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29.10.2015 | Breast

A simple scoring system for breast MRI interpretation: does it compensate for reader experience?

verfasst von: Maria Adele Marino, Paola Clauser, Ramona Woitek, Georg J. Wengert, Panagiotis Kapetas, Maria Bernathova, Katja Pinker-Domenig, Thomas H. Helbich, Klaus Preidler, Pascal A. T. Baltzer

Erschienen in: European Radiology | Ausgabe 8/2016

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Abstract

Purpose

To investigate the impact of a scoring system (Tree) on inter-reader agreement and diagnostic performance in breast MRI reading.

Materials and methods

This IRB-approved, single-centre study included 100 patients with 121 consecutive histopathologically verified lesions (52 malignant, 68 benign). Four breast radiologists with different levels of MRI experience and blinded to histopathology retrospectively evaluated all examinations. Readers independently applied two methods to classify breast lesions: BI-RADS and Tree. BI-RADS provides a reporting lexicon that is empirically translated into likelihoods of malignancy; Tree is a scoring system that results in a diagnostic category. Readings were compared by ROC analysis and kappa statistics.

Results

Inter-reader agreement was substantial to almost perfect (kappa: 0.643–0.896) for Tree and moderate (kappa: 0.455–0.657) for BI-RADS. Diagnostic performance using Tree (AUC: 0.889–0.943) was similar to BI-RADS (AUC: 0.872–0.953). Less experienced radiologists achieved AUC: improvements up to 4.7 % using Tree (P-values: 0.042–0.698); an expert’s performance did not change (P = 0.526). The least experienced reader improved in specificity using Tree (16 %, P = 0.001). No further sensitivity and specificity differences were found (P > 0.1).

Conclusion

The Tree scoring system improves inter-reader agreement and achieves a diagnostic performance similar to that of BI-RADS. Less experienced radiologists, in particular, benefit from Tree.

Key Points

• The Tree scoring system shows high diagnostic accuracy in mass and non-mass lesions.

• The Tree scoring system reduces inter-reader variability related to reader experience.

• The Tree scoring system improves diagnostic accuracy in non-expert readers.

Warner E, Messersmith H, Causer P et al (2008) Systematic review: using magnetic resonance imaging to screen women at high risk for breast cancer. Ann Intern Med 148:671–679CrossRefPubMed

Riedl CC, Luft N, Bernhart C et al (2015) Triple-modality screening trial for familial breast cancer underlines the importance of magnetic resonance imaging and questions the role of mammography and ultrasound regardless of patient mutation status, age, and breast density. J Clin Oncol Off J Am Soc Clin Oncol. doi:10.1200/JCO.2014.56.8626

Houssami N, Ciatto S, Macaskill P et al (2008) Accuracy and surgical impact of magnetic resonance imaging in breast cancer staging: systematic review and meta-analysis in detection of multifocal and multicentric cancer. J Clin Oncol Off J Am Soc Clin Oncol 26:3248–3258CrossRef

Kuhl C (2007) The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology 244:356–378CrossRefPubMed

Sardanelli F, Boetes C, Borisch B et al (2010) Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. Eur J Cancer Oxf Engl 46:1296–1316CrossRef

Mann RM, Balleyguier C, Baltzer PA, European Society of Breast Imaging (EUSOBI), with language review by Europa Donna–The European Breast Cancer Coalition et al (2015) Breast MRI: EUSOBI recommendations for women’s information. Eur Radiol. doi:10.1007/s00330-015-3807-z PubMedCentral

The American College of Radiology (ACR) (2013) Breast Imaging Reporting and Data System Atlas (BI-RADS® Atlas), Reston, VA

Pinker K, Bogner W, Baltzer P et al (2014) Improved differentiation of benign and malignant breast tumors with multiparametric 18Fluorodeoxyglucose positron emission tomography magnetic resonance imaging: a feasibility study. Clin Cancer Res. doi:10.1158/1078-0432.CCR-13-2810 PubMed

Benndorf M, Baltzer PAT, Kaiser WA (2011) Assessing the degree of collinearity among the lesion features of the MRI BI-RADS lexicon. Eur J Radiol 80:e322–e324CrossRefPubMed

10.

Ikeda DM, Hylton NM, Kinkel K et al (2001) Development, standardization, and testing of a lexicon for reporting contrast-enhanced breast magnetic resonance imaging studies. J Magn Reson Imaging 13:889–895CrossRefPubMed

11.

Kim SJ, Morris EA, Liberman L et al (2001) Observer variability and applicability of BI-RADS terminology for breast MR imaging: invasive carcinomas as focal masses. AJR Am J Roentgenol 177:551–557CrossRefPubMed

12.

Kinkel K, Helbich TH, Esserman LJ et al (2000) Dynamic high-spatial-resolution MR imaging of suspicious breast lesions: diagnostic criteria and interobserver variability. AJR Am J Roentgenol 175:35–43CrossRefPubMed

13.

Stoutjesdijk MJ, Fütterer JJ, Boetes C et al (2005) Variability in the description of morphologic and contrast enhancement characteristics of breast lesions on magnetic resonance imaging. Invest Radiol 40:355–362CrossRefPubMed

14.

OCEBM Levels of Evidence Working Group. “The Oxford Levels of Evidence 2”. Oxford Centre for Evidence-Based Medicine. http://www.cebm.net/index.aspx?o=5653

15.

Baltzer PAT, Dietzel M, Kaiser WA (2013) A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. Eur Radiol 23:2051–2060CrossRefPubMed

16.

Baum F, Fischer U, Vosshenrich R, Grabbe E (2002) Classification of hypervascularized lesions in CE MR imaging of the breast. Eur Radiol 12:1087–1092CrossRefPubMed

17.

Nunes LW, Schnall MD, Orel SG et al (1997) Breast MR imaging: interpretation model. Radiology 202:833–841CrossRefPubMed

18.

Tozaki M, Igarashi T, Matsushima S, Fukuda K (2005) High-spatial-resolution MR imaging of focal breast masses: interpretation model based on kinetic and morphological parameters. Radiat Med 23:43–50PubMed

19.

Tozaki M, Fukuda K (2006) High-spatial-resolution MRI of non-masslike breast lesions: interpretation model based on BI-RADS MRI descriptors. AJR Am J Roentgenol 187:330–337CrossRefPubMed

20.

Demartini WB, Kurland BF, Gutierrez RL et al (2011) Probability of malignancy for lesions detected on breast MRI: a predictive model incorporating BI-RADS imaging features and patient characteristics. Eur Radiol 21:1609–1617CrossRefPubMed

21.

Pinker K, Bogner W, Baltzer P et al (2014) Improved diagnostic accuracy with multiparametric magnetic resonance imaging of the breast using dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging, and 3-dimensional proton magnetic resonance spectroscopic imaging. Invest Radiol. doi:10.1097/RLI.0000000000000029

22.

Perry N, Broeders M, de Wolf C et al (2008) European guidelines for quality assurance in breast cancer screening and diagnosis. Fourth edition—summary document. Ann Oncol 19:614–622CrossRefPubMed

23.

Gutierrez RL, DeMartini WB, Eby PR et al (2009) BI-RADS lesion characteristics predict likelihood of malignancy in breast MRI for masses but not for nonmasslike enhancement. AJR Am J Roentgenol 193:994–1000CrossRefPubMed

24.

Baltzer PAT, Benndorf M, Dietzel M et al (2010) False-positive findings at contrast-enhanced breast MRI: a BI-RADS descriptor study. AJR Am J Roentgenol 194:1658–1663CrossRefPubMed

25.

Baltzer PAT, Kaiser WA and Dietzel M (2015) Lesion type and reader experience affect the diagnostic accuracy of breast MRI: a multiple reader ROC study. Eur J Radiol 84(1):86–91. doi:10.1016/j.ejrad.2014.10.023.

26.

Jansen SA, Shimauchi A, Zak L et al (2011) The diverse pathology and kinetics of mass, nonmass, and focus enhancement on MR imaging of the breast. J Magn Reson Imaging 33:1382–1389CrossRefPubMedPubMedCentral

27.

Dietzel M, Baltzer PAT, Schön K, Kaiser WA (2012) MR-mammography: high sensitivity but low specificity? New thoughts and fresh data on an old mantra. Eur J Radiol 81:S30–S32CrossRefPubMed

28.

Thomassin-Naggara I, Trop I, Chopier J et al (2011) Nonmasslike enhancement at breast MR imaging: the added value of mammography and US for lesion categorization. Radiology 261:69–79CrossRefPubMed

29.

Pinker K, Bickel H, Helbich TH et al (2013) Combined contrast-enhanced magnetic resonance and diffusion-weighted imaging reading adapted to the “Breast Imaging Reporting and Data System” for multiparametric 3-T imaging of breast lesions. Eur Radiol 23:1791–1802CrossRefPubMed

30.

Baltzer A, Dietzel M, Kaiser CG, Baltzer PA (2015) Combined reading of contrast enhanced and diffusion weighted magnetic resonance imaging by using a simple sum score. Eur Radiol. doi:10.1007/s00330-015-3886-x PubMedCentral

Titel: A simple scoring system for breast MRI interpretation: does it compensate for reader experience?
verfasst von: Maria Adele Marino
Paola Clauser
Ramona Woitek
Georg J. Wengert
Panagiotis Kapetas
Maria Bernathova
Katja Pinker-Domenig
Thomas H. Helbich
Klaus Preidler
Pascal A. T. Baltzer
Publikationsdatum: 29.10.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 8/2016
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-015-4075-7

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A simple scoring system for breast MRI interpretation: does it compensate for reader experience?