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Early prediction of triple negative breast cancer response to cisplatin treatment using diffusion-weighted MRI and 18F-FDG-PET

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Abstract

Background

We evaluated the potential of diffusion-weighted MRI (DW-MRI) and 18F-FDG-PET for the early prediction of a triple negative breast cancer (TNBC) response to cisplatin.

Methods

Cisplatin-treated TNBC tumor-bearing mice were categorized as responders or non-responders based on the tumor growth rate. DW-MRI and 18F-FDG-PET were performed before and after treatment (day 0 and days 3 and 7, respectively). The average apparent diffusion coefficient value (ADCmean), the highest standardized uptake value (SUVmax), and the metabolic tumor volume (MTV) were measured. The ratios of each parameter relative to day 0 were calculated [ΔADCmean (day 3) and (day 7), ΔSUVmax (day 3) and (day 7), and ΔMTV (day 3) and (day 7), respectively]. Overall survival rates were compared based on the thresholds determined by these parameters.

Results

Both the day 3 and day 7 ratios of ADCmean and MTV showed significant differences between the responder and non-responder groups, whereas the ratios of SUVmax did not. Mice with ΔADCmean (day 3) exceeding the threshold showed a longer overall survival rate. Mice with ΔSUVmax (day 7), ΔMTV (day 3), and ΔMTV (day 7) below the respective thresholds showed a longer overall survival rate.

Conclusions

The ratios of ADCmean, SUVmax, and MTV have the potential to predict the therapeutic response and to screen non-responders in the ultra-early phase following cisplatin treatment in patients with TNBC.

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References

  1. Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA, et al. Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res. 2007;13:4429–34.

    Article  PubMed  Google Scholar 

  2. Collignon J, Lousberg L, Schroeder H, Jerusalem G. Triple-negative breast cancer: treatment challenges and solutions. Breast Cancer Targets Ther. 2016;8:93–107.

    CAS  Google Scholar 

  3. Petrelli F, Coinu A, Borgonovo K, Cabiddu M, Ghilardi M, Lonati V, et al. The value of platinum agents as neoadjuvant chemotherapy in triple-negative breast cancers: a systematic review and meta-analysis. Breast Cancer Res Treat. 2014;144:223–32.

    Article  CAS  PubMed  Google Scholar 

  4. Gerratana L, Fanotto V, Pelizzari G, Agostinetto E, Puglisi F. Do platinum salts fit all triple negative breast cancers ? Cancer Treat Rev. 2016;48:34–41.

    Article  CAS  PubMed  Google Scholar 

  5. Park SH, Moon WK, Cho N, Song IC, Chang JM, Park I-A, et al. Diffusion-weighted MR imaging: pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Radiology. 2010;257:56–63.

    Article  PubMed  Google Scholar 

  6. Iwasa H, Kubota K, Hamada N, Nogami M, Nishioka A. Early prediction of response to neoadjuvant chemotherapy in patients with breast cancer using diffusion-weighted imaging and gray-scale ultrasonography. Oncol Rep. 2014;31:1555–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Weber WA. Assessing tumor response to therapy. J Nucl Med. 2009;50:1S–10S.

    Article  CAS  PubMed  Google Scholar 

  8. Lucignani G. Monitoring cancer therapy with PET: probably effective, but more research is needed. Eur J Nucl Med Mol Imaging. 2009;36:1520–5.

    Article  PubMed  Google Scholar 

  9. Woodhams R, Ramadan S, Stanwell P, Sakamoto S, Hata H, Ozaki M, et al. Diffusion-weighted imaging of the breast: principles and clinical applications. Radiographics. 2011;31:1059–84.

    Article  PubMed  Google Scholar 

  10. Pickles MD, Gibbs P, Lowry M, Turnbull LW. Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. Magn Reson Imaging. 2006;24:843–7.

    Article  PubMed  Google Scholar 

  11. Li XR, Cheng LQ, Liu M, Zhang YJ, Wang JD, Zhang AL, et al. DW-MRI ADC values can predict treatment response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. Med Oncol. 2012;29:425–31.

    Article  CAS  PubMed  Google Scholar 

  12. Groheux D, Biard L, Giacchetti S, Teixeira L, Hindie E, Cuvier C, et al. 18F-FDG PET/CT for the early evaluation of response to neoadjuvant treatment in triple-negative breast cancer: influence of the chemotherapy regimen. J Nucl Med. 2016;57:536–43.

    Article  CAS  PubMed  Google Scholar 

  13. Im HJ, Kim YK, Kim YI, Lee JJ, Lee WW, Kim SE. Usefulness of combined metabolic-volumetric indices of 18F-FDG PET/CT for the early prediction of neoadjuvant chemotherapy outcomes in breast cancer. Nucl Med Mol Imaging. 2013;47:36–43.

    Article  CAS  PubMed  Google Scholar 

  14. Jandial DD, Messer K, Farshchi-Heydari S, Pu M, Howell SB. Tumor platinum concentration following intraperitoneal administration of cisplatin versus carboplatin in an ovarian cancer model. Gynecol Oncol. 2009;115:362–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Abedi I, Tavakkoli MB, Rabbani M, Jabbari K, Sirous M, Far GY. Multiparametric magnetic resonance imaging of prostate cancer: association of quantitative magnetic resonance parameters with histopathologic findings. Iran J Radiol. 2017;13:e37844.

    Google Scholar 

  16. Hyun SH, Choi JY, Kim K, Kim J, Shim YM, Um S-W, et al. Volume-based parameters of 18F-fluorodeoxyglucose positron emission tomography/computed tomography improve outcome prediction in early-stage non-small cell lung cancer after surgical resection. Ann Surg. 2013;257:364–70.

    Article  PubMed  Google Scholar 

  17. Arpino G, Gutierrez C, Weiss H, Rimawi M, Massarweh S, Bharwani L, et al. Treatment of human epidermal growth factor receptor 2-overexpressing breast cancer xenografts with multiagent HER-targeted therapy. J Natl Cancer Inst. 2007;99:694–705.

    Article  CAS  PubMed  Google Scholar 

  18. Liu S, Ren R, Chen Z, Wang Y, Fan T, Li C, et al. Diffusion-weighted imaging in assessing pathological response of tumor in breast cancer subtype to neoadjuvant chemotherapy. J Magn Reson Imaging. 2015;42:779–87.

    Article  PubMed  Google Scholar 

  19. Humphries PD, Sebire NJ, Siegel MJ, Olsen OE. Tumors in pediatric patients at diffusion-weighted MR imaging: apparent diffusion coefficient and tumor cellularity. Radiology. 2007;245:848–54.

    Article  PubMed  Google Scholar 

  20. Huang W, Fan M, Liu B, Fu Z, Zhou T, Zhang Z, et al. Value of metabolic tumor volume on repeated 18F-FDG PET/CT for early prediction of survival in locally advanced non-small cell lung cancer treated with concurrent chemoradiotherapy. J Nucl Med. 2014;55:1584–90.

    Article  CAS  PubMed  Google Scholar 

  21. Lim R, Eaton A, Lee NY, Setton J, Ohri N, Rao S, et al. 18F-FDG PET/CT metabolic tumor volume and total lesion glycolysis predict outcome in oropharyngeal squamous cell carcinoma. J Nucl Med. 2012;53:1506–13.

    Article  CAS  PubMed  Google Scholar 

  22. Yabuuchi H, Hatakenaka M, Takayama K, Matsuo Y, Sunami S, Kamitani T, et al. Non-small cell lung cancer: detection of early response to chemotherapy by using contrast-enhanced dynamic and diffusion-weighted MR imaging. Radiology. 2011;261:598–604.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan

    Huong Nguyen-Thu, Takahito Nakajima, Tien Nguyen-Cong, A. Adhipatria P. Kartamihardja & Yoshito Tsushima

  2. Department of Bioimaging Information Analysis, Gunma University Graduate School of Medicine, 3-39-22 Showa, Maebashi, Gunma, 371-8511, Japan

    Hirofumi Hanaoka & Aiko Yamaguchi

  3. Research Program for Diagnostic and Molecular Imaging, Division of Integrated Oncology Research, Gunma University Initiative for Advanced Research (GIAR), Maebashi, Japan

    Yoshito Tsushima

Authors
  1. Huong Nguyen-Thu
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  2. Hirofumi Hanaoka
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  3. Takahito Nakajima
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  4. Aiko Yamaguchi
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  5. Tien Nguyen-Cong
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  6. A. Adhipatria P. Kartamihardja
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Correspondence to Hirofumi Hanaoka.

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Nguyen-Thu, H., Hanaoka, H., Nakajima, T. et al. Early prediction of triple negative breast cancer response to cisplatin treatment using diffusion-weighted MRI and 18F-FDG-PET. Breast Cancer 25, 334–342 (2018). https://doi.org/10.1007/s12282-018-0834-z

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  • DOI: https://doi.org/10.1007/s12282-018-0834-z

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