Integrated 18F-FDG PET/MRI in breast cancer: early prediction of response to neoadjuvant chemotherapy

  • Nariya Cho
  • Seock-Ah Im
  • Gi Jeong Cheon
  • In-Ae Park
  • Kyung-Hun Lee
  • Tae-Yong Kim
  • Young Seon Kim
  • Bo Ra Kwon
  • Jung Min Lee
  • Hoon Young Suh
  • Koung Jin Suh
Original Article

Abstract

Purpose

To explore whether integrated 18F-FDG PET/MRI can be used to predict pathological response to neoadjuvant chemotherapy (NAC) in patients with breast cancer.

Methods

Between November 2014 and April 2016, 26 patients with breast cancer who had received NAC and subsequent surgery were prospectively enrolled. Each patient underwent 18F-FDG PET/MRI examination before and after the first cycle of NAC. Qualitative MRI parameters, including morphological descriptors and the presence of peritumoral oedema were assessed. Quantitatively, PET parameters, including maximum standardized uptake value, metabolic tumour volume and total lesion glycolysis (TLG), and MRI parameters, including washout proportion and signal enhancement ratio (SER), were measured. The performance of the imaging parameters singly and in combination in predicting a pathological incomplete response (non-pCR) was assessed.

Results

Of the 26 patients, 7 (26.9%) exhibited a pathological complete response (pCR), and 19 (73.1%) exhibited a non-pCR. No significant differences were found between the pCR and non-pCR groups in the qualitative MRI parameters. The mean percentage reductions in TLG30% on PET and SER on MRI were significantly greater in the pCR group than in the non-pCR group (TLG30% −64.8 ± 15.5% vs. −25.4 ± 48.7%, P = 0.005; SER −34.6 ± 19.7% vs. −8.7 ± 29.0%, P = 0.040). The area under the receiver operating characteristic curve for the percentage change in TLG30% (0.789, 95% CI 0.614 to 0.965) was similar to that for the percentage change in SER (0.789, 95% CI 0.552 to 1.000; P = 1.000).The specificity of TLG30% in predicting pCR) was 100% (7/7) and that of SER was 71.4% (5/7). The sensitivity of TLG30% in predicting non-pCR was 63.2% (12/19) and that of SER was 84.2% (16/19). When the combined TLG30% and SER criterion was applied, sensitivity was 100% (19/19), and specificity was 71.4% (5/7).

Conclusion

18F-FDG PET/MRI can be used to predict non-pCR after the first cycle of NAC in patients with breast cancer and has the potential to improve sensitivity by the addition of MRI parameters to the PET parameters.

Keywords

PET/MRI Breast cancer PET Response prediction Neoadjuvant chemotherapy 

Abbreviations

NAC

neoadjuvant chemotherapy

FDG PET

18F-fluoro-deoxy-glucose positron emission tomography

pCR

pathological complete response

non-pCR

pathological incomplete response

MRI

magnetic resonance imaging

SUV

standardized uptake value

MTV

metabolic tumour volume

TLG

total lesion glycolysis

LN

lymph node

SER

signal enhancement ratio

ER

oestrogen receptor

PR

progesterone receptor

HER

human epidermal growth factor receptor

RCB

residual cancer burden

ICC

intraclass correlation coefficient

Notes

Compliance with ethical standards

Conflicts of interest

None.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

References

  1. 1.
    Graham PJ, Brar MS, Foster T, et al. Neoadjuvant chemotherapy for breast cancer, is practice changing? A population-based review of current surgical trends. Ann Surg Oncol. 2015;22:3376–82.CrossRefPubMedGoogle Scholar
  2. 2.
    Rubovszky G, Horváth Z. Recent advances in the neoadjuvant treatment of breast cancer. J Breast Cancer. 2017;20:119–31.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    von Minckwitz G, Untch M, Blohmer JU, et al. Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J Clin Oncol. 2012;30:1796–804.CrossRefGoogle Scholar
  4. 4.
    von Minckwitz G, Blohmer JU, Costa SD, et al. Response-guided neoadjuvant chemotherapy for breast cancer. J Clin Oncol. 2013;31:3623–30.CrossRefGoogle Scholar
  5. 5.
    Tabchy A, Valero V, Vidaurre T, et al. Evaluation of a 30-gene paclitaxel, fluorouracil, doxorubicin, and cyclophosphamide chemotherapy response predictor in a multicenter randomized trial in breast cancer. Clin Cancer Res. 2010;16:5351–61.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Buchbender C, Heusner TA, Lauenstein TC, Bockisch A, Antoch G. Oncologic PET/MRI, part 1: tumors of the brain, head and neck, chest, abdomen, and pelvis. J Nucl Med. 2012;53:928–38.CrossRefPubMedGoogle Scholar
  7. 7.
    Rousseau C, Devillers A, Sagan C, et al. Monitoring of early response to neoadjuvant chemotherapy in stage II and III breast cancer by [18F]fluorodeoxyglucose positron emission tomography. J Clin Oncol. 2006;24:5366–72.CrossRefPubMedGoogle Scholar
  8. 8.
    Schwarz-Dose J, Untch M, Tiling R, et al. Monitoring primary systemic therapy of large and locally advanced breast cancer by using sequential positron emission tomography imaging with [18F]fluorodeoxyglucose. J Clin Oncol. 2009;27:535–41.CrossRefPubMedGoogle Scholar
  9. 9.
    Hylton NM, Blume JD, Bernreuter WK, et al. Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy – results from ACRIN 6657/I-SPY TRIAL. Radiology. 2012;263:663–72.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Ah-See ML, Makris A, Taylor NJ, et al. Early changes in functional dynamic magnetic resonance imaging predict for pathologic response to neoadjuvant chemotherapy in primary breast cancer. Clin Cancer Res. 2008;14:6580–9.CrossRefPubMedGoogle Scholar
  11. 11.
    Cho N, Im SA, Park IA, et al. Breast cancer: early prediction of response to neoadjuvant chemotherapy using parametric response maps for MR imaging. Radiology. 2014;272:385–96.CrossRefPubMedGoogle Scholar
  12. 12.
    Avril S, Muzic RF Jr, Plecha D, Traughber BJ, Vinayak S, Avril N. 18F-FDG PET/CT for monitoring of treatment response in breast cancer. J Nucl Med. 2016;57:34S–9S.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Cysouw MC, Kramer GM, Schoonmade LJ, et al. Impact of partial-volume correction in oncological PET studies: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging. 2017.  https://doi.org/10.1007/s00259-017-3775-4
  14. 14.
    Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50(Suppl 1):122S–50S.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Lopci E, Zucali PA, Ceresoli GL, et al. Quantitative analyses at baseline and interim PET evaluation for response assessment and outcome definition in patients with malignant pleural mesothelioma. Eur J Nucl Med Mol Imaging. 2015;42:667–75.CrossRefPubMedGoogle Scholar
  16. 16.
    Im HJ, Kim YK, Kim YI, Lee JJ, Lee WW, Kim SE. Usefulness of combined metabolic-volumetric indices of (18)F-FDG PET/CT for the early prediction of neoadjuvant chemotherapy outcomes in breast cancer. Nucl Med Mol Imaging. 2013;47:36–43.CrossRefPubMedGoogle Scholar
  17. 17.
    Morris EA, Comstock CE, Lee CH, et al. ACR BI-RADS® magnetic resonance imaging. In: ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. Reston: American College of Radiology; 2013.Google Scholar
  18. 18.
    Esserman L, Kaplan E, Partridge S, et al. MRI phenotype is associated with response to doxorubicin and cyclophosphamide neoadjuvant chemotherapy in stage III breast cancer. Ann Surg Oncol. 2001;8:549–59.CrossRefPubMedGoogle Scholar
  19. 19.
    Uematsu T. Focal breast edema associated with malignancy on T2-weighted images of breast MRI: peritumoral edema, prepectoral edema, and subcutaneous edema. Breast Cancer. 2015;22:66–70.CrossRefPubMedGoogle Scholar
  20. 20.
    Bae MS, Shin SU, Ryu HS, et al. Pretreatment MR imaging features of triple-negative breast cancer: association with response to neoadjuvant chemotherapy and recurrence-free survival. Radiology. 2016;281:392–400.CrossRefPubMedGoogle Scholar
  21. 21.
    Symmans WF, Peintinger F, Hatzis C, et al. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol. 2007;25:4414–22.CrossRefPubMedGoogle Scholar
  22. 22.
    Symmans WF, Wei C, Gould R, et al. Long-term prognostic risk after neoadjuvant chemotherapy associated with residual cancer burden and breast cancer subtype. J Clin Oncol. 2017;35:1049–60.CrossRefPubMedGoogle Scholar
  23. 23.
    Tateishi U, Miyake M, Nagaoka T, et al. Neoadjuvant chemotherapy in breast cancer: prediction of pathologic response with PET/CT and dynamic contrast-enhanced MR imaging – prospective assessment. Radiology. 2012;263:53–63.CrossRefPubMedGoogle Scholar
  24. 24.
    Pengel KE, Koolen BB, Loo CE, et al. Combined use of 18F-FDG PET/CT and MRI for response monitoring of breast cancer during neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging. 2014;41:1515–24.CrossRefPubMedGoogle Scholar
  25. 25.
    Lim I, Noh WC, Park J, et al. The combination of FDG PET and dynamic contrast-enhanced MRI improves the prediction of disease-free survival in patients with advanced breast cancer after the first cycle of neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging. 2014;41:1852–60.CrossRefPubMedGoogle Scholar
  26. 26.
    Liu Q, Wang C, Li P, Liu J, Huang G, Song S. The role of (18)F-FDG PET/CT and MRI in assessing pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: a systematic review and meta-analysis. Biomed Res Int. 2016;2016:3746232.PubMedPubMedCentralGoogle Scholar
  27. 27.
    Moy L, Noz ME, Maguire GQ Jr, et al. Role of fusion of prone FDG-PET and magnetic resonance imaging of the breasts in the evaluation of breast cancer. Breast J. 2010;16:369–76.PubMedGoogle Scholar
  28. 28.
    Groheux D, Sanna A, Majdoub M, et al. Baseline tumor 18F-FDG uptake and modifications after 2 cycles of neoadjuvant chemotherapy are prognostic of outcome in ER+/HER2- breast cancer. J Nucl Med. 2015;56:824–31.CrossRefPubMedGoogle Scholar
  29. 29.
    Specht JM, Kurland BF, Montgomery SK, et al. Tumor metabolism and blood flow as assessed by positron emission tomography varies by tumor subtype in locally advanced breast cancer. Clin Cancer Res. 2010;16:2803–10.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Mankoff DA, Dunnwald LK, Gralow JR, et al. Blood flow and metabolism in locally advanced breast cancer: relationship to response to therapy. J Nucl Med. 2002;43:500–9.PubMedGoogle Scholar
  31. 31.
    Dunnwald LK, Gralow JR, Ellis GK, et al. Tumor metabolism and blood flow changes by positron emission tomography: relation to survival in patients treated with neoadjuvant chemotherapy for locally advanced breast cancer. J Clin Oncol. 2008;26:4449–57.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Dunnwald LK, Doot RK, Specht JM, et al. PET tumor metabolism in locally advanced breast cancer patients undergoing neoadjuvant chemotherapy: value of static versus kinetic measures of fluorodeoxyglucose uptake. Clin Cancer Res. 2011;17:2400–9.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Hatt M, Groheux D, Martineau A, et al. Comparison between 18F-FDG PET image-derived indices for early prediction of response to neoadjuvant chemotherapy in breast cancer. J Nucl Med. 2013;54:341–9.CrossRefPubMedGoogle Scholar
  34. 34.
    Groheux D, Majdoub M, Sanna A, et al. Early metabolic response to neoadjuvant treatment: FDG PET/CT criteria according to breast cancer subtype. Radiology. 2015;277:358–71.CrossRefPubMedGoogle Scholar
  35. 35.
    Li KL, Henry RG, Wilmes LJ, et al. Kinetic assessment of breast tumors using high spatial resolution signal enhancement ratio (SER) imaging. Magn Reson Med. 2007;58:572–81.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Nariya Cho
    • 1
    • 2
    • 3
  • Seock-Ah Im
    • 4
    • 5
  • Gi Jeong Cheon
    • 5
    • 6
  • In-Ae Park
    • 7
  • Kyung-Hun Lee
    • 4
    • 5
  • Tae-Yong Kim
    • 4
    • 5
  • Young Seon Kim
    • 1
    • 8
  • Bo Ra Kwon
    • 1
  • Jung Min Lee
    • 6
  • Hoon Young Suh
    • 6
  • Koung Jin Suh
    • 4
  1. 1.Department of RadiologySeoul National University HospitalSeoulRepublic of Korea
  2. 2.Department of RadiologySeoul National University College of MedicineSeoulRepublic of Korea
  3. 3.Institute of Radiation MedicineSeoul National University Medical Research CenterSeoulRepublic of Korea
  4. 4.Department of Internal MedicineSeoul National University Hospital, Seoul National University College of MedicineSeoulRepublic of Korea
  5. 5.Cancer Research InstituteSeoul National UniversitySeoulRepublic of Korea
  6. 6.Department of Nuclear MedicineSeoul National University HospitalSeoulRepublic of Korea
  7. 7.Department of PathologySeoul National University Hospital, Seoul National University College of MedicineSeoulRepublic of Korea
  8. 8.Department of Radiology, College of MedicineYeungnam UniversityDaeguRepublic of Korea

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