Can imaging kinetic parameters of dynamic contrast-enhanced magnetic resonance imaging be valuable in predicting clinicopathological prognostic factors of invasive breast cancer?

Abstract

Background

Intrinsic molecular profiling of breast cancer provides clinically relevant information that helps tailor therapy directed to the specific tumor subtype. We hypothesized that dynamic contrast-enhanced MRI (DCE-MRI) derived quantitative kinetic parameters (CD-QKPs) may help predict molecular tumor profiles non-invasively.

Purpose

To determine the association between DCE-MRI (CD-QKPs) and breast cancer clinicopathological prognostic factors.

Material and Methods

Clinicopathological factors in consecutive women with biopsy-confirmed invasive breast cancer who underwent breast DCE-MRI were retrospectively reviewed. Analysis of variance was used to examine associations between prognostic factors and CD-QKPs. Fisher’s exact test was used to investigate the relationship between kinetic curve type and prognostic factors.

Results

A total of 198 women with invasive breast cancer were included. High-grade and HER2+ tumors were more likely to have a washout type curve while luminal A tumors were less likely. High-grade was significantly associated with increased peak enhancement (PE; P = 0.01), enhancement maximum slope (MS; P = 0.03), and mean enhancement (ME, P = 0.03), while high clinical lymph node stage (cN3) was significantly associated with increased MS and time to peak (tP; P = 0.01). HER2+ tumors were associated with a higher PE (P = 0.03) and ME (P = 0.06) than HER2- counterparts, and ER-/HER2+ tumors showed higher PE and ME values than ER+/HER2- tumors (P = 0.06).

Conclusion

DCE-MRI time-intensity CD-QKPs are associated with high tumor grade, advanced nodal stage, and HER2+ status, indicating their utility as imaging biomarkers.

Keywords

Breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)time-intensity curve-derived quantitative kinetic parameter tumor prognostic factors immunohistochemical prognostic factors molecular profile

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References

American Cancer Society. What are the key statistics about breast cancer? Atlanta, GA: ACS. Available at: http://www.cancer.org/cancer/breastcancer/detailedguide/breast-cancer-key-statistics (accessed 20 March 2013).

Sotiriou

Pusztai

. Gene-expression signatures in breast cancer. N Engl J Med 2009; 360: 790–800.

Braunstein

Niemierko

Shenouda

et al.

Outcome following local-regional recurrence in women with early-stage breast cancer: impact of biologic subtype. Breast J 2015; 21: 161–167.

Engstrøm

Opdahl

Hagen

et al.

Molecular subtypes, histopathological grade and survival in a historic cohort of breast cancer patients. Breast Cancer Res Treat 2013; 140: 463–473.

Wang

Yin

et al.

A retrospective study of breast cancer subtypes: the risk of relapse and the relations with treatments. Breast Cancer Res Treat 2011; 130: 489–498.

Peppercorn

Perou

Carey

. Molecular subtypes in breast cancer evaluation and management: divide and conquer. Cancer Invest 2008; 26: 1–10.

Turkoz

Solak

Petekkaya

et al.

Association between common risk factors and molecular subtypes in breast cancer patients. Breast 2003; 22: 344–350.

Kao

Chang

Hsu

et al.

Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization. BMC Cancer 2011; 11: 143–143.

Schnitt

. Major molecular subtypes of breast cancer determined by gene expression profiling. Modern Pathology 2010; 23: S60–S64.

10.

Kim

Kang

. Characteristic features and usefulness of MRI in breast cancer in patients under 40 years old: correlations with conventional imaging and prognostic factors. Breast Cancer 2014; 21: 302–315.

11.

Boisserie-Lacroix

Hurtevent-Labrot

Ferron

et al.

Correlation between imaging and molecular classification of breast cancers. Diagn Interv Imaging 2013; 94: 1069–1080.

12.

Dogan

Gonzalez-Angulo

Gilcrease

et al.

Multimodality imaging of triple receptor-negative tumors with mammography, ultrasound, and MRI. Am J Roentgenol 2010; 194: 1160–1166.

13.

Bae

Seo

Kim

et al.

Quantitative MRI morphology of invasive breast cancer: correlation with immunohistochemical biomarkers and subtypes. Acta Radiol 2015; 56: 269–275.

14.

Tardivon

Athanasiou

Thibault

et al.

Breast imaging and reporting data system (BI-RADS): magnetic resonance imaging. Eur J Radiol 2007; 61: 212–215.

15.

Tardivon

Athanasiou

Thibault

et al.

Breast imaging and reporting data system (BI-RADS) magnetic resonance imaging illustrated cases. Eur J Radiol 2007; 61: 216–223.

16.

Lee

Cho

Kim

et al.

Correlation between high resolution dynamic MR features and prognostic factors in breast cancer. Korean J Radiol 2008; 9: 10–18.

17.

Szabó

Aspelin

KristoffersenWiberg

et al.

Invasive breast cancer: correlation of dynamic MR features with prognostic factors. Eur Radiol 2003; 13: 2425–2435.

18.

Fernández-Guinea

Andicoechea

González

et al.

Relationship between morphological features and kinetic patterns of enhancement of the dynamic breast magnetic resonance imaging and clinico-pathological and biological factors in invasive breast cancer. BMC Cancer 2010; 10: 8–8.

19.

Morris

Comstock

Lee

et al.

ACR BI-RADS® magnetic resonance imaging. In: ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System, Reston, VA: American College of Radiology, 2010.

20.

Pickles

Lowry

Manton

et al.

Role of dynamic contrast enhanced MRI in monitoring early response of locally advanced breast cancer to neoadjuvant chemotherapy. Breast Cancer Res Treat 2005; 91: 1–10.

21.

Goldhirsch

Wood

Coates

et al.

Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer. Ann Oncol 2011; 22: 1736–1747.

22.

Polley

Leung

McShane

et al.

An international Ki67 reproducibility study. J Natl Cancer Inst 2013; 105: 1897–1906.

23.

Brenton

Carey

Ahmed

et al.

Molecular classification and molecular forecasting of breast cancer: ready for clinical application?

J Clin Oncol 2005; 23: 7350–7360.

24.

Perou

Borresen-Dale

. Systems biology and genomics of breast cancer. Cold Spring Harb Perspect Biol 2011; 3: a003293–a003293.

25.

Elston

. Classification and grading of invasive breast carcinoma. Verh Dtsch Ges Pathol 2005; 89: 35–44.

26.

Millet

Curros-Doyon

Molinari

et al.

Invasive breast carcinoma: influence of prognosis and patient-related factors on kinetic MR imaging characteristics. Radiology 2014; 270: 57–66.

27.

Kim

et al.

Enhancement parameters on dynamic contrast enhanced breast MRI: do they correlate with prognostic factors and subtypes of breast cancers?

Magn Reson Imaging 2015; 33: 72–80.

28.

Lehman

Gatsonis

Kuhl

et al.

MRI evaluateon of the contralateral breast in women with recently diagnosed breast cancer. N Engl J Med 2007; 356: 1295–1303.

29.

Hylton

Gatsonis

Rosen

et al.

Neoadjuvant chemotherapy for breast cancer: functional tumor volume by MR imaging predicts recurrence-free survival-results from the ACRIN 6657/CALGB 15007 I-SPY 1 TRIAL. Radiology 2016; 279: 44–55.

30.

De Felice

Musio

Bulzonetti

et al.

Relationship of clinical and pathologic nodal staging in locally advanced breast cancer: current controversies in daily practice?

J Clin Med Res 2014; 6: 409–413.

31.

Tuncbilek

Tokatli

Altaner

et al.

Prognostic value DCE-MRI parameters in predicting factor disease free survival and overall survival for breast cancer patients. Eur J Radiol 2012; 81: 863–867.

32.

Yamaguchi

Abe

Newstead

et al.

Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer. Breast Cancer 2014; 22: 496–502.

33.

Schlossbauer

Sourbron

Scholz

et al.

Dynamic breast MRI in the course of neoadjuvant chemotherapy: standardized evaluation of tumor size and enhancement parameters in correlation to different histopathologic characteristics. Acad Radiol 2010; 17: 441–449.

34.

El Khouli

Macura

Kamel

et al.

3-T dynamic contrast-enhanced MRI of the breast: pharmacokinetic parameters versus conventional kinetic curve analysis. Am J Roentgenol 2011; 197: 1498–1505.

35.

Di Giovanni

Azlan

Ahearn

et al.

The accuracy of pharmacokinetic parameter measurement in DCE-MRI of the breast at 3 T. Phys Med Biol 2010; 55: 121–132.

36.

Koo

Cho

Song

et al.

Correlation of perfusion parameters on dynamic contrast-enhanced MRI with prognostic factors and subtypes of breast cancers. J Magn Reson Imaging 2012; 36: 145–151.

37.

Ikeda

Hylton

Kuhl

et al.

BI-RADS: magnetic resonance imaging. In: D’Orsi

Mendelson

Ikeda

et al. (eds). Breast Imaging Reporting and Data System. ACR BI-RADS—Breast Imaging Atlas, 2nd ed. Reston, VA: American College of Radiology, 2013.

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