The Value of Structured Reporting for AI

  • Daniel Pinto dos Santos


Besides image data from which AI systems could potentially extract meaningful information, radiological departments possess vast amounts of clinically relevant information contained in the report texts associated with the respective imaging studies. However, the automated extraction of data contained in radiological reports is difficult due to the unstructured and heterogeneous nature of current day’s prose-like reports. Even though natural language processing has seen substantial improvements over the past years, it remains difficult to use radiological reports from clinical routine as valid annotations for the training of algorithms in computer vision.

Structured reporting is currently being discussed within the radiological communities and besides providing other benefits, for example, in the communication with referring physicians, would make radiological reports much more machine-readable. Also, through providing clearly defined structures, report templates would facilitate data from other systems to be integrated into the radiological report.

This chapter aims to provide an overview of the current state of structured reporting with a special focus on its potential implications for the development of and interaction with AI systems.


Radiological reporting Structured reporting Natural language processing Data annotations Data integration 


  1. 1.
    Langlotz CP. The radiology report. 2015.Google Scholar
  2. 2.
    Clinger NJ, Hunter TB, Hillman BJ. Radiology reporting: attitudes of referring physicians. Radiology. 1988;169(3):825–6.CrossRefGoogle Scholar
  3. 3.
    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;259(1):184–95.CrossRefGoogle Scholar
  4. 4.
    Hall FM. Language of the radiology report: primer for residents and wayward radiologists. Am J Roentgenol. 2000 Nov;175(5):1239–42.CrossRefGoogle Scholar
  5. 5.
    Ridley LJ. Guide to the radiology report. Australas Radiol. 2002;46(4):366–9.CrossRefGoogle Scholar
  6. 6.
    Sistrom C, Lanier L, Mancuso A. Reporting instruction for radiology residents. Acad Radiol. 2004;11(1):76–84.CrossRefGoogle Scholar
  7. 7.
    Schwartz LH, Panicek DM, Berk AR, Li Y, Hricak H. Improving communication of diagnostic radiology findings through structured reporting. Radiology. 2011;260(1):174–81.CrossRefGoogle Scholar
  8. 8.
    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;274(2):464–72.CrossRefGoogle Scholar
  9. 9.
    Flusberg M, Ganeles J, Ekinci T, Goldberg-Stein S, Paroder V, Kobi M, et al. Impact of a structured report template on the quality of CT and MRI reports for hepatocellular carcinoma diagnosis. J Am Coll Radiol. 2017;14(9):1206–11.CrossRefGoogle Scholar
  10. 10.
    Sahni VA, Silveira PC, Sainani NI, Khorasani R. Impact of a structured report template on the quality of MRI reports for rectal cancer staging. Am J Roentgenol. 2015;205(3):584–8.CrossRefGoogle Scholar
  11. 11.
    Sabel BO, Plum JL, Kneidinger N, Leuschner G, Koletzko L, Raziorrouh B, et al. Structured reporting of CT examinations in acute pulmonary embolism. J Cardiovasc Comput Tomogr. 2017;11:188–95.CrossRefGoogle Scholar
  12. 12.
    Dickerson E, Davenport MS, Syed F, Stuve O, Cohen JA, Rinker JR, et al. Effect of template reporting of brain MRIs for multiple sclerosis on report thoroughness and neurologist-rated quality: results of a prospective quality improvement project. J Am Coll Radiol. 2016;14:371–379.e1.CrossRefGoogle Scholar
  13. 13.
    Evans LR, Fitzgerald MC, Varma D, Mitra B. A novel approach to improving the interpretation of CT brain in trauma. Injury. 2017;49:56–61.CrossRefGoogle Scholar
  14. 14.
    Dunnick NR, Langlotz CP. The radiology report of the future: a summary of the 2007 Intersociety Conference. J Am Coll Radiol. 2008;5:626–9.CrossRefGoogle Scholar
  15. 15.
    Hickey P. Standardization of Roentgen-ray reports. Am J Roentgenol. 1922;9:422–5.Google Scholar
  16. 16.
    IHE Radiology Technical Committee. IHE radiology technical framework supplement management of radiology report templates (MRRT). 2017. p.1–51.Google Scholar
  17. 17.
    Langlotz CP. RadLex: a new method for indexing online educational materials. Radiographics. 2006;26(6):1595–7.CrossRefGoogle Scholar
  18. 18.
    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. 2017;27(1):424–30.CrossRefGoogle Scholar
  19. 19.
    Rubin DL, Kahn CE. Common data elements in radiology. Radiology. 2016;283:837–44.CrossRefGoogle Scholar
  20. 20.
    Channin DS, Mongkolwat P, Kleper V, Rubin DL. The annotation and image mark-up project. Radiology. 2009;253(3):590–2.CrossRefGoogle Scholar
  21. 21.
    Tesauro G, Gondek DC, Lenchner J, Fan J, Prager JM. Analysis of Watson’s strategies for playing Jeopardy! J Artif Intell Res. 2013;47:205–51.CrossRefGoogle Scholar
  22. 22.
    Kreimeyer K, Foster M, Pandey A, Arya N, Halford G, Jones SF, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Inform. 2017;73:14–29.CrossRefGoogle Scholar
  23. 23.
    Ni Y, Kennebeck S, Dexheimer JW, McAneney CM, Tang H, Lingren T, et al. Automated clinical trial eligibility prescreening. J Am Med Inform Assoc. 2015;22(1):166–78.CrossRefGoogle Scholar
  24. 24.
    Cai T, Giannopoulos AA, Yu S, Kelil T, Ripley B, Kumamaru KK, et al. Natural language processing technologies in radiology research and clinical applications. Radiographics. 2016;36(1):176–91.CrossRefGoogle Scholar
  25. 25.
    Hassanpour S, Langlotz CP. Information extraction from multi-institutional radiology reports. Artif Intell Med. 2016;66:29–39.CrossRefGoogle Scholar
  26. 26.
    Gerstmair A, Daumke P, Simon K, Langer M, Kotter E. Intelligent image retrieval based on radiology reports. Eur Radiol. 2012;22(12):2750–8.CrossRefGoogle Scholar
  27. 27.
    Zech J, Pain M, Titano J, Badgeley M, Schefflein J, Su A, et al. Natural language-based machine learning models for the annotation of clinical radiology reports. Radiology. 2018;287:570–80.CrossRefGoogle Scholar
  28. 28.
    Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. 2017. cs.CV,
  29. 29.
    Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers R. ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Accessed 12 Aug 2018.
  30. 30.
    Oakden-Rayner L. CheXNet: an in-depth review. 2018., Accessed 12 Aug 2018.
  31. 31.
    Beets-Tan RGH, Lambregts DMJ, Maas M, Bipat S, Barbaro B, Curvo-Semedo L, et al. Magnetic resonance imaging for clinical management of rectal cancer: updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting. Eur Radiol. 2017;23(Suppl 1):2522–11.Google Scholar
  32. 32.
    KSAR Study Group for Rectal Cancer. Essential items for structured reporting of rectal cancer MRI: 2016 consensus recommendation from the korean society of abdominal radiology. Korean J Radiol. 2017;18(1):132–51.CrossRefGoogle Scholar
  33. 33.
    Al-Hawary MM, Francis IR, Chari ST, Fishman EK, Hough DM, Lu DS, et al. Pancreatic ductal adenocarcinoma radiology reporting template: consensus statement of the Society of Abdominal Radiology and the American Pancreatic Association. Radiology. 2014;270(1):248–60.CrossRefGoogle Scholar
  34. 34.
    Anderson TJT, Lu N, Brook OR. Disease-specific report templates for your practice. J Am Coll Radiol. 2017;14(8):1055–7.CrossRefGoogle Scholar
  35. 35.
    Daniel PDS, Sonja S, Gordon A, Aline M-K, Christoph D, Peter M, et al. A proof of concept for epidemiological research using structured reporting with pulmonary embolism as a use case. Br J Radiol. 2018;91:20170564.Google Scholar
  36. 36.
    Goldberg-Stein S, Gutman D, Kaplun O, Wang D, Negassa A, Scheinfeld MH. Autopopulation of intravenous contrast type and dose in structured report templates decreases report addenda. J Am Coll Radiol. 2017;14(5):659–61.CrossRefGoogle Scholar
  37. 37.
    Lee M-C, Chuang K-S, Hsu T-C, Lee C-D. Enhancement of structured reporting – an integration reporting module with radiation dose collection supporting. J Med Syst. 2016;40(11):852.CrossRefGoogle Scholar
  38. 38.
    Wells PS, Anderson DR, Rodger M, Stiell I, Dreyer JF, Barnes D, et al. Excluding pulmonary embolism at the bedside without diagnostic imaging: management of patients with suspected pulmonary embolism presenting to the emergency department by using a simple clinical model and d-dimer. Ann Intern Med. 2001;135(2):98–107.CrossRefGoogle Scholar
  39. 39.
    MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284(1):228–43.CrossRefGoogle Scholar
  40. 40.
    Lacson R, Prevedello LM, Andriole KP, Gill R, Lenoci-Edwards J, Roy C, et al. Factors associated with radiologists’ adherence to Fleischner Society guidelines for management of pulmonary nodules. J Am Coll Radiol. 2012;9(7):468–73.CrossRefGoogle Scholar
  41. 41.
    Blagev DP, Lloyd JF, Conner K, Dickerson J, Adams D, Stevens SM, et al. Follow-up of incidental pulmonary nodules and the radiology report. J Am Coll Radiol. 2016;13(2 Suppl):R18–24.CrossRefGoogle Scholar
  42. 42.
    Wolf SJ, McCubbin TR, Feldhaus KM, Faragher JP, Adcock DM. Prospective validation of wells criteria in the evaluation of patients with suspected pulmonary embolism. Ann Emerg Med. 2004;44(5):503–10.CrossRefGoogle Scholar
  43. 43.
    Righini M, Van Es J, Exter Den PL, Roy P-M, Verschuren F, Ghuysen A, et al. Age-adjusted D-dimer cutoff levels to rule out pulmonary embolism: the ADJUST-PE study. JAMA. 2014;311(11):1117–24.CrossRefGoogle Scholar
  44. 44.
    Char S, Yoon H-C. Improving appropriate use of pulmonary computed tomography angiography by increasing the serum D-dimer threshold and assessing clinical probability. Perm J. 2014;18(4):10–5.CrossRefGoogle Scholar
  45. 45.
    Raja AS, Ip IK, Dunne RM, Schuur JD, Mills AM, Khorasani R. Effects of performance feedback reports on adherence to evidence-based guidelines in use of CT for evaluation of pulmonary embolism in the emergency department: a randomized trial. Am J Roentgenol. 2015;205(5):1–5.CrossRefGoogle Scholar
  46. 46.
    Raja AS, Ip IK, Prevedello LM, Sodickson AD, Farkas C, Zane RD, et al. Effect of computerized clinical decision support on the use and yield of CT pulmonary angiography in the emergency department. Radiology. 2012;262(2):468–74.CrossRefGoogle Scholar
  47. 47.
    Hutchinson BD, Navin P, Marom EM, Truong MT, Bruzzi JF. Overdiagnosis of pulmonary embolism by pulmonary CT angiography. Am J Roentgenol. 2015;205(2):271–7.CrossRefGoogle Scholar
  48. 48.
    Yoo HH, Queluz TH, Dib El R. Anticoagulant treatment for subsegmental pulmonary embolism. Cochrane Database Syst Rev. 2016;126(4):e266.Google Scholar
  49. 49.
    Bariteau A, Stewart LK, Emmett TW, Kline JA. Systematic review and meta-analysis of outcomes of patients with subsegmental pulmonary embolism with and without anticoagulation treatment. Acad Emerg Med. 2018;25(1):CD010222.Google Scholar
  50. 50.
    Kelahan LC, Kalaria AD, Filice RW. PathBot: a radiology-pathology correlation dashboard. J Digit Imaging. 2017;30(6):681–6.CrossRefGoogle Scholar
  51. 51.
    Bosmans JML, Neri E, Ratib O, Kahn CE. Structured reporting: a fusion reactor hungry for fuel. Insights Imaging. 2015;6(1):129–32.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Daniel Pinto dos Santos
    • 1
  1. 1.University Hospital of CologneCologneGermany

Personalised recommendations