asked to suggest additional terms to include in the lexicon.

Round 2 questionnaire

The responses from Round 1 were analyzed by the coordinator using simple descriptive statistics. The percentage of responses for inclusion or exclusion of each term were summarized. The proposed definitions of each term were compiled to create either a single unified definition, or 2–4 alternative definitions if the content of the proposed definitions varied substantially and the coordinator was unable to coalesce them into a single definition. The summary statistics regarding inclusion and exclusion and proposed summary definition(s) were incorporated into the Round 2 questionnaire. Any rationale provided by the panelists in Round 1 to support their conflicting views was included for consideration by the other members in Round 2. In Round 2, the panelists were asked again to vote to ‘include’ or ‘exclude’ each term. For each term, if a single unified definition was suggested, the panelists were asked if they agreed with the proposed definition (Options: ‘agree’ or ‘disagree’). If they disagreed, they were required to provide an alternative definition. When more than one definition was provided, they were asked to select one of the options or to provide a new definition.

Two new terms were proposed to be added during Round 1 (‘magnetic susceptibility’ and ‘growth rate’). These were included in Round 2. Panelists were asked if these terms should be included and, if so, they were asked to suggest a definition, similar to Round 1.

The same anonymous blinded method was used to administer the Round 2 questionnaire.

Teleconference

A teleconference was conducted after the completion of Round 2 data analysis to address issues that prevented reaching consensus for terms with persistent disagreement. A summary of the discussion at the teleconference was provided to all the panelists as part of the Round 3 questionnaire.

Round 3 questionnaire

Data extracted from the Round 2 questionnaire were analyzed by the coordinator in the same fashion as the data from Round 1. A consensus definition was provided for each term. Whenever new definitions were provided, an attempt was made to reconcile these with the original proposed definition to create either a single proposed definition or a set of alternatives from which to select.

If there was 100% consensus regarding inclusion or exclusion of a particular term or definition of a term, these terms and definitions were considered ‘finalized.’ This information was provided in the Round 3 questionnaire with no further questions regarding these terms. Terms and definitions that had not met 100% consensus were included in the Round 3 questionnaire. The Round 3 questionnaire was administered to all panelists from Round 2 in the same blinded and anonymous manner as the first two questionnaire rounds. The results were summarized at the end of Round 3.

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Shinagare, A.B., Davenport, M.S., Park, H. et al. Lexicon for renal mass terms at CT and MRI: a consensus of the society of abdominal radiology disease-focused panel on renal cell carcinoma. Abdom Radiol 46, 703–722 (2021). https://doi.org/10.1007/s00261-020-02644-x

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