A Hybrid Reporting Platform for Extended RadLex Coding Combining Structured Reporting Templates and Natural Language Processing


Structured reporting is a favorable and sustainable form of reporting in radiology. Among its advantages are better presentation, clearer nomenclature, and higher quality. By using MRRT-compliant templates, the content of the categorized items (e.g., select fields) can be automatically stored in a database, which allows further research and quality analytics based on established ontologies like RadLex® linked to the items. Additionally, it is relevant to provide free-text input for descriptions of findings and impressions in complex imaging studies or for the information included with the clinical referral. So far, however, this unstructured content cannot be categorized. We developed a solution to analyze and code these free-text parts of the templates in our MRRT-compliant reporting platform, using natural language processing (NLP) with RadLex® terms in addition to the already categorized items. The established hybrid reporting concept is working successfully. The NLP tool provides RadLex® codes with modifiers (affirmed, speculated, negated). Radiologists can confirm or reject codes provided by NLP before finalizing the structured report. Furthermore, users can suggest RadLex® codes from free text that is not correctly coded with NLP or can suggest to change the modifier. Analyzing free-text fields took 1.23 s on average. Hybrid reporting enables coding of free-text information in our MRRT-compliant templates and thus increases the amount of categorized data that can be stored in the database. This enhances the possibilities for further analyses, such as correlating clinical information with radiological findings or storing high-quality structured information for machine-learning approaches.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

    Brady AP: Radiology reporting-from Hemingway to HAL? Insights Imaging 9(2):237–46, 2018

    Article  Google Scholar 

  2. 2.

    Lee B, Whitehead MT: Radiology Reports: What YOU Think You’re Saying and What THEY Think You’re Saying. Curr Probl Diagn Radiol 46(3):186–95, 2017

  3. 3.

    European Society of Radiology: ESR paper on structured reporting in radiology. Insights Imaging 9(1):1–7, 2018

    Article  Google Scholar 

  4. 4.

    Gassenmaier S, Armbruster M, Haasters F, Helfen T, Henzler T, Alibek S, Pförringer D, Sommer WH, Sommer NN: Structured reporting of MRI of the shoulder – improvement of report quality? Eur Radiol 27(10):4110–9, 2017

    Article  Google Scholar 

  5. 5.

    Schoeppe F, Sommer WH, Nörenberg D, Verbeek M, Bogner C, Westphalen CB Dreyling M, Rummeny EJ, Fingerle AA: Structured reporting adds clinical value in primary CT staging of diffuse large B-cell lymphoma. Eur Radiol 28(9):3702–9, 2018

    Article  Google Scholar 

  6. 6.

    Wetterauer C, Winkel DJ, Federer-Gsponer JR, Halla A, Subotic S, Deckart A, Seifert HH, Boll DT, Ebbing J: Structured reporting of prostate magnetic resonance imaging has the potential to improve interdisciplinary communication. PLoS One 14(2):e0212444, 2019

    CAS  Article  Google Scholar 

  7. 7.

    Brook OR, Brook A, Vollmer CM, Kent TS, Sacnhez N, Pedrosa I: Structured Reporting of Multiphasic CT for Pancreatic Cancer: Potential Effect on Staging and Surgical Planning. Radiology 274:464–72, 2015

    Article  Google Scholar 

  8. 8.

    Folio LR, Nelson CJ, Benjamin M, Ran A, Engelhard G, Bluemke DA: Quantitative Radiology Reporting in Oncology: Survey of Oncologists and Radiologists. AJR Am J Roentgenol 205(3):W233–43, 2015

    Article  Google Scholar 

  9. 9.

    Sobez LM, Kim SH, Angstwurm M, Stormann S, Pforringer D, Schmidutz F, Prezzi D, Kelly-Morland C, Sommer WH, Sabel B, Nörenberg D: Creating high-quality radiology reports in foreign languages through multilingual structured reporting. Eur Radiol https://doi.org/10.1007/s00330-019-06206-8. 2019

  10. 10.

    Pinto Dos Santos D, Scheibl S, Arnhold G, Maehringer-Kunz A, Düber C, Mildenberger P, Kloeckner R: A proof of concept for epidemiological research using structured reporting with pulmonary embolism as a use case. Br J Radiol doi https://doi.org/10.1259/bjr.20170564. 2018

  11. 11.

    IHE Radiology Technical Committee: Management of Radiology Report Templates (MRRT) Rev.1.7 - Trial Implementation, July 27, 2018. Available at https://www.ihe.net/uploadedFiles/Documents/Radiology/IHE_RAD_Suppl_MRRT.pdf. Accessed 1 April 2020

  12. 12.

    Pinto Dos Santos D, Klos G, Kloeckner R, Oberle R, Dueber C, Mildenberger P: Development of an IHE MRRT-compliant open-source web-based reporting platform. Eur Radiol 27(1):424–30, 2017

    Article  Google Scholar 

  13. 13.

    Jungmann F, Kuhn S, Kampgen B: [Basics and applications of Natural Language Processing (NLP) in radiology]. Radiologe 58(8):764–8, 2018

    CAS  Article  Google Scholar 

  14. 14.

    Dutta S, Long WJ, Brown DF, Reisner AT: Automated detection using natural language processing of radiologists recommendations for additional imaging of incidental findings. Ann Emerg Med 62(2):162–9, 2013

    Article  Google Scholar 

  15. 15.

    Huesch MD, Cherian R, Labib S, Mahraj R: Evaluating Report Text Variation and Informativeness: Natural Language Processing of CT Chest Imaging for Pulmonary Embolism. J Am Coll Radiol 15(3):554–62, 2018

    Article  Google Scholar 

  16. 16.

    Galvez JA, Pappas JM, Ahumada L, Martin JN, Simpao AF, Rehman MA, Witmer C: The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children. J Thromb Thrombolysis 44(3):281–90, 2017

    Article  Google Scholar 

  17. 17.

    Langlotz CP: RadLex: a new method for indexing online educational materials. Radiographics 26(6):1595-7, 2006

    Article  Google Scholar 

  18. 18.

    Jungmann F, Kuhn S, Tsaur I, Kampgen B: Natural language processing in radiology: Neither trivial nor impossible. Radiologe 59(9):828–32, 2019

  19. 19.

    Pinto dos Santos D, Arnhold G, Mildenberger P, Düber C, Kloeckner R: Guidelines Regarding §16 of the German Transplantation Act - Initial Experiences with Structured Reporting. Rofo 189:1145–51, 2017

  20. 20.

    Pinto Dos Santos D, Baessler B: Big data, artificial intelligence, and structured reporting. Eur Radiol Exp 2(1):42, 2018

    Article  Google Scholar 

  21. 21.

    Pinto Dos Santos D, Hempel JM, Mildenberger P, Klockner R, Persigehl T: Structured Reporting in Clinical Routine. Rofo 191(1):33–9, 2019

    Article  Google Scholar 

  22. 22.

    Pinto dos Santos D, Brodehl S, Baeßler B, Arnhold G, Dratsch T, Chon SH et al.: Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs. Insights Imaging 10(1):93, 2019

  23. 23.

    Radiological Society of North America: RadReport Template Library. Available at https://radreport.org. Accessed 1 April 2020

  24. 24.

    Chen PH, Zafar H, Galperin-Aizenberg M, Cook T: Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports. J Digit Imaging 31(2):178–84, 2018

    Article  Google Scholar 

  25. 25.

    Fukuhara H, Ichiyanagi O, Midorikawa S, Kakizaki H, Kaneko H, Tsuchiya N: Internal validation of a scoring system to evaluate the probability of ureteral stones: The CHOKAI score. Am J Emerg Med 35(12):1859–66, 2017

    Article  Google Scholar 

  26. 26.

    Wang RC, Rodriguez RM, Moghadassi M, Noble V, Bailitz J, Mallin M, Corbo J, Kang TL, Chu P, Shiboski S, Smith-Bindman R: External Validation of the STONE Score, a Clinical Prediction Rule for Ureteral Stone: An Observational Multi-institutional Study. Ann Emerg Med 67(4):423–32 e2, 2016

    Article  Google Scholar 

  27. 27.

    Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, Bellomi M. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp 2(1):36, 2018

    Article  Google Scholar 

  28. 28.

    Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S: Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749–62, 2017

    Article  Google Scholar 

  29. 29.

    Folio LR, Machado LB, Dwyer AJ: Multimedia-enhanced Radiology Reports: Concept, Components, and Challenges. Radiographics 38(2):462–82, 2018

    Article  Google Scholar 

Download references


This project received funding from the Euratom research and training programme, 2014–2018, under grant agreement No 755523.

Author information



Corresponding author

Correspondence to Florian Jungmann.

Ethics declarations

Benedikt Kämpgen is an employee of the Empolis Information Management GmbH (Kaiserslautern, Germany).

This article does not contain any studies with human participants or animals performed by any of the authors.

This retrospective, single-site, controlled cohort study did not need professional legal advice by the Institutional Review Board or informed consent of patients according to the state hospital law. All analyzed patient data were fully de-identified.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jungmann, F., Arnhold, G., Kämpgen, B. et al. A Hybrid Reporting Platform for Extended RadLex Coding Combining Structured Reporting Templates and Natural Language Processing. J Digit Imaging 33, 1026–1033 (2020). https://doi.org/10.1007/s10278-020-00342-0

Download citation


  • Structured reporting
  • Natural language processing
  • RadLex
  • Medical informatics
  • Database