Skip to main content
Top
Published in: Insights into Imaging 1/2023

Open Access 01-12-2023 | Statement

ESR paper on structured reporting in radiology—update 2023

Author: European Society of Radiology (ESR)

Published in: Insights into Imaging | Issue 1/2023

Login to get access

Abstract

Structured reporting in radiology continues to hold substantial potential to improve the quality of service provided to patients and referring physicians. Despite many physicians’ preference for structured reports and various efforts by radiological societies and some vendors, structured reporting has still not been widely adopted in clinical routine.
While in many countries national radiological societies have launched initiatives to further promote structured reporting, cross-institutional applications of report templates and incentives for usage of structured reporting are lacking. Various legislative measures have been taken in the USA and the European Union to promote interoperable data formats such as Fast Healthcare Interoperability Resources (FHIR) in the context of the EU Health Data Space (EHDS) which will certainly be relevant for the future of structured reporting. Lastly, recent advances in artificial intelligence and large language models may provide innovative and efficient approaches to integrate structured reporting more seamlessly into the radiologists’ workflow.
The ESR will remain committed to advancing structured reporting as a key component towards more value-based radiology. Practical solutions for structured reporting need to be provided by vendors. Policy makers should incentivize the usage of structured radiological reporting, especially in cross-institutional setting.
Critical relevance statement Over the past years, the benefits of structured reporting in radiology have been widely discussed and agreed upon; however, implementation in clinical routine is lacking due—policy makers should incentivize the usage of structured radiological reporting, especially in cross-institutional setting.
Key points
1. Various national societies have established initiatives for structured reporting in radiology.
2. Almost no monetary or structural incentives exist that favor structured reporting.
3. A consensus on technical standards for structured reporting is still missing.
4. The application of large language models may help structuring radiological reports.
5. Policy makers should incentivize the usage of structured radiological reporting.

Graphical Abstract

Literature
1.
go back to reference Hickey P (1922) Standardization of Roentgen-ray reports. Am J Roentgenol 9:422–425 Hickey P (1922) Standardization of Roentgen-ray reports. Am J Roentgenol 9:422–425
2.
go back to reference Bosmans JML, Weyler JJ, De Schepper AM, Parizel PM (2011) The radiology report as seen by radiologists and referring clinicians: results of the COVER and ROVER surveys. Radiology 259(1):184–195CrossRefPubMed Bosmans JML, Weyler JJ, De Schepper AM, Parizel PM (2011) The radiology report as seen by radiologists and referring clinicians: results of the COVER and ROVER surveys. Radiology 259(1):184–195CrossRefPubMed
3.
go back to reference Bosmans JML, Neri E, Ratib O, Kahn CE (2015) Structured reporting: a fusion reactor hungry for fuel. Insights Imaging 6(1):129–132CrossRefPubMed Bosmans JML, Neri E, Ratib O, Kahn CE (2015) Structured reporting: a fusion reactor hungry for fuel. Insights Imaging 6(1):129–132CrossRefPubMed
4.
go back to reference Yoon JS, Boutis K, Pecaric MR, Fefferman NR, Ericsson KA, Pusic MV (2020) A think-aloud study to inform the design of radiograph interpretation practice. Adv in Health Sci Educ 25(4):877–903CrossRef Yoon JS, Boutis K, Pecaric MR, Fefferman NR, Ericsson KA, Pusic MV (2020) A think-aloud study to inform the design of radiograph interpretation practice. Adv in Health Sci Educ 25(4):877–903CrossRef
5.
go back to reference European Society of Radiology (ESR) (2018) ESR paper on structured reporting in radiology. Insights Imaging 9(1):1–7CrossRef European Society of Radiology (ESR) (2018) ESR paper on structured reporting in radiology. Insights Imaging 9(1):1–7CrossRef
6.
go back to reference Nobel JM, van Geel K, Robben SGF (2022) Structured reporting in radiology: a systematic review to explore its potential. Eur Radiol 32(4):2837–2854CrossRefPubMed Nobel JM, van Geel K, Robben SGF (2022) Structured reporting in radiology: a systematic review to explore its potential. Eur Radiol 32(4):2837–2854CrossRefPubMed
13.
go back to reference Persigehl T, Baumhauer M, Baeßler B et al (2020) Structured reporting of solid and cystic pancreatic lesions in CT and MRI: consensus-based structured report templates of the German Society of Radiology (DRG). Rofo 192(07):641–656CrossRefPubMed Persigehl T, Baumhauer M, Baeßler B et al (2020) Structured reporting of solid and cystic pancreatic lesions in CT and MRI: consensus-based structured report templates of the German Society of Radiology (DRG). Rofo 192(07):641–656CrossRefPubMed
14.
go back to reference Brendle C, Bender B, Selo N et al (2021) Structured reporting of acute ischemic stroke – consensus-based reporting templates for non-contrast cranial computed tomography, CT angiography, and CT perfusion. Rofo 193(11):1315–1317CrossRefPubMed Brendle C, Bender B, Selo N et al (2021) Structured reporting of acute ischemic stroke – consensus-based reporting templates for non-contrast cranial computed tomography, CT angiography, and CT perfusion. Rofo 193(11):1315–1317CrossRefPubMed
15.
go back to reference Bunck AC, Baeßler B, Ritter C et al (2019) Structured reporting in cross-sectional imaging of the heart: reporting templates for CMR imaging of cardiomyopathies (myocarditis, dilated cardiomyopathy, hypertrophic cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy and siderosis). Rofo. https://doi.org/10.1055/a-0998-4116 Bunck AC, Baeßler B, Ritter C et al (2019) Structured reporting in cross-sectional imaging of the heart: reporting templates for CMR imaging of cardiomyopathies (myocarditis, dilated cardiomyopathy, hypertrophic cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy and siderosis). Rofo. https://​doi.​org/​10.​1055/​a-0998-4116
17.
go back to reference Jorg T, Halfmann MC, Arnhold G et al (2023) Insights Imaging 14(1):61 Jorg T, Halfmann MC, Arnhold G et al (2023) Insights Imaging 14(1):61
19.
go back to reference Granata V, Faggioni L, Grassi R et al (2022) Structured reporting of computed tomography in the staging of colon cancer: a Delphi consensus proposal. Radiol Med 127(1):21–29CrossRefPubMed Granata V, Faggioni L, Grassi R et al (2022) Structured reporting of computed tomography in the staging of colon cancer: a Delphi consensus proposal. Radiol Med 127(1):21–29CrossRefPubMed
20.
go back to reference Granata V, Morana G, D’Onofrio M et al (2021) Structured reporting of computed tomography and magnetic resonance in the staging of pancreatic adenocarcinoma: a Delphi consensus proposal. Diagnostics (Basel) 11(11):2033CrossRefPubMed Granata V, Morana G, D’Onofrio M et al (2021) Structured reporting of computed tomography and magnetic resonance in the staging of pancreatic adenocarcinoma: a Delphi consensus proposal. Diagnostics (Basel) 11(11):2033CrossRefPubMed
21.
go back to reference Granata V, Pradella S, Cozzi D et al (2021) Computed tomography structured reporting in the staging of lymphoma: a Delphi consensus proposal. J Clin Med 10(17):4007CrossRefPubMedPubMedCentral Granata V, Pradella S, Cozzi D et al (2021) Computed tomography structured reporting in the staging of lymphoma: a Delphi consensus proposal. J Clin Med 10(17):4007CrossRefPubMedPubMedCentral
22.
go back to reference Granata V, Coppola F, Grassi R et al (2021) Structured reporting of computed tomography in the staging of neuroendocrine neoplasms: a Delphi consensus proposal. Front Endocrinol (Lausanne) 12:748944CrossRefPubMed Granata V, Coppola F, Grassi R et al (2021) Structured reporting of computed tomography in the staging of neuroendocrine neoplasms: a Delphi consensus proposal. Front Endocrinol (Lausanne) 12:748944CrossRefPubMed
23.
go back to reference Neri E, Granata V, Montemezzi S et al (2022) Structured reporting of x-ray mammography in the first diagnosis of breast cancer: a Delphi consensus proposal. Radiol Med 127(5):471–483CrossRefPubMedPubMedCentral Neri E, Granata V, Montemezzi S et al (2022) Structured reporting of x-ray mammography in the first diagnosis of breast cancer: a Delphi consensus proposal. Radiol Med 127(5):471–483CrossRefPubMedPubMedCentral
24.
go back to reference Granata V, Grassi R, Miele V et al (2021) Structured reporting of lung cancer staging: a consensus proposal. Diagnostics (Basel) 11(9):1569CrossRefPubMed Granata V, Grassi R, Miele V et al (2021) Structured reporting of lung cancer staging: a consensus proposal. Diagnostics (Basel) 11(9):1569CrossRefPubMed
25.
go back to reference Granata V, Caruso D, Grassi R et al (2021) Structured reporting of rectal cancer staging and restaging: a consensus proposal. Cancers (Basel) 13(9):2135CrossRefPubMedPubMedCentral Granata V, Caruso D, Grassi R et al (2021) Structured reporting of rectal cancer staging and restaging: a consensus proposal. Cancers (Basel) 13(9):2135CrossRefPubMedPubMedCentral
26.
29.
go back to reference Alvfeldt G, Aspelin P, Blomqvist L, Sellberg N (2021) Radiology reporting in rectal cancer using MRI: adherence to national template for structured reporting. Acta Radiol 6:028418512110572 Alvfeldt G, Aspelin P, Blomqvist L, Sellberg N (2021) Radiology reporting in rectal cancer using MRI: adherence to national template for structured reporting. Acta Radiol 6:028418512110572
31.
go back to reference Morgan TA, Helibrun ME, Kahn CE (2014) Reporting Initiative of the Radiological Society of North America: progress and new directions. Radiology 273(3):642–645CrossRefPubMed Morgan TA, Helibrun ME, Kahn CE (2014) Reporting Initiative of the Radiological Society of North America: progress and new directions. Radiology 273(3):642–645CrossRefPubMed
32.
go back to reference Powell DK, Silberzweig JE (2015) State of structured reporting in radiology, a survey. Acad Radiol 22(2):226–233CrossRefPubMed Powell DK, Silberzweig JE (2015) State of structured reporting in radiology, a survey. Acad Radiol 22(2):226–233CrossRefPubMed
35.
go back to reference Harris D, Yousem DM, Krupinski EA, Motaghi M (2023) Eye-tracking differences between free text and template radiology reports: a pilot study. JMI 10(S1):S11902PubMedPubMedCentral Harris D, Yousem DM, Krupinski EA, Motaghi M (2023) Eye-tracking differences between free text and template radiology reports: a pilot study. JMI 10(S1):S11902PubMedPubMedCentral
37.
go back to reference Pinto dos Santos D, Scheibl S, Arnhold G et al (2018) A proof of concept for epidemiological research using structured reporting with pulmonary embolism as a use case. Br J Radiol 91(1088):20170564 Pinto dos Santos D, Scheibl S, Arnhold G et al (2018) A proof of concept for epidemiological research using structured reporting with pulmonary embolism as a use case. Br J Radiol 91(1088):20170564
38.
go back to reference Blagev DP, Lloyd JF, Conner K et al (2016) Follow-up of incidental pulmonary nodules and the radiology report. J Am Coll Radiol 13(2):R18-24CrossRefPubMed Blagev DP, Lloyd JF, Conner K et al (2016) Follow-up of incidental pulmonary nodules and the radiology report. J Am Coll Radiol 13(2):R18-24CrossRefPubMed
39.
go back to reference Vosshenrich J, Nesic I, Cyriac J, Boll DT, Merkle EM, Heye T (2020) Revealing the most common reporting errors through data mining of the report proofreading process. Eur Radiol 1(4):2115–2125 Vosshenrich J, Nesic I, Cyriac J, Boll DT, Merkle EM, Heye T (2020) Revealing the most common reporting errors through data mining of the report proofreading process. Eur Radiol 1(4):2115–2125
40.
go back to reference Kabadi SJ, Krishnaraj A (2017) Strategies for improving the value of the radiology report: a retrospective analysis of errors in formally over-read studies. J Am Coll Radiol 14(4):459–466CrossRefPubMed Kabadi SJ, Krishnaraj A (2017) Strategies for improving the value of the radiology report: a retrospective analysis of errors in formally over-read studies. J Am Coll Radiol 14(4):459–466CrossRefPubMed
42.
go back to reference Lyu Q, Tan J, Zapadka ME et al (2023) Translating radiology reports into plain language using ChatGPT and GPT-4 with prompt learning: promising results, limitations, and potential [Internet]. arXiv. Available from: http://arxiv.org/abs/2303.09038. Cited 2023 Apr 11 Lyu Q, Tan J, Zapadka ME et al (2023) Translating radiology reports into plain language using ChatGPT and GPT-4 with prompt learning: promising results, limitations, and potential [Internet]. arXiv. Available from: http://​arxiv.​org/​abs/​2303.​09038. Cited 2023 Apr 11
44.
go back to reference Pinto dos Santos D, Brodehl S, Baeßler B et al (2019) Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs. Insights Imaging 10(1):93CrossRefPubMedPubMedCentral Pinto dos Santos D, Brodehl S, Baeßler B et al (2019) Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs. Insights Imaging 10(1):93CrossRefPubMedPubMedCentral
45.
go back to reference IHE Radiology Technical Committee (2022) IHE Radiology Technical Framework Supplement - AI Results (AIR) IHE Radiology Technical Committee (2022) IHE Radiology Technical Framework Supplement - AI Results (AIR)
46.
go back to reference IHE Radiology Technical Committee (2022) IHE Radiology Technical Framework Supplement - AI Workflow for Imaging (AIW-I) IHE Radiology Technical Committee (2022) IHE Radiology Technical Framework Supplement - AI Workflow for Imaging (AIW-I)
48.
go back to reference Chen PH, Zafar H, Galperin-Aizenberg M, Cook T (2018) Integrating natural language processing and machine learning algorithms to categorize oncologic response in radiology reports. J Digit Imaging 31(2):178–184CrossRefPubMed Chen PH, Zafar H, Galperin-Aizenberg M, Cook T (2018) Integrating natural language processing and machine learning algorithms to categorize oncologic response in radiology reports. J Digit Imaging 31(2):178–184CrossRefPubMed
49.
go back to reference Steinkamp JM, Chambers C, Lalevic D, Zafar HM, Cook TS (2019) Toward complete structured information extraction from radiology reports using machine learning. J Digit Imaging 32(4):554–564CrossRefPubMedPubMedCentral Steinkamp JM, Chambers C, Lalevic D, Zafar HM, Cook TS (2019) Toward complete structured information extraction from radiology reports using machine learning. J Digit Imaging 32(4):554–564CrossRefPubMedPubMedCentral
50.
go back to reference Banerjee I, Chen MC, Lungren MP, Rubin DL (2018) Radiology report annotation using intelligent word embeddings: applied to multi-institutional chest CT cohort. J Biomed Inform 77:11–20CrossRefPubMed Banerjee I, Chen MC, Lungren MP, Rubin DL (2018) Radiology report annotation using intelligent word embeddings: applied to multi-institutional chest CT cohort. J Biomed Inform 77:11–20CrossRefPubMed
51.
go back to reference Castro SM, Tseytlin E, Medvedeva O et al (2017) Automated annotation and classification of BI-RADS assessment from radiology reports. J Biomed Inform 1(69):177–187CrossRef Castro SM, Tseytlin E, Medvedeva O et al (2017) Automated annotation and classification of BI-RADS assessment from radiology reports. J Biomed Inform 1(69):177–187CrossRef
52.
go back to reference Tahmasebi AM, Zhu H, Mankovich G et al (2019) Automatic normalization of anatomical phrases in radiology reports using unsupervised learning. J Digit Imaging 32(1):6–18CrossRefPubMed Tahmasebi AM, Zhu H, Mankovich G et al (2019) Automatic normalization of anatomical phrases in radiology reports using unsupervised learning. J Digit Imaging 32(1):6–18CrossRefPubMed
53.
go back to reference Chen TL, Emerling M, Chaudhari GR et al (2021) Domain specific word embeddings for natural language processing in radiology. J Biomed Inform 1(113):103665CrossRef Chen TL, Emerling M, Chaudhari GR et al (2021) Domain specific word embeddings for natural language processing in radiology. J Biomed Inform 1(113):103665CrossRef
55.
go back to reference Adams LC, Truhn D, Busch F et al (2023) Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study. Radiology 4:230725CrossRef Adams LC, Truhn D, Busch F et al (2023) Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study. Radiology 4:230725CrossRef
56.
go back to reference Jorg T, Kämpgen B, Feiler D et al (2023) Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing. Insights Imaging 14(1):47CrossRefPubMedPubMedCentral Jorg T, Kämpgen B, Feiler D et al (2023) Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing. Insights Imaging 14(1):47CrossRefPubMedPubMedCentral
57.
go back to reference European Society of Radiology (ESR) (2021) Value-based radiology: what is the ESR doing, and what should we do in the future? Insights Imaging 12(1):108CrossRef European Society of Radiology (ESR) (2021) Value-based radiology: what is the ESR doing, and what should we do in the future? Insights Imaging 12(1):108CrossRef
58.
go back to reference Brady AP, Bello JA, Derchi LE et al (2020) Radiology in the era of value-based healthcare: a multi-society expert statement from the ACR, CAR, ESR, IS3R, RANZCR, and RSNA. Insights Imaging 11(1):136CrossRefPubMedPubMedCentral Brady AP, Bello JA, Derchi LE et al (2020) Radiology in the era of value-based healthcare: a multi-society expert statement from the ACR, CAR, ESR, IS3R, RANZCR, and RSNA. Insights Imaging 11(1):136CrossRefPubMedPubMedCentral
Metadata
Title
ESR paper on structured reporting in radiology—update 2023
Author
European Society of Radiology (ESR)
Publication date
01-12-2023
Publisher
Springer Vienna
Published in
Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI
https://doi.org/10.1186/s13244-023-01560-0

Other articles of this Issue 1/2023

Insights into Imaging 1/2023 Go to the issue