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La radiologia medica

, Volume 124, Issue 7, pp 628–635 | Cite as

Value of structured reporting in neuromuscular disorders

  • Francesco AlessandrinoEmail author
  • Lara Cristiano
  • Claudia Maria Cinnante
  • Tommaso Tartaglione
  • Simonetta Gerevini
  • Tommaso Verdolotti
  • Giovanna Stefania Colafati
  • Emanuele Ghione
  • Raimondo Vitale
  • Lorenzo Peverelli
  • Claudia Brogna
  • Angela Berardinelli
  • Maurizio Moggio
  • Eugenio M. Mercuri
  • Anna Pichiecchio
HEAD, NECK AND DENTAL RADIOLOGY

Abstract

Objective

To assess whether structured reports (SRs) of MRI in patients with inherited neuromuscular disorders (IND) provide more clinically relevant information than non-structured reports (NSRs) and whether neuroradiologists’ expertise affects completeness of reports.

Material and methods

Lower limbs’ MRI reports of patients with IND produced by neuroradiologists with different level of expertise (> 15 years vs. < 15 years of experience in reading IND-MRI) before and after implementation of a SR template were included. Reports were assessed for the presence of 9 key features relevant for IND management. Reports and images were evaluated by neurologists who assessed: disease-specific muscular involvement pattern; presence of sufficient information to order the appropriate genetic/diagnostic tests; presence of sufficient information to make therapeutic decision/perform biopsy and necessity to review MRI images. Mann–Whitney and Fisher’s exact tests were used to compare the number of key features for NSR and SR and neurologists’ answers for reports produced by neuroradiologists with different experience.

Results

Thirty-one SRs and 101 NSRs were reviewed. A median of 8 and 6 key features was present in SR and NSR, respectively (p value < 0.0001). When reports were produced by less expert neuroradiologists, neurologists recognized muscular involvement pattern, had sufficient information for clinical decision-making/perform biopsy more often with SR than NSR (p values: < 0.0001), and needed to evaluate images less often with SR (p value: 0.0001). When reports produced by expert neuroradiologists were evaluated, no significant difference in neurologists’ answers was observed.

Conclusion

SR of IND-MRI contained more often clinically relevant information considered important for disease management than NSR. Radiologist’s expertise affects completeness of NSR reports.

Keywords

Magnetic resonance imaging Neuromuscular diseases Limb-girdle muscular dystrophies Sarcoglycanopathies Structured reporting 

Notes

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

For this type of study formal consent is not required.

References

  1. 1.
    Quijano-Roy S, Avila-Smirnow D, Carlier RY, WB-MRI muscle study group (2012) Whole body muscle MRI protocol: pattern recognition in early onset NM disorders. Neuromuscul Disord 22(S2):S68–S84CrossRefPubMedGoogle Scholar
  2. 2.
    Gonorazky HD, Bönnemann CG, Dowling JJ (2018) The genetics of congenital myopathies. Handb Clin Neurol 148:549–564CrossRefPubMedGoogle Scholar
  3. 3.
    Cassandrini D, Trovato R, Rubegni A et al (2017) Congenital myopathies: clinical phenotypes and new diagnostic tools. Ital J Pediatr 43:101CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Wattjes MP, Kley RA, Fischer D (2010) Neuromuscular imaging in inherited muscle diseases. Eur Radiol 20:2447–2460CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Warman Chardon J, Straub V (2017) The role of muscle imaging in the diagnosis and assessment of children with genetic muscle disease. Neuropediatrics 48:233–241CrossRefPubMedGoogle Scholar
  6. 6.
    Jungbluth H (2017) Myopathology in times of modern imaging. Neuropathol Appl Neurobiol 43:24–43CrossRefPubMedGoogle Scholar
  7. 7.
    Fischer D, Bonati U, Wattjes MP (2016) Recent developments in muscle imaging of neuromuscular disorders. Curr Opin Neurol 29:614–620CrossRefPubMedGoogle Scholar
  8. 8.
    Quijano-Roy S, Carlier RY, Fischer D (2011) Muscle imaging in congenital myopathies. Semin Pediatr Neurol 18:221–229CrossRefPubMedGoogle Scholar
  9. 9.
    Brook OR, Brook A, Vollmer CM, Kent TS, Sanchez N, Pedrosa I (2015) Structured reporting of multiphasic CT for pancreatic cancer: potential effect on staging and surgical planning. Radiology 274:464–472CrossRefPubMedGoogle Scholar
  10. 10.
    Al-Sukhni E, Messenger DE, Victor JC, McLeod RS, Kennedy ED (2013) Do MRI reports contain adequate preoperative staging information for end users to make appropriate treatment decisions for rectal cancer? Ann Surg Oncol 20:1148–1155CrossRefPubMedGoogle Scholar
  11. 11.
    Dickerson E, Davenport MS, Syed F et al (2017) 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 14(3):371–379CrossRefPubMedGoogle Scholar
  12. 12.
    Alessandrino F, Pichiecchio A, Mallucci G et al (2018) Do MRI structured reports for multiple sclerosis contain adequate information for clinical decision making? AJR Am J Roentgenol 210:24–29CrossRefPubMedGoogle Scholar
  13. 13.
    Schiavon F, Girgenti F (2008) The structured report and PACS. In: Radiological reporting in clinical practice. Springer, Milano, pp 103–108Google Scholar
  14. 14.
    Sabel BO, Plum JL, Kneidinger N et al (2017) Structured reporting of CT examinations in acute pulmonary embolism. J Cardiovasc Comput Tomogr 11:188–195CrossRefPubMedGoogle Scholar
  15. 15.
    European Society of Radiology (ESR) (2018) ESR paper on structured reporting in radiology. Insights Imaging 9:1–7CrossRefGoogle Scholar
  16. 16.
    Johnson AJ, Chen MYM, Swan JS, Applegate KE, Littenberg B (2009) Cohort study of structured reporting compared with conventional dictation. Radiology 253:74–80CrossRefPubMedGoogle Scholar
  17. 17.
    Faggioni L, Coppola F, Ferrari R, Neri E, Regge D (2017) Usage of structured reporting in radiological practice: results from an Italian online survey. Eur Radiol 27(5):1934–1943CrossRefPubMedGoogle Scholar
  18. 18.
    Mercuri E, Cini C, Pichiecchio A et al (2003) Muscle magnetic resonance imaging in patients with congenital muscular dystrophy and Ullrich phenotype. Neuromuscul Disord 13:554–558CrossRefPubMedGoogle Scholar
  19. 19.
    Mercuri E, Pichiecchio A, Allsop J, Messina S, Pane M, Muntoni F (2007) Muscle MRI in inherited neuromuscular disorders: past, present, and future. J Magn Reson Imaging 25:433–440CrossRefPubMedGoogle Scholar
  20. 20.
    Straub V, Carlier PG, Mercuri E (2012) TREAT-NMD workshop: pattern recognition in genetic muscle diseases using muscle MRI: 25–26 February 2011, Rome, Italy. Neuromuscul Disord 22:S42–S53CrossRefPubMedGoogle Scholar
  21. 21.
    Margolies LR, Pandey G, Horowitz ER, Mendelson DS (2016) Breast imaging in the era of big data: structured reporting and data mining. AJR Am J Roentgenol 206:259–264CrossRefPubMedGoogle Scholar
  22. 22.
    D’Orsi CJ, Sickles EA, Mendelson EB, et al (2013) ACR BI-RADS® Atlas, breast imaging reporting and data system, 5th edn. American College of Radiology, Reston, VA. https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Bi-Rads. Accessed 11 Feb 2019
  23. 23.
    Lin E, Powell DK, Kagetsu NJ (2014) Efficacy of a checklist-style structured radiology reporting template in reducing resident misses on cervical spine computed tomography examinations. J Digit Imaging 27:588–593CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Gassenmaier S, Armbruster M, Haasters F et al (2017) Structured reporting of MRI of the shoulder—improvement of report quality? Eur Radiol 27:4110–4119CrossRefPubMedGoogle Scholar
  25. 25.
    Bink A, Benner J, Reinhardt J et al (2018) Structured reporting in neuroradiology: intracranial tumors. Front Neurol 9:32CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Tasca G, Monforte M, Díaz-Manera J et al (2018) MRI in sarcoglycanopathies: a large international cohort study. J Neurol Neurosurg Psychiatry 89(1):72–77CrossRefPubMedGoogle Scholar

Copyright information

© Italian Society of Medical Radiology 2019

Authors and Affiliations

  • Francesco Alessandrino
    • 1
    • 11
    Email author
  • Lara Cristiano
    • 2
    • 3
  • Claudia Maria Cinnante
    • 4
  • Tommaso Tartaglione
    • 2
    • 3
  • Simonetta Gerevini
    • 5
  • Tommaso Verdolotti
    • 2
  • Giovanna Stefania Colafati
    • 6
  • Emanuele Ghione
    • 1
  • Raimondo Vitale
    • 1
  • Lorenzo Peverelli
    • 7
  • Claudia Brogna
    • 8
  • Angela Berardinelli
    • 9
  • Maurizio Moggio
    • 7
  • Eugenio M. Mercuri
    • 8
  • Anna Pichiecchio
    • 1
    • 10
  1. 1.Neuroradiology DepartmentIRCCS C. Mondino FoundationPaviaItaly
  2. 2.Department of RadiologyUniversità Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli”RomeItaly
  3. 3.Radiology UnitIstituto Dermopatico dell’Immacolata-IRCCS-FLMMRomeItaly
  4. 4.Neuroradiology UnitFondazione IRCCS Ca’ Granda Ospedale Maggiore PoliclinicoMilanItaly
  5. 5.Unit of NeuroradiologyIRCCS San Raffaele Scientific InstituteMilanItaly
  6. 6.Neuroradiology Unit, Department of ImagingIRCCS Bambino Gesù Children’s HospitalRomeItaly
  7. 7.Neuromuscular and Rare Diseases Unit, Department of NeuroscienceFondazione IRCCS Ca’ Granda Ospedale Maggiore PoliclinicoMilanItaly
  8. 8.Pediatric Neurology and Nemo Clinical CentreUniversità Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli”RomeItaly
  9. 9.Child and Adolescent UnitIRCCS C. Mondino FoundationPaviaItaly
  10. 10.Department of Brain and Behavioural NeuroscienceUniversity of PaviaPaviaItaly
  11. 11.Department of Radiology, Harvard Medical SchoolBrigham and Women’s HospitalBostonUSA

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