Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
European Radiology
Published in: European Radiology 8/2016

01-08-2016 | Breast

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

Authors: 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

Published in: European Radiology | Issue 8/2016

Login to get access

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.
Literature
1.
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
2.
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
3.
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
4.
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
5.
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
6.
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
7.
The American College of Radiology (ACR) (2013) Breast Imaging Reporting and Data System Atlas (BI-RADS® Atlas), Reston, VA
8.
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
9.
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.
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.
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.
Metadata
Title
A simple scoring system for breast MRI interpretation: does it compensate for reader experience?
Authors
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
Publication date
01-08-2016
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 8/2016
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-015-4075-7

Other articles of this Issue 8/2016

European Radiology 8/2016 Go to the issue

Interventional

Navigated MRI-guided liver biopsies in a closed-bore scanner: experience in 52 patients

Hepatobiliary-Pancreas

Advantages of radial volumetric breath-hold examination (VIBE) with k-space weighted image contrast reconstruction (KWIC) over Cartesian VIBE in liver imaging of volunteers simulating inadequate or no breath-holding ability

Head and Neck

Comparison of MR imaging findings in paediatric and adult patients with acute mastoiditis and incidental intramastoid bright signal on T2-weighted images

Magnetic Resonance

Non-contrast-enhanced MR angiography using time-spin labelling inversion pulse technique for detecting crossing renal vessels in children with symptomatic ureteropelvic junction obstruction: comparison with surgical findings

Neuro

Quantitative validation of a visual rating scale for frontal atrophy: associations with clinical status, APOE e4, CSF biomarkers and cognition

Computed Tomography

Organ-based tube current modulation in a clinical context: Dose reduction may be largely overestimated in breast tissue