Potential of MR Mammography to Predict Tumor Grading of Invasive Breast Cancer

Zusammenfassung

Ziel: Der Differenzierungsgrad zählt zu den am häufigsten ermittelten prognostischen Faktoren des invasiven Mammakarzinoms. Diese Studie wurde durchgeführt, um das Potenzial der Magnet-Resonanz Mammografie (MRM) zur nicht invasiven Abschätzung des Differenzierungsgrads zu ermitteln. Material und Methoden: 399 invasive Mammakarzinome wurden in die Studie eingeschlossen (einheitliches klinisches Messprotokoll; Genehmigung durch Ethikkommission) und von 2 geblindeten, erfahrenen Radiologen (> 500 MRM) prospektiv im Konsensus evaluiert. Zur Gewebsdifferenzierung wurden detaillierte MRM-Deskriptoren (n = 18) herangezogen. Basierend auf diesen Analysen wurde anschließend mittels uni- und multivariater Statistik das Potenzial der MRM zur Abschätzung des Differenzierungsgrads ermittelt (X2-Tests; binär logistische Regression; area under the ROC-curve [AUC]). Ergebnisse: 8 der insgesamt 18 MRM-Deskriptoren zeigten eine Assoziation mit dem Differenzierungsgrad, z. B. „interne Struktur”, „Ödem” (p < 0,001), „Cutisverdickung” und „Zerstörung des Mamillensaums” (p < 0,05). Die multivariate Analyse ermittelte ein signifikantes Potenzial zur Prädiktion des Differenzierungsgrads mittels MRM (p < 0,001). Hierbei konnten insbesondere prognostisch günstige, „gut” differenzierte Karzinome mit hoher Treffsicherheit identifiziert werden (AUC = 0,930). Schlussfolgerung: Die Abschätzung des Differenzierungsgrads invasiver Mammakarzinoms ist anhand typischer MRM-Charakteristika in einem standardisierten klinischen Messprotokoll möglich. Da der Differenzierungsgrad m. E. als Surrogat für das Gesamtüberleben gilt, kann die MRM somit neben differenzialdiagnostischen auch initiale prognostische Informationen liefern.

Abstract

Purpose: Tumor grading (TG) is one of the most widely used prognostic factors in the case of breast cancer. This study aims to identify the potential of magnetic resonance mammography (MRM) to non-invasively assess TG. Materials and Methods: 399 invasive breast cancers were included (IRB approval; standardized clinical MRM protocols). All breast cancers were prospectively evaluated by two experienced (> 500 MRM) and blinded radiologists in consensus. In every cancer a set of 18 previously published MRM descriptors was assessed. These were assessed by univariate and multivariate analysis to identify the potential of MRM to predict TG (X2 statistics; binary logistic regression; area under the ROC curve [AUC]). Results: 8 of 18 MRM descriptors were associated with TG, e. g. internal structure, edema (p < 0.001), as well as skin thickening and destruction of the nipple line (p < 0.05). MRM was feasible to predict TG by multivariate analysis (p < 0.001). The highest potential could be identified to predict well differentiated breast cancers with good prognosis (AUC = 0.930). Conclusion: MR mammography was able to non-invasively assess tumor grading in a standard protocol. Since tumor grading is a surrogate for overall survival, these results provide further evidence to the clinical application of MR mammography as a noninvasive prognostic tool.

breast - mammography - MR imaging - contrast agents - decision analysis - gadolinium

References

• 1 Houssami N, Ciatto S, Macaskill P et al. 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. 2008;  26 3248-3258
• 2 Jemal A, Siegel R, Ward E et al. Cancer statistics, 2009.  CA Cancer J Clin. 2009;  59 225-249
• 3 Soerjomataram I, Louwman M W, Ribot J G et al. An overview of prognostic factors for long-term survivors of breast cancer.  Breast Cancer Res Treat. 2008;  107 309-330
• 4 Mortellaro V E, Marshall J, Singer L et al. Magnetic resonance imaging for axillary staging in patients with breast cancer.  J Magn Reson Imaging. 2009;  30 309-312
• 5 Lee S H, Cho N, Kim S J et al. Correlation between high resolution dynamic MR features and prognostic factors in breast cancer.  Korean J Radiol. 2008;  9 10-18
• 6 Montemurro F, Martincich L, Sarotto I et al. Relationship between DCE-MRI morphological and functional features and histopathological characteristics of breast cancer.  Eur Radiol. 2007;  17 1490-1497
• 7 Teifke A, Behr O, Schmidt M et al. Dynamic MR imaging of breast lesions: correlation with microvessel distribution pattern and histologic characteristics of prognosis.  Radiology. 2006;  239 351-360
• 8 Tuncbilek N, Karakas H M, Okten O O. Dynamic magnetic resonance imaging in determining histopathological prognostic factors of invasive breast cancers.  Eur J Radiol. 2005;  53 199-205
• 9 Szabo B K, Aspelin P, Kristoffersen Wiberg M et al. Invasive breast cancer: correlation of dynamic MR features with prognostic factors.  Eur Radiol. 2003;  13 2425-2435
• 10 Mussurakis S, Buckley D L, Horsman A. Dynamic MR imaging of invasive breast cancer: correlation with tumour grade and other histological factors.  Br J Radiol. 1997;  70 446-451
• 11 Fischer U, Kopka L, Brinck U et al. Prognostic value of contrast-enhanced MR mammography in patients with breast cancer.  Eur Radiol. 1997;  7 1002-1005
• 12 Dietzel M, Baltzer P A, Vag T et al. Magnetic resonance mammography in small vs. advanced breast lesions – systematic comparison reveals significant impact of lesion size on diagnostic accuracy in 936 histologically verified breast lesions.  Fortschr Röntgenstr. 2011;  183 126-135
• 13 Harris G C, Denley H E, Pinder S E et al. Correlation of histologic prognostic factors in core biopsies and therapeutic excisions of invasive breast carcinoma.  Am J Surg Pathol. 2003;  27 11-15
• 14 Elston C W, Ellis I O. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up.  Histopathology. 1991;  19 403-410
• 15 Edge S, Byrd D, Carducci M et al eds.. TNM Classification of Malignant Tumours. 7 ed. New York: Springer; 2009
• 16 Fischer U, Kopka L, Grabbe E. Breast carcinoma: effect of preoperative contrast-enhanced MR imaging on the therapeutic approach.  Radiology. 1999;  213 881-888
• 17 Kaiser W A. Signs in MR-Mammography. 1st ed. Berlin, Heidelberg, New York: Springer; 2007
• 18 American College of Radiology (ACR) .ACR BI-RADS® – MRI. In, Breast imaging reporting and data system atlas (BI-RADS atlas). 4th ed. Reston, VA: American College of Radiology; 2003
• 19 Siegmann K C, Moron H U, Baur A et al. Diagnostische Wertigkeit des Göttinger Scores zur Malignitätsvorhersage von ausschließlich in der MRT darstellbaren Mammaläsionen.  Fortschr Röntgenstr. 2009;  181 556-563
• 20 Fischer D R, Baltzer P, Malich A et al. Is the ”blooming sign” a promising additional tool to determine malignancy in MR mammography?.  Eur Radiol. 2004;  14 394-401
• 21 Baltzer P A, Yang F, Dietzel M et al. Sensitivity and specificity of unilateral edema on T 2w-TSE sequences in MR-Mammography considering 974 histologically verified lesions.  Breast J. 2010;  16 233-239
• 22 Dietzel M, Baltzer P A, Vag T et al. The hook sign for differential diagnosis of malignant from benign lesions in magnetic resonance mammography: experience in a study of 1084 histologically verified cases.  Acta Radiol. 2010;  51 137-143
• 23 Malich A, Fischer D R, Wurdinger S et al. Potential MRI interpretation model: differentiation of benign from malignant breast masses.  Am J Roentgenol. 2005;  185 964-970
• 24 Dietzel M, Baltzer P A, Vag T et al. The necrosis sign in magnetic resonance-mammography: diagnostic accuracy in 1,084 histologically verified breast lesions.  Breast J. 2010;  16 603-608
• 25 Dietzel M, Baltzer P A, Vag T et al. The adjacent vessel sign on breast MRI: new data and a subgroup analysis for 1,084 histologically verified cases.  Korean J Radiol. 2010;  11 178-186
• 26 Bland M. An Introduction to Medical Statistics. 3rd ed. Oxford: Oxford University Press; 2000
• 27 Ignatiadis M, Sotiriou C. Understanding the molecular basis of histologic grade.  Pathobiology. 2008;  75 104-111
• 28 Longacre T A, Ennis M, Quenneville L A et al. Interobserver agreement and reproducibility in classification of invasive breast carcinoma: an NCI breast cancer family registry study.  Mod Pathol. 2006;  19 195-207
• 29 Michaelson J S, Silverstein M, Wyatt J et al. Predicting the survival of patients with breast carcinoma using tumor size.  Cancer. 2002;  95 713-723
• 30 Carlomagno C, Perrone F, Lauria R et al. Prognostic significance of necrosis, elastosis, fibrosis and inflammatory cell reaction in operable breast cancer.  Oncology. 1995;  52 272-277
• 31 Jimenez R E, Wallis T, Visscher D W. Centrally necrotizing carcinomas of the breast: a distinct histologic subtype with aggressive clinical behavior.  Am J Surg Pathol. 2001;  25 331-337
• 32 Cetintas S K, Kurt M, Ozkan L et al. Factors influencing axillary node metastasis in breast cancer.  Tumori. 2006;  92 416-422
• 33 Rakha E A, El-Sayed M E, Lee A H et al. Prognostic significance of Nottingham histologic grade in invasive breast carcinoma.  J Clin Oncol. 2008;  26 3153-3158
• 34 Baltzer P AT, Renz D M, Herrmann K H et al. Diffusion-weighted imaging (DWI) in MR mammography (MRM): clinical comparison of echo planar imaging (EPI) and half-Fourier single-shot turbo spin echo (HASTE) diffusion techniques.  Eur Radiol. 2009;  19 1612-1620
• 35 Mountford C, Ramadan S, Stanwell P et al. Proton MRS of the breast in the clinical setting.  NMR Biomed. 2009;  22 54-64
• 36 Baltzer P A, Vag T, Dietzel M et al. Computer-aided interpretation of dynamic magnetic resonance imaging reflects histopathology of invasive breast cancer.  Eur Radiol. 2010;  20 1563-1571