European Radiology

, Volume 24, Issue 3, pp 610–618 | Cite as

18F-FDG uptake in breast cancer correlates with immunohistochemically defined subtypes

  • Hye Ryoung Koo
  • Jeong Seon Park
  • Keon Wook Kang
  • Nariya Cho
  • Jung Min Chang
  • Min Sun Bae
  • Won Hwa Kim
  • Su Hyun Lee
  • Mi Young Kim
  • Jin You Kim
  • Mirinae Seo
  • Woo Kyung MoonEmail author



To determine whether a correlation exists between maximum standardized uptake value (SUVmax) on 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and the subtypes of breast cancer.


This retrospective study involved 548 patients (mean age 51.6 years, range 21–81 years) with 552 index breast cancers (mean size 2.57 cm, range 1.0–14.5 cm). The correlation between 18F-FDG uptake in PET/CT, expressed as SUVmax, and immunohistochemically defined subtypes (luminal A, luminal B, human epidermal growth factor receptor 2 (HER2) positive and triple negative) was analyzed.


The mean SUVmax value of the 552 tumours was 6.07 ± 4.63 (range 0.9–32.8). The subtypes of the 552 tumours were 334 (60 %) luminal A, 66 (12 %) luminal B, 60 (11 %) HER2 positive and 92 (17 %) triple negative, for which the mean SUVmax values were 4.69 ± 3.45, 6.51 ± 4.18, 7.44 ± 4.73 and 9.83 ± 6.03, respectively. In a multivariate regression analysis, triple-negative and HER2-positive tumours had 1.67-fold (P < 0.001) and 1.27-fold (P = 0.009) higher SUVmax values, respectively, than luminal A tumours after adjustment for invasive tumour size, lymph node involvement status and histologic grade.


FDG uptake was independently associated with subtypes of invasive breast cancer. Triple-negative and HER2-positive breast cancers showed higher SUVmax values than luminal A tumours.

Key Points

18 F-FDG PET demonstrates increased tissue glucose metabolism, a hallmark of cancers.

Immunohistochemically defined subtypes appear significantly associated with FDG uptake (expressed as SUV max ).

Triple-negative tumours had 1.67-fold higher SUV max values than luminal A tumours.

HER2-positive tumours had 1.27-fold higher SUV max values than luminal A tumours.


Breast cancer PET FDG uptake Immunohistochemistry Subtype 



This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2012-01010846).

Conflict of interest

The authors declare no conflict of interest.


  1. 1.
    Perou CM, Sorlie T, Eisen MB et al (2000) Molecular portraits of human breast tumours. Nature 406:747–752PubMedCrossRefGoogle Scholar
  2. 2.
    van ’t Veer LJ, Dai H, van de Vijver MJ et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536CrossRefGoogle Scholar
  3. 3.
    van’t Veer LJ, Paik S, Hayes DF (2005) Gene expression profiling of breast cancer: a new tumor marker. J Clin Oncol 23:1631–1635CrossRefGoogle Scholar
  4. 4.
    Paik S, Shak S, Tang G et al (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351:2817–2826PubMedCrossRefGoogle Scholar
  5. 5.
    Sorlie T, Perou CM, Tibshirani R et al (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98:10869–10874PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Cheang MC, Chia SK, Voduc D et al (2009) Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst 101:736–750PubMedCrossRefGoogle Scholar
  7. 7.
    Hugh J, Hanson J, Cheang MC et al (2009) Breast cancer subtypes and response to docetaxel in node-positive breast cancer: use of an immunohistochemical definition in the BCIRG 001 trial. J Clin Oncol 27:1168–1176PubMedCrossRefGoogle Scholar
  8. 8.
    Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thurlimann B, Senn HJ (2011) 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 2011. Ann Oncol 22:1736–1747PubMedCrossRefGoogle Scholar
  9. 9.
    Dubsky P, Filipits M, Jakesz R et al (2013) EndoPredict improves the prognostic classification derived from common clinical guidelines in ER-positive, HER2-negative early breast cancer. Ann Oncol 24:640–647PubMedCrossRefGoogle Scholar
  10. 10.
    Niemiec J, Adamczyk A, Malecki K, Ambicka A, Rys J (2013) Tumor grade and matrix metalloproteinase 2 expression in stromal fibroblasts help to stratify the high-risk group of patients with early breast cancer identified on the basis of St Gallen recommendations. Clin Breast Cancer 13:119–128PubMedCrossRefGoogle Scholar
  11. 11.
    Koolen BB, Vrancken Peeters MJ, Aukema TS et al (2012) 18F-FDG PET/CT as a staging procedure in primary stage II and III breast cancer: comparison with conventional imaging techniques. Breast Cancer Res Treat 131:117–126PubMedCrossRefGoogle Scholar
  12. 12.
    Aukema TS, Rutgers EJ, Vogel WV et al (2010) The role of FDG PET/CT in patients with locoregional breast cancer recurrence: a comparison to conventional imaging techniques. Eur J Surg Oncol 36:387–392PubMedCrossRefGoogle Scholar
  13. 13.
    Hatt M, Groheux D, Martineau A et al (2013) Comparison Between 18F-FDG PET image-derived indices for early prediction of response to neoadjuvant chemotherapy in breast cancer. J Nucl Med 54:341–349PubMedCrossRefGoogle Scholar
  14. 14.
    Groheux D, Hindie E, Delord M et al (2012) Prognostic impact of (18)FDG-PET-CT findings in clinical stage III and IIB breast cancer. J Natl Cancer Inst 104:1879–1887PubMedCrossRefGoogle Scholar
  15. 15.
    Song BI, Lee SW, Jeong SY et al (2012) 18F-FDG uptake by metastatic axillary lymph nodes on pretreatment PET/CT as a prognostic factor for recurrence in patients with invasive ductal breast cancer. J Nucl Med 53:1337–1344PubMedCrossRefGoogle Scholar
  16. 16.
    Ueda S, Kondoh N, Tsuda H et al (2008) Expression of centromere protein F (CENP-F) associated with higher FDG uptake on PET/CT, detected by cDNA microarray, predicts high-risk patients with primary breast cancer. BMC Cancer 8:384PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Specht JM, Kurland BF, Montgomery SK et al (2010) Tumor metabolism and blood flow as assessed by positron emission tomography varies by tumor subtype in locally advanced breast cancer. Clin Cancer Res 16:2803–2810PubMedCentralPubMedCrossRefGoogle Scholar
  18. 18.
    Elston CW, Ellis IO (1991) 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 19:403–410PubMedCrossRefGoogle Scholar
  19. 19.
    Hammond ME, Hayes DF, Dowsett M et al (2010) American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol 28:2784–2795PubMedCrossRefGoogle Scholar
  20. 20.
    Goldhirsch A, Wood WC, Gelber RD, Coates AS, Thurlimann B, Senn HJ (2007) Progress and promise: highlights of the international expert consensus on the primary therapy of early breast cancer 2007. Ann Oncol 18:1133–1144PubMedCrossRefGoogle Scholar
  21. 21.
    Jadvar H, Alavi A, Gambhir SS (2009) 18F-FDG uptake in lung, breast, and colon cancers: molecular biology correlates and disease characterization. J Nucl Med 50:1820–1827PubMedCentralPubMedCrossRefGoogle Scholar
  22. 22.
    Edge SB, Compton CC (2010) The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 17:1471–1474PubMedCrossRefGoogle Scholar
  23. 23.
    Groheux D, Giacchetti S, Moretti JL et al (2011) Correlation of high 18F-FDG uptake to clinical, pathological and biological prognostic factors in breast cancer. Eur J Nucl Med Mol Imaging 38:426–435PubMedCrossRefGoogle Scholar
  24. 24.
    Avril N, Menzel M, Dose J et al (2001) Glucose metabolism of breast cancer assessed by 18F-FDG PET: histologic and immunohistochemical tissue analysis. J Nucl Med 42:9–16PubMedGoogle Scholar
  25. 25.
    Buck A, Schirrmeister H, Kuhn T et al (2002) FDG uptake in breast cancer: correlation with biological and clinical prognostic parameters. Eur J Nucl Med Mol Imaging 29:1317–1323PubMedCrossRefGoogle Scholar
  26. 26.
    Kennecke H, Yerushalmi R, Woods R et al (2010) Metastatic behavior of breast cancer subtypes. J Clin Oncol 28:3271–3277PubMedCrossRefGoogle Scholar
  27. 27.
    Gianni L, Dafni U, Gelber RD et al (2011) Treatment with trastuzumab for 1 year after adjuvant chemotherapy in patients with HER2-positive early breast cancer: a 4-year follow-up of a randomised controlled trial. Lancet Oncol 12:236–244PubMedCrossRefGoogle Scholar
  28. 28.
    Caudle AS, Yu TK, Tucker SL et al (2012) Local-regional control according to surrogate markers of breast cancer subtypes and response to neoadjuvant chemotherapy in breast cancer patients undergoing breast conserving therapy. Breast Cancer Res 14:R83PubMedCentralPubMedCrossRefGoogle Scholar
  29. 29.
    Basu S, Chen W, Tchou J et al (2008) Comparison of triple-negative and estrogen receptor-positive/progesterone receptor-positive/HER2-negative breast carcinoma using quantitative fluorine-18 fluorodeoxyglucose/positron emission tomography imaging parameters: a potentially useful method for disease characterization. Cancer 112:995–1000PubMedCrossRefGoogle Scholar
  30. 30.
    Palaskas N, Larson SM, Schultz N et al (2011) 18F-fluorodeoxy-glucose positron emission tomography marks MYC-overexpressing human basal-like breast cancers. Cancer Res 71:5164–5174PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Ueda S, Tsuda H, Asakawa H et al (2008) Clinicopathological and prognostic relevance of uptake level using 18F-fluorodeoxyglucose positron emission tomography/computed tomography fusion imaging (18F-FDG PET/CT) in primary breast cancer. Jpn J Clin Oncol 38:250–258PubMedCrossRefGoogle Scholar
  32. 32.
    Koolen BB, Vrancken Peeters MJ, Wesseling J et al (2012) Association of primary tumour FDG uptake with clinical, histopathological and molecular characteristics in breast cancer patients scheduled for neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging 39:1830–1838PubMedCrossRefGoogle Scholar
  33. 33.
    Koolen BB, Pengel KE, Wesseling J et al (2013) FDG PET/CT during neoadjuvant chemotherapy may predict response in ER-positive/HER2-negative and triple negative, but not in HER2-positive breast cancer. Breast. doi: 10.1016/j.breast.2012.12.020 PubMedGoogle Scholar
  34. 34.
    Kumar R, Chauhan A, Zhuang H, Chandra P, Schnall M, Alavi A (2006) Clinicopathologic factors associated with false negative FDG-PET in primary breast cancer. Breast Cancer Res Treat 98:267–274PubMedCrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2013

Authors and Affiliations

  • Hye Ryoung Koo
    • 1
    • 2
  • Jeong Seon Park
    • 2
  • Keon Wook Kang
    • 3
  • Nariya Cho
    • 1
  • Jung Min Chang
    • 1
  • Min Sun Bae
    • 1
  • Won Hwa Kim
    • 1
  • Su Hyun Lee
    • 1
  • Mi Young Kim
    • 4
  • Jin You Kim
    • 5
  • Mirinae Seo
    • 1
  • Woo Kyung Moon
    • 1
    Email author
  1. 1.Department of RadiologySeoul National University College of MedicineSeoulKorea
  2. 2.Department of RadiologyHanyang University College of MedicineSeoulKorea
  3. 3.Department of Nuclear MedicineSeoul National University College of MedicineSeoulKorea
  4. 4.Department of RadiologyKonkuk University Medical CenterSeoulRepublic of Korea
  5. 5.Department of RadiologyPusan National University HospitalPusanRepublic of Korea

Personalised recommendations