Skip to main content

Advertisement

SpringerLink
Log in

Diffusion-weighted imaging reflects pathological therapeutic response and relapse in breast cancer

  • Original Article
  • Published:
Breast Cancer Aims and scope Submit manuscript

Abstract

Background

Conventional imaging does not always accurately depict the pathological response to neoadjuvant chemotherapy (NAC). Diffusion-weighted imaging (DWI) may provide additional insight into the chemotherapeutic effect. This study assessed whether the apparent diffusion coefficient (ADC) correlated with pathological outcome and prognosis in breast cancer patients receiving NAC.

Methods

Fifty-six patients with locally advanced breast cancer received surgery after NAC. Dynamic contrast-enhanced (DCE) and DWI were performed before and after NAC. The pathological response was classified into five categories from no response to complete response according to amount of residual cancer. The correlation between ADC and postoperative pathologic and prognostic outcome was assessed.

Results

The distribution of the pathological response classification was as follows: no response, 3 cases; mild response, 22 cases; moderate response, 12 cases; marked response, 11 cases; complete response, 8 cases. ADC after NAC correlated with pathological response, but ADC before NAC did not. The change in ADC after chemotherapy had better correlation coefficient (r = 0.67) than change in size (r = 0.58) and ADC after NAC (r = 0.64). Although the group with larger change of tumor size showed only marginal significance compared with the smaller change group (p = 0.089), the group with higher change of ADC showed significantly better prognosis than the lower one (p = 0.038).

Conclusions

Change in ADC after chemotherapy better correlated with pathological outcome and prognosis than change in tumor size. DWI has potential in evaluating the pathological outcome of NAC in breast cancer patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. EBCTCG. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet. 2005;365:1687–717.

    Article  Google Scholar 

  2. Fisher B, Bryant J, Wolmark N, Mamounas E, Brown A, Fisher ER, et al. Effect of preoperative chemotherapy on the outcome of women with operable breast cancer. J Clin Oncol. 1998;16:2672–85.

    CAS  PubMed  Google Scholar 

  3. Bear HD, Anderson S, Smith RE, Geyer CE Jr, Mamounas EP, Fisher B, et al. Sequential preoperative or postoperative docetaxel added to preoperative doxorubicin plus cyclophosphamide for operable breast cancer:National Surgical Adjuvant Breast and Bowel Project Protocol B-27. J Clin Oncol. 2006;24:2019–27.

    Article  CAS  PubMed  Google Scholar 

  4. Wolmark N, Wang J, Mamounas E, Bryant J, Fisher B. Preoperative chemotherapy in patients with operable breast cancer: nine-year results from National Surgical Adjuvant Breast and Bowel Project B-18. J Natl Cancer Inst Monogr. 2001;30:96–102.

    Article  PubMed  Google Scholar 

  5. Symmans WF, Peintinger F, Hatzis C, Rajan R, Kuerer H, Valero V, et al. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol. 2007;25:4414–22.

    Article  PubMed  Google Scholar 

  6. Nagashima T, Sakakibara M, Sangai T, Kazama T, Nakatani Y, Miyazaki M. Tumor reduction rate predicts early recurrence in patients with breast cancer failing to achieve complete response to primary chemotherapy. Breast Cancer. 2010;17:125–30.

    Article  PubMed  Google Scholar 

  7. von Minckwitz G, Untch M, Blohmer JU, Costa SD, Eidtmann H, Fasching PA, 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.

    Article  Google Scholar 

  8. Chagpar AB, Middleton LP, Sahin AA, Dempsey P, Buzdar AU, Mirza AN, et al. Accuracy of physical examination, ultrasonography, and mammography in predicting residual pathologic tumor size in patients treated with neoadjuvant chemotherapy. Ann Surg. 2006;243:257–64.

    Article  PubMed Central  PubMed  Google Scholar 

  9. Esserman L, Hylton N, Yassa L, Barclay J, Frankel S, Sickles E. Utility of magnetic resonance imaging in the management of breast cancer: evidence for improved preoperative staging. J Clin Oncol. 1999;17:110–9.

    CAS  PubMed  Google Scholar 

  10. Padhani AR, Hayes C, Assersohn L, Powles T, Makris A, Suckling J, et al. Prediction of clinicopathologic response of breast cancer to primary chemotherapy at contrast-enhanced MR imaging: initial clinical results. Radiology. 2006;239:361–74.

    Article  PubMed  Google Scholar 

  11. Thoeny HC, Ross BD. Predicting and monitoring cancer treatment response with diffusion-weighted MRI. J Magn Reson Imaging. 2010;32:2–16.

    Article  PubMed Central  PubMed  Google Scholar 

  12. Guo Y, Cai YQ, Cai ZL, Gao YG, An NY, Ma L, et al. Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging. 2002;16:172–8.

    Article  PubMed  Google Scholar 

  13. Rubesova E, Grell AS, De Maertelaer V, Metens T, Chao SL, Lemort M. Quantitative diffusion imaging in breast cancer: a clinical prospective study. J Magn Reson Imaging. 2006;24:319–24.

    Article  PubMed  Google Scholar 

  14. Ross BD, Moffat BA, Lawrence TS, Mukherji SK, Gebarski SS, Quint DJ, et al. Evaluation of cancer therapy using diffusion magnetic resonance imaging. Mol Cancer Ther. 2003;2:581–7.

    CAS  PubMed  Google Scholar 

  15. 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 

  16. Kurosumi M, Akashi-Tanaka S, Akiyama F, Komoike Y, Mukai H, Nakamura S, et al. Histopathological criteria for assessment of therapeutic response in breast cancer (2007 version). Breast Cancer. 2008;15:5–7.

    Article  PubMed  Google Scholar 

  17. Goldhirsch A, Wood WC, Gelber RD, Coates AS, Thurlimann B, Senn HJ. Progress and promise: highlights of the international expert consensus on the primary therapy of early breast cancer 2007. Ann Oncol. 2007;18:1133–44.

    Article  CAS  PubMed  Google Scholar 

  18. Goldhirsch A, Glick JH, Gelber RD, Coates AS, Thurlimann B, Senn HJ. Meeting highlights: international expert consensus on the primary therapy of early breast cancer 2005. Ann Oncol. 2005;16:1569–83.

    Article  CAS  PubMed  Google Scholar 

  19. Chenevert TL, Stegman LD, Taylor JM, Robertson PL, Greenberg HS, Rehemtulla A, et al. Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst. 2000;92:2029–36.

    Article  CAS  PubMed  Google Scholar 

  20. Lee KC, Moffat BA, Schott AF, Layman R, Ellingworth S, Juliar R, et al. Prospective early response imaging biomarker for neoadjuvant breast cancer chemotherapy. Clin Cancer Res. 2007;13:443–50.

    Article  CAS  PubMed  Google Scholar 

  21. Chenevert TL, McKeever PE, Ross BD. Monitoring early response of experimental brain tumors to therapy using diffusion magnetic resonance imaging. Clin Cancer Res. 1997;3:1457–66.

    CAS  PubMed  Google Scholar 

  22. Woodhams R, Kakita S, Hata H, Iwabuchi K, Kuranami M, Gautam S, et al. Identification of residual breast carcinoma following neoadjuvant chemotherapy: diffusion-weighted imaging–comparison with contrast-enhanced MR imaging and pathologic findings. Radiology. 2010;254:357–66.

    Article  PubMed  Google Scholar 

  23. Fangberget A, Nilsen LB, Hole KH, Holmen MM, Engebraaten O, Naume B, et al. Neoadjuvant chemotherapy in breast cancer-response evaluation and prediction of response to treatment using dynamic contrast-enhanced and diffusion-weighted MR imaging. Eur Radiol. 2011;21:1188–99.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Wu LM, Hu JN, Gu HY, Hua J, Chen J, Xu JR. Can diffusion-weighted MR imaging and contrast-enhanced MR imaging precisely evaluate and predict pathological response to neoadjuvant chemotherapy in patients with breast cancer? Breast Cancer Res Treat. 2012;135:17–28.

    Article  CAS  PubMed  Google Scholar 

  25. Woodhams R, Matsunaga K, Kan S, Hata H, Ozaki M, Iwabuchi K, et al. ADC mapping of benign and malignant breast tumors. Magn Reson Med Sci. 2005;4:35–42.

    Article  PubMed  Google Scholar 

  26. Parsian S, Rahbar H, Allison KH, Demartini WB, Olson ML, Lehman CD, et al. Nonmalignant breast lesions: ADCs of benign and high-risk subtypes assessed as false-positive at dynamic enhanced MR Imaging. Radiology. 2012;265:696–706.

    Article  PubMed Central  PubMed  Google Scholar 

  27. Rahbar H, Partridge SC, Demartini WB, Gutierrez RL, Allison KH, Peacock S, et al. In vivo assessment of ductal carcinoma in situ grade: a model incorporating dynamic contrast-enhanced and diffusion-weighted breast MR imaging parameters. Radiology. 2012;263:374–82.

    Article  PubMed Central  PubMed  Google Scholar 

  28. Tsushima Y, Takahashi-Taketomi A, Endo K. Magnetic resonance (MR) differential diagnosis of breast tumors using apparent diffusion coefficient (ADC) on 1.5-T. J Magn Reson Imaging. 2009;30:249–55.

    Article  PubMed  Google Scholar 

  29. Sharma U, Danishad KK, Seenu V, Jagannathan NR. Longitudinal study of the assessment by MRI and diffusion-weighted imaging of tumor response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. NMR Biomed. 2009;22:104–13.

    Article  PubMed  Google Scholar 

  30. Choi SY, Chang YW, Park HJ, Kim HJ, Hong SS, Seo DY. Correlation of the apparent diffusion coefficiency values on diffusion-weighted imaging with prognostic factors for breast cancer. Br J Radiol. 2012;85:e474–9.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Jeh SK, Kim SH, Kim HS, Kang BJ, Jeong SH, Yim HW, et al. Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging. 2011;33:102–9.

    Article  PubMed  Google Scholar 

  32. Rajan R, Poniecka A, Smith TL, Yang Y, Frye D, Pusztai L, et al. Change in tumor cellularity of breast carcinoma after neoadjuvant chemotherapy as a variable in the pathologic assessment of response. Cancer. 2004;100:1365–73.

    Article  PubMed  Google Scholar 

  33. Nilsen L, Fangberget A, Geier O, Olsen DR, Seierstad T. Diffusion-weighted magnetic resonance imaging for pretreatment prediction and monitoring of treatment response of patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. Acta Oncol. 2010;49:354–60.

    Article  PubMed  Google Scholar 

  34. Park SH, Moon WK, Cho N, Song IC, Chang JM, Park IA, 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 

  35. Iacconi C, Giannelli M, Marini C, Cilotti A, Moretti M, Viacava P, et al. The role of mean diffusivity (MD) as a predictive index of the response to chemotherapy in locally advanced breast cancer: a preliminary study. Eur Radiol. 2010;20:303–8.

    Article  PubMed  Google Scholar 

  36. Dzik-Jurasz A, Domenig C, George M, Wolber J, Padhani A, Brown G, et al. Diffusion MRI for prediction of response of rectal cancer to chemoradiation. Lancet. 2002;360:307–8.

    Article  PubMed  Google Scholar 

  37. Koh DM, Scurr E, Collins D, Kanber B, Norman A, Leach MO, et al. Predicting response of colorectal hepatic metastasis: value of pretreatment apparent diffusion coefficients. AJR Am J Roentgenol. 2007;188:1001–8.

    Article  PubMed  Google Scholar 

Download references

Conflict of interest

The authors declare that they have no conflicts of interest.

Author information

Authors and Affiliations

  1. Department of General Surgery, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba, 260-0856, Japan

    Hiroshi Fujimoto, Takeshi Nagashima, Masahiro Sakakibara, Tiberiu Hiroshi Suzuki, Yoshiyuki Okubo, Nobumitsu Shiina, Kaoru Fujisaki & Masaru Miyazaki

  2. Department of Radiology, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba, 260-0856, Japan

    Toshiki Kazama

  3. Department of Diagnostic Pathology, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba, 260-0856, Japan

    Satoshi Ota

Authors
  1. Hiroshi Fujimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Toshiki Kazama
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takeshi Nagashima
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Masahiro Sakakibara
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tiberiu Hiroshi Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yoshiyuki Okubo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nobumitsu Shiina
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kaoru Fujisaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Satoshi Ota
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Masaru Miyazaki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiroshi Fujimoto.

About this article

Cite this article

Fujimoto, H., Kazama, T., Nagashima, T. et al. Diffusion-weighted imaging reflects pathological therapeutic response and relapse in breast cancer. Breast Cancer 21, 724–731 (2014). https://doi.org/10.1007/s12282-013-0449-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12282-013-0449-3

Keywords

Search

Navigation