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Correlation Between F-18 Fluorodeoxyglucose Positron Emission Tomography Metabolic Parameters and Dynamic Contrast-Enhanced MRI-Derived Perfusion Data in Patients with Invasive Ductal Breast Carcinoma

  • Breast Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Purpose

The aim of this study was to establish possible relationships among the metabolic and vascular characteristics of breast cancer using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging.

Methods

Sixty-seven female patients with invasive ductal breast carcinoma (age 32–79 years) who underwent FDG PET/CT and DCE–MRI prior to cancer treatment were included in the study. The maximum standardized uptake value (SUVmax), metabolic tumor volume, total lesion glycolysis (TLG), and heterogeneity factor (HF) were derived from FDG PET/CT. The DCE–MRI parameters K trans, K ep, and V e were obtained for all tumors, and relationships between the metabolic and perfusion parameters were sought via Spearman’s rank correlation analysis. The prognostic significance of clinicopathological and imaging parameters in terms of recurrence-free survival (RFS) was also evaluated.

Results

No significant correlation between perfusion and metabolic parameters (p > 0.05) was found, except between SUVmax and V e (p = 0.001, rho = −0.391). Recurrence developed in 12 of the 67 patients (17.9 %, follow-up period 8–41 months). Age (p = 0.016) and HF (p = 0.027) were significant independent predictors of recurrence-free survival (RFS) upon multivariate analysis. The RFS of patients under 40 years of age was significantly poorer than that of older patients (p < 0.001). Survival of patients with more heterogeneous tumors (HF less than −0.12) was poorer than those with relatively homogenous tumors (p = 0.033).

Conclusions

Tumors with higher levels of glucose metabolism (SUVmax values) exhibited higher tumor cellularities (V e values). Also, of the various metabolic and perfusion parameters available, tumor heterogeneity measured via FDG PET/CT (HF) may be useful in predicting RFS in breast cancer patients.

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References

  1. Peters NH, Borel Rinkes IH, Zuithoff NP, Mali WP, Moons KG, Peeters PH. Meta-analysis of MR imaging in the diagnosis of breast lesions. Radiology. 2008;246:116–24.

    Article  PubMed  Google Scholar 

  2. Groheux D, Espie M, Giacchetti S, Hindie E. Performance of FDG PET/CT in the clinical management of breast cancer. Radiology. 2013;266:388–405.

    Article  PubMed  Google Scholar 

  3. Tateishi U, Gamez C, Dawood S, Yeung HW, Cristofanilli M, Macapinlac HA. Bone metastases in patients with metastatic breast cancer: morphologic and metabolic monitoring of response to systemic therapy with integrated PET/CT. Radiology. 2008;247:189–96.

    Article  PubMed  Google Scholar 

  4. Lee HY, Hyun SH, Lee KS, et al. Volume-based parameter of 18)F-FDG PET/CT in malignant pleural mesothelioma: prediction of therapeutic response and prognostic implications. Ann Surg Oncol. 2010;17:2787–94.

    Article  PubMed  Google Scholar 

  5. Guillem JG, Moore HG, Akhurst T, et al. Sequential preoperative fluorodeoxyglucose-positron emission tomography assessment of response to preoperative chemoradiation: a means for determining longterm outcomes of rectal cancer. J Am Coll Surg. 2004;199:1–7.

    Article  PubMed  Google Scholar 

  6. van Baardwijk A, Bosmans G, van Suylen RJ et al. Correlation of intra-tumour heterogeneity on 18F-FDG PET with pathologic features in non-small cell lung cancer: a feasibility study. Radiother Oncol. 2008;87:55–8.

    Article  PubMed  Google Scholar 

  7. Kidd EA, Grigsby PW. Intratumoral metabolic heterogeneity of cervical cancer. Clin Cancer Res. 2008;14:5236–41.

    Article  CAS  PubMed  Google Scholar 

  8. Tixier F, Groves AM, Goh V, et al. Correlation of intra-tumor 18F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer. PLoS One. 2014;9:e99567.

    Article  PubMed Central  PubMed  Google Scholar 

  9. Yankeelov TE, Gore JC. Dynamic contrast enhanced magnetic resonance imaging in oncology: theory, data acquisition, analysis, and examples. Curr Med Imaging Rev. 2009;3:91–107.

    Article  PubMed Central  PubMed  Google Scholar 

  10. Tofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999;10:223–32.

    Article  CAS  PubMed  Google Scholar 

  11. Semple SI, Gilbert FJ, Redpath TW, et al. The relationship between vascular and metabolic characteristics of primary breast tumours. Eur Radiol. 2004;14:2038–45.

    Article  PubMed  Google Scholar 

  12. Brix G, Henze M, Knopp MV, et al. Comparison of pharmacokinetic MRI and [18F] fluorodeoxyglucose PET in the diagnosis of breast cancer: initial experience. Eur Radiol. 2001;11:2058–70.

    Article  CAS  PubMed  Google Scholar 

  13. Singletary SE, Allred C, Ashley P, et al. Revision of the American Joint Committee on Cancer staging system for breast cancer. J Clin Oncol. 2002;20:3628–36.

    Article  PubMed  Google Scholar 

  14. Huang B, Chan T, Kwong DL, Chan WK, Khong PL. Nasopharyngeal carcinoma: investigation of intratumoral heterogeneity with FDG PET/CT. AJR Am J Roentgenol. 2012;199:169–74.

    Article  PubMed  Google Scholar 

  15. Tofts PS. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging. 1997;7:91–101.

    Article  CAS  PubMed  Google Scholar 

  16. Hudis CA, Barlow WE, Costantino JP, et al. Proposal for standardized definitions for efficacy end points in adjuvant breast cancer trials: the STEEP system. J Clin Oncol. 2007;25:2127–32.

    Article  PubMed  Google Scholar 

  17. Knopp MV, von Tengg-Kobligk H, Choyke PL. Functional magnetic resonance imaging in oncology for diagnosis and therapy monitoring. Mol Cancer Ther. 2003;2:419–26.

    CAS  PubMed  Google Scholar 

  18. Higashi T, Tamaki N, Torizuka T, et al. FDG uptake, GLUT-1 glucose transporter and cellularity in human pancreatic tumors. J Nucl Med. 1998;39:1727–35.

    CAS  PubMed  Google Scholar 

  19. Miller JC, Pien HH, Sahani D, Sorensen AG, Thrall JH. Imaging angiogenesis: applications and potential for drug development. J Natl Cancer Inst. 2005;97:172–87.

    Article  CAS  PubMed  Google Scholar 

  20. Assi HA, Khoury KE, Dbouk H, Khalil LE, Mouhieddine TH, El Saghir NS. Epidemiology and prognosis of breast cancer in young women. J Thorac Dis. 2013;5 Suppl 1:S2–8.

    PubMed Central  PubMed  Google Scholar 

  21. Soussan M, Orlhac F, Boubaya M, et al. Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer. PLoS One. 2014;9:e94017.

    Article  PubMed Central  PubMed  Google Scholar 

  22. Cook GJ, Yip C, Siddique M, et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013;54:19–26.

    Article  PubMed  Google Scholar 

  23. Tixier F, Le Rest CC, Hatt M, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med. 2011;52:369–78.

    Article  PubMed Central  PubMed  Google Scholar 

  24. Lu X, Kang Y. Hypoxia and hypoxia-inducible factors: master regulators of metastasis. Clin Cancer Res. 2010;16:5928–35.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Leek RD, Landers RJ, Harris AL, Lewis CE. Necrosis correlates with high vascular density and focal macrophage infiltration in invasive carcinoma of the breast. Br J Cancer. 1999;79:991–5.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. Jimenez RE, Wallis T, Visscher DW. Centrally necrotizing carcinomas of the breast: a distinct histologic subtype with aggressive clinical behavior. Am J Surg Pathol. 2001;25:331–7.

    Article  CAS  PubMed  Google Scholar 

  27. Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40:133–40.

    Article  PubMed  Google Scholar 

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Acknowledgment

Jung-Dong Lee (Office of Biostatistics, Ajou University School of Medicine) kindly provided statistical advice for this manuscript.

Disclosures

All authors declare that they have no potential conflicts of interest to declare.

Author information

Authors and Affiliations

  1. Department of Radiology, Ajou University School of Medicine, Suwon, Korea

    Tae Hee Kim MD, PhD & Doo Kyoung Kang MD

  2. Department of Nuclear Medicine and Molecular Imaging, School of Medicine, Ajou University School of Medicine, Suwon, Korea

    Joon-Kee Yoon MD, PhD, Su Jin Lee MD, PhD & Young-Sil An MD, PhD

  3. Department of Surgery, Ajou University School of Medicine, Suwon, Korea

    Yong Sik Jung MD

  4. Department of Pathology, Ajou University School of Medicine, Suwon, Korea

    Hyunee Yim MD, PhD

Authors
  1. Tae Hee Kim MD, PhD
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  2. Joon-Kee Yoon MD, PhD
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  3. Doo Kyoung Kang MD
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  4. Su Jin Lee MD, PhD
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  5. Yong Sik Jung MD
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  6. Hyunee Yim MD, PhD
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  7. Young-Sil An MD, PhD
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Correspondence to Young-Sil An MD, PhD.

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Kim, T.H., Yoon, JK., Kang, D.K. et al. Correlation Between F-18 Fluorodeoxyglucose Positron Emission Tomography Metabolic Parameters and Dynamic Contrast-Enhanced MRI-Derived Perfusion Data in Patients with Invasive Ductal Breast Carcinoma. Ann Surg Oncol 22, 3866–3872 (2015). https://doi.org/10.1245/s10434-015-4526-z

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  • DOI: https://doi.org/10.1245/s10434-015-4526-z

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