Evidenzbasierte und strukturierte Diagnostik in der MR-Mammografie anhand des Kaiser-Score

Zusammenfassung

Hintergrund Die MR-Mammografie (MRM) ist als sensitivstes Verfahren zur Detektion von Brustkrebs integraler Bestandteil der modernen Mammadiagnostik. Aufgrund umfangreicher multiparametrischer Bildinformationen gilt die Befundung der MRM jedoch als schwierig. Klinische Entscheidungsregeln kombinieren diagnostische Kriterien in einem Algorithmus. Damit unterstützen sie Radiologen dabei, objektive und exakte sowie weitgehend von der Untersuchererfahrung unabhängige MRM-Diagnosen zu stellen.

Methodik Narrativer review. Der Kaiser-Score (KS) als klinische Entscheidungsregel für die MRM wird eingeführt. Befundkriterien werden erläutert, Strategien zur klinischen Entscheidungsfindung diskutiert und illustriert.

Ergebnisse Entwickelt mit Methoden des maschinellen Lernens wurde der Kaiser-Score in internationalen Studien unabhängig validiert. Dabei ist der KS unabhängig von der Untersuchungstechnik. Anhand von auf T2w- und kontrastangehobenen T1w-Aufnahmen fassbaren diagnostischen BI-RADS-Kriterien ermöglicht der KS die objektive und genaue Differenzialdiagnose von benignen und malignen Befunden in der MRM. Ein Flowchart leitet den Leser über maximal 3 Zwischenschritte zu einem Punktwert, entsprechend einer Malignomwahrscheinlichkeit. Damit lässt sich der KS direkt einer konkreten BI-RADS-Kategorie zuordnen. Individuelle Managemententscheidungen sollten dabei auch den klinischen Kontext berücksichtigen, was anhand von typischen Beispielen dargestellt wird.

Kernaussagen: 

• Der evidenzbasierte KS zur objektiven Differenzierung von gutartigen und bösartigen Brustläsionen basiert auf T2w- und dynamisch kontrastangehobenen T1w-Informationen und ist weitgehend unabhängig von der verwendeten Untersuchungstechnik.

• Der KS basiert auf den etablierten diagnostischen Kriterien des MRT BI-RADS-Lexikons. Wir setzen einen Fokus auf die Definition einer Basiskategorie, welche bei Unklarheiten greift.

• Der KS reflektiert steigende Malignomwahrscheinlichkeiten und kann in Zusammenschau mit dem klinischen Kontext als Entscheidungshilfe das Patientenmanagement unterstützen.

Zitierweise

• Baltzer PA, Krug KB, Dietzel M. Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. Fortschr Röntgenstr 2022; 194: 1216–1228

Abstract

Background Breast MRI is the most sensitive method for the detection of breast cancer and is an integral part of modern breast imaging. On the other hand, interpretation of breast MRI exams is considered challenging due to the complexity of the available information. Clinical decision rules that combine diagnostic criteria in an algorithm can help the radiologist to read breast MRI by supporting objective and largely experience-independent diagnosis.

Method Narrative review. In this article, the Kaiser Score (KS) as a clinical decision rule for breast MRI is introduced, its diagnostic criteria are defined, and strategies for clinical decision making using the KS are explained and discussed.

Results The KS is based on machine learning and has been independently validated by international research. It is largely independent of the examination technique that is used. It allows objective differentiation between benign and malignant contrast-enhancing breast MRI findings using diagnostic BI-RADS criteria taken from T2w and dynamic contrast-enhanced T1w images. A flowchart guides the reader in up to three steps to determine a score corresponding to the probability of malignancy that can be used to assign a BI-RADS category. Individual decision making takes the clinical context into account and is illustrated by typical scenarios.

Key Points:

• The KS as an evidence-based decision rule to objectively distinguish benign from malignant breast lesions is based on information contained in T2w und dynamic contrast-enhanced T1w sequences and is largely independent of specific examination protocols.

• The KS diagnostic criteria are in line with the MRI BI-RADS lexicon. We focused on defining a default category to be applied in the case of equivocal imaging criteria.

• The KS reflects increasing probabilities of malignancy and, together with the clinical context, assists individual decision making.

Citation Format

• Baltzer PA, Krug KB, Dietzel M. Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. Fortschr Röntgenstr 2022; 194: 1216–1228

Key words

breast - decision analysis - biopsy - MR imaging

Publication History

Article published online:
28 February 2023

• References

• 1 Kaiser WA. MR Mammography (MRM). 1st ed.. Berlin Heidelberg: Springer; 1994
• 2 Kaiser WA. False-positive results in dynamic MR mammography. Causes, frequency, and methods to avoid. Magn Reson Imaging Clin N Am 1994; 2: 539-555
• 3 Sardanelli F, Boetes C, Borisch B. et al. Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. Eur J Cancer 2010; 46: 1296-1316 DOI: 10.1016/j.ejca.2010.02.015.
• 4 Mann RM, Balleyguier C, Baltzer PA. et al. European Society of Breast Imaging (EUSOBI), with language review by Europa Donna – The European Breast Cancer Coalition, Breast MRI: EUSOBI recommendations for women’s information. Eur Radiol 2015; 25: 3669-3678 DOI: 10.1007/s00330-015-3807-z.
• 5 Bakker MF, de Lange SV, Pijnappel RM. et al. DENSE Trial Study Group, Supplemental MRI Screening for Women with Extremely Dense Breast Tissue. N Engl J Med 2019; 381: 2091-2102 DOI: 10.1056/NEJMoa1903986.
• 6 Kaiser CG, Dietzel M, Vag T. et al. Cost-effectiveness of MR-mammography vs. conventional mammography in screening patients at intermediate risk of breast cancer – A model-based economic evaluation. Eur J Radiol 2020; 109355 DOI: 10.1016/j.ejrad.2020.109355.
• 7 Tollens F, Baltzer PAT, Dietzel M. et al. Cost-Effectiveness of Digital Breast Tomosynthesis vs. Abbreviated Breast MRI for Screening Women with Intermediate Risk of Breast Cancer-How Low-Cost Must MRI Be?. Cancers (Basel) 2021; 13 DOI: 10.3390/cancers13061241.
• 8 Baltzer PAT. Supplemental screening using breast MRI in women with mammographically dense breasts. Eur J Radiol 2021; 136: 109513 DOI: 10.1016/j.ejrad.2020.109513.
• 9 Clauser P, Mann R, Athanasiou A. et al. A survey by the European Society of Breast Imaging on the utilisation of breast MRI in clinical practice. Eur Radiol 2018; 28: 1909-1918 DOI: 10.1007/s00330-017-5121-4.
• 10 Debald M, Abramian A, Nemes L. et al. Who may benefit from preoperative breast MRI? A single-center analysis of 1102 consecutive patients with primary breast cancer. Breast Cancer Res Treat 2015; 153: 531-537 DOI: 10.1007/s10549-015-3556-3.
• 11 Fueger BJ, Clauser P, Kapetas P. et al. Can supplementary contrast-enhanced MRI of the breast avoid needle biopsies in suspicious microcalcifications seen on mammography? A systematic review and meta-analysis. Breast 2021; 56: 53-60 DOI: 10.1016/j.breast.2021.02.002.
• 12 Gommers JJ, Voogd AC, Broeders MJ. et al. Breast magnetic resonance imaging as a problem solving tool in women recalled at biennial screening mammography: A population-based study in the Netherlands. Breast 2021; 60: 279-286 DOI: 10.1016/j.breast.2021.11.014.
• 13 Bennani-Baiti B, Bennani-Baiti N, Baltzer PA. Diagnostic Performance of Breast Magnetic Resonance Imaging in Non-Calcified Equivocal Breast Findings: Results from a Systematic Review and Meta-Analysis. PLoS ONE 2016; 11: e0160346 DOI: 10.1371/journal.pone.0160346.
• 14 Spick C, Szolar DHM, Preidler KW. et al. Breast MRI used as a problem-solving tool reliably excludes malignancy. Eur J Radiol 2015; 84: 61-64 DOI: 10.1016/j.ejrad.2014.10.005.
• 15 Comstock CE, Gatsonis C, Newstead GM. et al. Comparison of Abbreviated Breast MRI vs Digital Breast Tomosynthesis for Breast Cancer Detection Among Women With Dense Breasts Undergoing Screening. JAMA 2020; 323: 746-756 DOI: 10.1001/jama.2020.0572.
• 16 Geuzinge HA. et al. Cost-effectiveness of MRI screening for women with extremely dense breast tissue. Journal of the National Cancer Institute 2021; 113 (11) 1476-1483 DOI: 10.1093/jnci/djab119.
• 17 Ikeda DM, Hylton NM, Kinkel K. et al. Development, standardization, and testing of a lexicon for reporting contrast-enhanced breast magnetic resonance imaging studies. J Magn Reson Imaging 2001; 13: 889-895
• 18 Marino MA, Clauser P, Woitek R. et al. A simple scoring system for breast MRI interpretation: does it compensate for reader experience?. Eur Radiol 2016; 26: 2529-2537 DOI: 10.1007/s00330-015-4075-7.
• 19 Morris EA, Comstock C, Lee C. et al. ACR BI-RADS^® Magnetic Resonance Imaging, in: ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. 5th ed.. Reston, VA: American College of Radiology; 2013
• 20 Weinreb JC, Barentsz JO, Choyke PL. et al. PI-RADS Prostate Imaging – Reporting and Data System: 2015, Version 2. Eur. Urol 2016; 69: 16-40 DOI: 10.1016/j.eururo.2015.08.052.
• 21 Stoutjesdijk MJ, Fütterer JJ, Boetes C. et al. Variability in the description of morphologic and contrast enhancement characteristics of breast lesions on magnetic resonance imaging. Invest Radiol 2005; 40: 355-362
• 22 Baltzer PAT, Kaiser WA, Dietzel M. Lesion type and reader experience affect the diagnostic accuracy of breast MRI: A multiple reader ROC study. Eur J Radiol 2015; 84: 86-91 DOI: 10.1016/j.ejrad.2014.10.023.
• 23 Jansen SA, Shimauchi A, Zak L. et al. The diverse pathology and kinetics of mass, nonmass, and focus enhancement on MR imaging of the breast. J Magn Reson Imaging 2011; 33: 1382-1389 DOI: 10.1002/jmri.22567.
• 24 Baltzer PAT, Dietzel M, Kaiser WA. A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. Eur Radiol 2013; 23: 2051-2060 DOI: 10.1007/s00330-013-2804-3.
• 25 Marino MA, Helbich T, Baltzer P. et al. Multiparametric MRI of the breast: A review. J Magn Reson Imaging 2018; 47: 301-315 DOI: 10.1002/jmri.25790.
• 26 Dietzel M, Baltzer PAT. How to use the Kaiser score as a clinical decision rule for diagnosis in multiparametric breast MRI: a pictorial essay. Insights Imaging 2018; 9: 325-335 DOI: 10.1007/s13244-018-0611-8.
• 27 Dietzel M, Wenkel E, Hammon M. et al. Does higher field strength translate into better diagnostic accuracy? A prospective comparison of breast MRI at 3 and 1.5 Tesla. Eur J Radiol 2019; 114: 51-56 DOI: 10.1016/j.ejrad.2019.02.033..
• 28 Clauser P, Pinker K, Helbich TH. et al. Fat saturation in dynamic breast MRI at 3 Tesla: is the Dixon technique superior to spectral fat saturation? A visual grading characteristics study. Eur Radiol 2014; 24: 2213-2219 DOI: 10.1007/s00330-014-3189-7.
• 29 Folkman J. The role of angiogenesis in tumor growth. Semin Cancer Biol 1992; 3: 65-71
• 30 Kuhl CK, Schrading S, Bieling HB. et al. MRI for diagnosis of pure ductal carcinoma in situ: a prospective observational study. Lancet 2007; 370: 485-492 DOI: 10.1016/S0140-6736(07)61232-X.
• 31 Vag T, Baltzer PAT, Renz DM. et al. Diagnosis of ductal carcinoma in situ using contrast-enhanced magnetic resonance mammography compared with conventional mammography. Clin Imaging 2008; 32: 438-442 DOI: 10.1016/j.clinimag.2008.05.005.
• 32 Baltzer PAT, Benndorf M, Gajda M. et al. An exception to tumour neoangiogenesis in a malignant breast-lesion. Breast J 2010; 16: 197-198 DOI: 10.1111/j.1524-4741.2009.00875.x.
• 33 Bennani-Baiti B, Baltzer PA. MR Imaging for Diagnosis of Malignancy in Mammographic Microcalcifications: A Systematic Review and Meta-Analysis. Radiology 2017; 283: 692-701 DOI: 10.1148/radiol.2016161106.
• 34 Kuhl CK, Keulers A, Strobel K. et al. Not all false positive diagnoses are equal: On the prognostic implications of false-positive diagnoses made in breast MRI versus in mammography/digital tomosynthesis screening. Breast Cancer Res 2018; 20: 13 DOI: 10.1186/s13058-018-0937-7.
• 35 Baltzer PAT, Freiberg C, Beger S. et al. Clinical MR-mammography: are computer-assisted methods superior to visual or manual measurements for curve type analysis? A systematic approach. Acad Radiol 2009; 16: 1070-1076 DOI: 10.1016/j.acra.2009.03.017.
• 36 Baltzer PAT, Dietzel M, Camara O. et al. A systematic comparison of two pulse sequences for edema assessment in MR-mammography. Eur J Radiol 2012; 81: 1500-1503 DOI: 10.1016/j.ejrad.2011.03.001.
• 37 Baltzer P, Mann RM, Iima M. et al. EUSOBI international Breast Diffusion-Weighted Imaging working group, Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group. Eur Radiol 2020; 30: 1436-1450 DOI: 10.1007/s00330-019-06510-3.
• 38 Baltzer PAT, Kapetas P, Sodano C. et al. Kontrastmittelfreie Mamma-MRT. Radiologe 2019; DOI: 10.1007/s00117-019-0524-7.
• 39 Dietzel M, Krug B, Clauser P. et al. A Multicentric Comparison of Apparent Diffusion Coefficient Mapping and the Kaiser Score in the Assessment of Breast Lesions. Invest Radiol 2021; 56: 274-282 DOI: 10.1097/RLI.0000000000000739.
• 40 Woitek R, Spick C, Schernthaner M. et al. A simple classification system (the Tree flowchart) for breast MRI can reduce the number of unnecessary biopsies in MRI-only lesions. Eur Radiol 2017; 27: 3799-3809 DOI: 10.1007/s00330-017-4755-6.
• 41 Wengert GJ, Pipan F, Almohanna J. et al. Impact of the Kaiser score on clinical decision-making in BI-RADS 4 mammographic calcifications examined with breast MRI. Eur Radiol 2020; 30: 1451-1459 DOI: 10.1007/s00330-019-06444-w.
• 42 Milos RI, Pipan F, Kalovidouri A. et al. The Kaiser score reliably excludes malignancy in benign contrast-enhancing lesions classified as BI-RADS 4 on breast MRI high-risk screening exams. Eur Radiol 2020; 30: 6052-6061 DOI: 10.1007/s00330-020-06945-z.
• 43 Jajodia A, Sindhwani G, Pasricha S. et al. Application of the Kaiser score to increase diagnostic accuracy in equivocal lesions on diagnostic mammograms referred for MR mammography. European Journal of Radiology 2020; 109413 DOI: 10.1016/j.ejrad.2020.109413.
• 44 Zhang B, Feng L, Wang L. et al. [Kaiser score for diagnosis of breast lesions presenting as non-mass enhancement on MRI]. Nan Fang Yi Ke Da Xue Xue Bao 2020; 40: 562-566 DOI: 10.12122/j.issn.1673-4254.2020.04.18.
• 45 Baum F, Fischer U, Vosshenrich R. et al. Classification of hypervascularized lesions in CE MR imaging of the breast. Eur Radiol 2002; 12: 1087-1092 DOI: 10.1007/s00330-001-1213-1.
• 46 Nunes LW, Schnall MD, Orel SG. et al. Breast MR imaging: interpretation model. Radiology 1997; 202: 833-841
• 47 Demartini WB, Kurland BF, Gutierrez RL. et al. Probability of malignancy for lesions detected on breast MRI: a predictive model incorporating BI-RADS imaging features and patient characteristics. Eur Radiol 2011; 21: 1609-1617 DOI: 10.1007/s00330-011-2094-6.
• 48 Schnall MD, Blume J, Bluemke DA. et al. Diagnostic architectural and dynamic features at breast MR imaging: multicenter study. Radiology 2006; 238: 42-53 DOI: 10.1148/radiol.2381042117.
• 49 Ellmann S, Wenkel E, Dietzel M. et al. Bäuerle, Implementation of machine learning into clinical breast MRI: Potenzial for objective and accurate decision-making in suspicious breast masses. PLOS ONE 2020; 15: e0228446 DOI: 10.1371/journal.pone.0228446.
• 50 Istomin A, Masarwah A, Vanninen R. et al. Diagnostic performance of the Kaiser score for characterizing lesions on breast MRI with comparison to a multiparametric classification system. European Journal of Radiology 2021; 138: 109659 DOI: 10.1016/j.ejrad.2021.109659.
• 51 Baltzer P, Sardanelli F. The Mantra about Low Specificity of Breast MRI, in: Breast MRI for High-Risk Screening. Springer 2020; 11-21 DOI: 10.1007/978-3-030-41207-4_2.
• 52 Vag T, Baltzer PAT, Dietzel M. et al. Kinetic analysis of lesions without mass effect on breast MRI using manual and computer-assisted methods. Eur Radiol 2011; 21: 893-898 DOI: 10.1007/s00330-010-2001-6.
• 53 Grippo C, Jagmohan P, Helbich TH. et al. Correct determination of the enhancement curve is critical to ensure accurate diagnosis using the Kaiser score as a clinical decision rule for breast MRI. Eur J Radiol 2021; 138: 109630 DOI: 10.1016/j.ejrad.2021.109630.
• 54 Baltzer PAT, Yang F, Dietzel M. et al. Sensitivity and specificity of unilateral edema on T2w-TSE sequences in MR-Mammography considering 974 histologically verified lesions. Breast J 2010; 16: 233-239 DOI: 10.1111/j.1524-4741.2010.00915.x.
• 55 Cheon H, Kim HJ, Kim TH. et al. Invasive Breast Cancer: Prognostic Value of Peritumoral Edema Identified at Preoperative MR Imaging. Radiology 2018; 287: 68-75 DOI: 10.1148/radiol.2017171157.
• 56 Kaiser CG, Herold M, Krammer J. et al. Prognostic Value of “Prepectoral Edema” in MR-mammography. Anticancer Res 2017; 37: 1989-1995 DOI: 10.21873/anticanres.11542.
• 57 Kaiser WA. Signs in MR-Mammography. Berlin Heidelberg: Springer; 2008. http://www.springer.com/us/book/9783540732921 (accessed March 17, 2015).