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Can diffusion-weighted MR imaging and contrast-enhanced MR imaging precisely evaluate and predict pathological response to neoadjuvant chemotherapy in patients with breast cancer?

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Abstract

Clinical evidence regarding the value of MRI for therapy responses assessment in breast cancer is increasing. The objective of this study is to compare the diagnostic capability of diffusion-weighted MR imaging (DW-MRI) and contrast-enhanced MR imaging (CE-MRI) to evaluate and predict pathological response in breast cancer patients receiving neoadjuvant chemotherapy (NAC). We performed a meta-analysis of all available studies of the diagnostic performance of DW-MRI or CE-MRI to evaluate and predict pathological response to NAC in patients with breast cancer. We determined sensitivities and specificities across studies, calculated positive and negative likelihood ratios (LR+ and LR−), diagnostic odds ratio (DOR) and constructed summary receiver operating characteristic curves using hierarchical regression models. Methodological quality was assessed by QUADAS tool. Thirty-four studies met the inclusion criteria and involved 1,932 pathologically confirmed patients in total. Methodological quality was relatively high. DW-MRI sensitivity was 0.93 (95 % CI 0.82–0.97) and specificity was 0.82 (95 % CI 0.70–0.90). Overall LR+ was 5.09 (95 % CI 3.09–8.38), LR− was 0.09 (95 % CI 0.04–0.22), and DOR was 55.59 (95 % CI 21.80–141.80). CE-MRI sensitivity was 0.68 (95 % CI 0.57–0.77) and specificity was 0.91 (95 % CI 0.87–0.94). Overall LR+ was 7.48 (95 % CI 5.29–10.57), LR− was 0.36 (95 % CI 0.27–0.48), and DOR was 20.98 (95 % CI 13.24–33.24). Our study confirms that DW-MRI is a high sensitive and CE-MRI is a high specific modality in predicting pathological response to NAC in breast cancer patients. The combined use of DW-MRI and CE-MRI has the potential to improve the diagnostic performance in monitoring NAC. Further large prospective studies are warranted to assess the actual value of this combination in breast cancer preoperative treatment screening.

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References

  1. Mieog JS, van der Hage JA, van de Velde CJ (2007) Neoadjuvant chemotherapy for operable breast cancer. Br J Surg 94(10):1189–1200

    Article  PubMed  CAS  Google Scholar 

  2. Kaufmann M, von Minckwitz G, Bear HD et al (2007) Recommendations from an international expert panel on the use of neoadjuvant (primary) systemic treatment of operable breast cancer: new perspectives 2006. Ann Oncol 18(12):1927–1934

    Article  PubMed  CAS  Google Scholar 

  3. Rastogi P, Anderson SJ, Bear HD et al (2008) Preoperative chemotherapy: updates of National Surgical Adjuvant Breast and Bowel Project Protocols B-18 and B-27. J Clin Oncol 26(5):778–785

    Article  PubMed  Google Scholar 

  4. Jeruss JS, Mittendorf EA, Tucker SL et al (2008) Combined use of clinical and pathologic staging variables to define outcomes for breast cancer patients treated with neoadjuvant therapy. J Clin Oncol 26(2):246–252

    Article  PubMed  CAS  Google Scholar 

  5. Symmans WF, Peintinger F, Hatzis C et al (2007) Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol 25(28):4414–4422

    Article  PubMed  Google Scholar 

  6. Tardivon AA, Ollivier L, El Khoury C, Thibault F (2006) Monitoring therapeutic efficacy in breast carcinomas. Eur Radiol 16(11):2549–2558

    Article  PubMed  Google Scholar 

  7. Heldahl MG, Lundgren S, Jensen LR, Gribbestad IS, Bathen TF (2011) Monitoring neoadjuvant chemotherapy in breast cancer patients: improved MR assessment at 3 T? J Magn Reson Imaging. doi:10.1002/jmri.22642

    Google Scholar 

  8. Liu YH, Ye JM, Xu L et al (2011) Effectiveness of dynamic contrast-enhanced magnetic resonance imaging in evaluating clinical responses to neoadjuvant chemotherapy in breast cancer. Chin Med J (Engl) 124(2):194–198

    Google Scholar 

  9. Chenevert TL, Stegman LD, Taylor JM et al (2000) Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst 92(24):2029–2036

    Article  PubMed  CAS  Google Scholar 

  10. Herneth AM, Guccione S, Bednarski M (2003) Apparent diffusion coefficient: a quantitative parameter for in vivo tumor characterization. Eur J Radiol 45(3):208–213

    Article  PubMed  Google Scholar 

  11. Padhani AR, Liu G, Koh DM et al (2009) Diffusion weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 11(2):102–125

    PubMed  CAS  Google Scholar 

  12. Lyng H, Haraldseth O, Rofstad EK (2000) Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med 43(6):828–836

    Article  PubMed  CAS  Google Scholar 

  13. Theilmann RJ, Borders R, Trouard TP et al (2004) Changes in water mobility measured by diffusion MRI predict response of metastatic breast cancer to chemotherapy. Neoplasia 6:831–837

    Article  PubMed  Google Scholar 

  14. Thoeny HC, Ross BD (2010) Predicting and monitoring cancer treatment response with diffusion-weighted MRI. J Magn Reson Imaging 32(1):2–16

    Article  PubMed  Google Scholar 

  15. Pickles MD, Gibbs P, Lowry M, Turnbull LW (2006) Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. Magn Reson Imaging 24:843–847

    Article  PubMed  Google Scholar 

  16. Tozaki M, Oyama Y, Fukuma E (2010) Preliminary study of early response to neoadjuvant chemotherapy after the first cycle in breast cancer: comparison of 1H magnetic resonance spectroscopy with diffusion magnetic resonance imaging. Jpn J Radiol 28(2):101–109

    Article  PubMed  CAS  Google Scholar 

  17. Yankeelov TE, Lepage M, Chakravarthy A et al (2007) Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results. Magn Reson Imaging 25:1–13

    Article  PubMed  Google Scholar 

  18. Whiting P, Rutjes AW, Reitsma JB et al (2003) The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med Res Methodol 3:25

    Article  PubMed  Google Scholar 

  19. Rutter CM, Gatsonis CA (2010) A hierarchical regression approach to metaanalysis of diagnostic test accuracy evaluations. Stat Med 20(19):2865–2884

    Article  Google Scholar 

  20. Park SH, Moon WK, Cho N et al (2012) Comparison of diffusion-weighted MR imaging and FDG PET/CT to predict pathological complete response to neoadjuvant chemotherapy in patients with breast cancer. Eur Radiol 22(1):18–25

    Article  PubMed  Google Scholar 

  21. Park JS, Moon WK, Lyou CY, Cho N, Kang KW, Chung JK (2011) The assessment of breast cancer response to neoadjuvant chemotherapy: comparison of magnetic resonance imaging and 18F-fluorodeoxyglucose positron emission tomography. Acta Radiol 52(1):21–28

    Article  PubMed  Google Scholar 

  22. Fangberget A, Nilsen LB, Hole KH et al (2011) Neoadjuvant chemotherapy in breast cancer-response evaluation and prediction of response to treatment using dynamic contrast-enhanced and diffusion-weighted MR imaging. Eur Radiol 21(6):1188–1199

    Article  PubMed  CAS  Google Scholar 

  23. Dongfeng H, Daqing M, Erhu J (2012) Dynamic breast magnetic resonance imaging: pretreatment prediction of tumor response to neoadjuvant chemotherapy. Clin Breast Cancer 12(2):94–101

    Google Scholar 

  24. De Los Santos J, Bernreuter W, Keene K et al (2011) Accuracy of breast magnetic resonance imaging in predicting pathologic response in patients treated with neoadjuvant chemotherapy. Clin Breast Cancer 11(5):312–319

    Article  PubMed  Google Scholar 

  25. Chen JH, Bahri S, Mehta RS et al (2011) Breast cancer: evaluation of response to neoadjuvant chemotherapy with 3.0-T MR imaging. Radiology 261(3):735–743

    Article  PubMed  Google Scholar 

  26. Belli P, Costantini M, Ierardi C et al (2011) Diffusion-weighted imaging in evaluating the response to neoadjuvant breast cancer treatment. Breast J 17(6):610–619

    Article  PubMed  CAS  Google Scholar 

  27. Woodhams R, Kakita S, Hata H et al (2010) Identification of residual breast carcinoma following neoadjuvant chemotherapy: diffusion-weighted imaging—comparison with contrast-enhanced MR imaging and pathologic findings. Radiology 254(2):357–366

    Article  PubMed  Google Scholar 

  28. Park SH, Moon WK, Cho N et al (2010) Diffusion-weighted MR imaging: pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Radiology 257(1):56–63

    Article  PubMed  Google Scholar 

  29. Sharma U, Danishad KK, Seenu V, Jagannathan NR (2009) 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 22(1):104–113

    Article  PubMed  Google Scholar 

  30. Moon HG, Han W, Lee JW et al (2009) Age and HER2 expression status affect MRI accuracy in predicting residual tumor extent after neo-adjuvant systemic treatment. Ann Oncol 20(4):636–641

    Article  PubMed  Google Scholar 

  31. Craciunescu OI, Blackwell KL, Jones EL et al (2009) DCE-MRI parameters have potential to predict response of locally advanced breast cancer patients to neoadjuvant chemotherapy and hyperthermia: a pilot study. Int J Hyperthermia 25(6):405–415

    Article  PubMed  CAS  Google Scholar 

  32. Chen JH, Mehta RS, Nalcioglu O, Su MY (2008) Inflammatory breast cancer after neoadjuvant chemotherapy: can magnetic resonance imaging precisely diagnose the final pathological response? Ann Surg Oncol 15(12):3609–3613

    Article  PubMed  Google Scholar 

  33. Bhattacharyya M, Ryan D, Carpenter R, Vinnicombe S, Gallagher CJ (2008) Using MRI to plan breast-conserving surgery following neoadjuvant chemotherapy for early breast cancer. Br J Cancer 98(2):289–293

    Article  PubMed  CAS  Google Scholar 

  34. Segara D, Krop IE, Garber JE et al (2007) Does MRI predict pathologic tumor response in women with breast cancer undergoing preoperative chemotherapy? J Surg Oncol 96(6):474–480

    Article  PubMed  Google Scholar 

  35. Hsiang DJ, Yamamoto M, Mehta RS, Su MY, Baick CH, Lane KT, Butler JA (2007) Predicting nodal status using dynamic contrast-enhanced magnetic resonance imaging in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy with and without sequential trastuzumab. Arch Surg 142(9):855–861 discussion 860–861

    Article  PubMed  Google Scholar 

  36. Garimella V, Qutob O, Fox JN, Long ED, Chaturvedi A, Turnbull LW, Drew PJ (2007) Recurrence rates after DCE-MRI image guided planning for breast-conserving surgery following neoadjuvant chemotherapy for locally advanced breast cancer patients. Eur J Surg Oncol 33(2):157–161

    Article  PubMed  CAS  Google Scholar 

  37. Belli P, Costantini M, Malaspina C, Magistrelli A, Latorre G, Bonomo L (2006) MRI accuracy in residual disease evaluation in breast cancer patients treated with neoadjuvant chemotherapy. Clin Radiol 61(11):946–953

    Article  PubMed  CAS  Google Scholar 

  38. Akazawa K, Tamaki Y, Taguchi T et al (2006) Preoperative evaluation of residual tumor extent by three-dimensional magnetic resonance imaging in breast cancer patients treated with neoadjuvant chemotherapy. Breast J 12(2):130–137

    Article  PubMed  Google Scholar 

  39. Yeh E, Slanetz P, Kopans DB et al (2005) Prospective comparison of mammography, sonography, and MRI in patients undergoing neoadjuvant chemotherapy for palpable breast cancer. AJR Am J Roentgenol 184(3):868–877

    PubMed  Google Scholar 

  40. Schott AF, Roubidoux MA, Helvie MA et al (2005) Clinical and radiologic assessments to predict breast cancer pathologic complete response to neoadjuvant chemotherapy. Breast Cancer Res Treat 92(3):231–238

    Article  PubMed  CAS  Google Scholar 

  41. Warren RM, Bobrow LG, Earl HM et al (2004) Can breast MRI help in the management of women with breast cancer treated by neoadjuvant chemotherapy? Br J Cancer 90(7):1349–1360

    Article  PubMed  CAS  Google Scholar 

  42. Martincich L, Montemurro F, De Rosa G et al (2004) Monitoring response to primary chemotherapy in breast cancer using dynamic contrast-enhanced magnetic resonance imaging. Breast Cancer Res Treat 83(1):67–76

    Article  PubMed  Google Scholar 

  43. Londero V, Bazzocchi M, Del Frate C, Puglisi F, Di Loreto C, Francescutti G, Zuiani C (2004) Locally advanced breast cancer: comparison of mammography, sonography and MR imaging in evaluation of residual disease in women receiving neoadjuvantchemotherapy. Eur Radiol 14(8):1371–1379

    Article  PubMed  Google Scholar 

  44. Denis F, Desbiez-Bourcier AV, Chapiron C, Arbion F, Body G, Brunereau L (2004) Contrast enhanced magnetic resonance imaging underestimates residual disease following neoadjuvant docetaxel based chemotherapy for breast cancer. Eur J Surg Oncol 30(10):1069–1076

    Article  PubMed  CAS  Google Scholar 

  45. Bodini M, Berruti A, Bottini A et al (2004) Magnetic resonance imaging in comparison to clinical palpation in assessing the response of breast cancer to epirubicin primary chemotherapy. Breast Cancer Res Treat 85(3):211–218

    Article  PubMed  CAS  Google Scholar 

  46. Wasser K, Sinn HP, Fink C et al (2003) Accuracy of tumor size measurement in breast cancer using MRI is influenced by histological regression induced by neoadjuvant chemotherapy. Eur Radiol 13(6):1213–1223

    PubMed  CAS  Google Scholar 

  47. Rosen EL, Blackwell KL, Baker JA et al (2003) Accuracy of MRI in the detection of residual breast cancer after neoadjuvant chemotherapy. AJR Am J Roentgenol 181(5):1275–1282

    PubMed  Google Scholar 

  48. Delille JP, Slanetz PJ, Yeh ED, Halpern EF, Kopans DB, Garrido L (2003) Invasive ductal breast carcinoma response to neoadjuvant chemotherapy: noninvasive monitoring with functional MR imaging pilot study. Radiology 228(1):63–69

    Article  PubMed  Google Scholar 

  49. Cheung YC, Chen SC, Su MY et al (2003) Monitoring the size and response of locally advanced breast cancers to neoadjuvant chemotherapy (weekly paclitaxel and epirubicin) with serial enhanced MRI. Breast Cancer Res Treat 78(1):51–58

    Article  PubMed  CAS  Google Scholar 

  50. Rieber A, Brambs HJ, Gabelmann A, Heilmann V, Kreienberg R, Kühn T (2002) Breast MRI for monitoring response of primary breast cancer to neo-adjuvant chemotherapy. Eur Radiol 12(7):1711–1719

    Article  PubMed  CAS  Google Scholar 

  51. Partridge SC, Gibbs JE, Lu Y, Esserman LJ, Sudilovsky D, Hylton NM (2002) Accuracy of MR imaging for revealing residual breast cancer in patients who have undergone neoadjuvant chemotherapy. AJR Am J Roentgenol 179(5):1193–1199

    PubMed  Google Scholar 

  52. Balu-Maestro C, Chapellier C, Bleuse A, Chanalet I, Chauvel C, Largillier R (2002) Imaging in evaluation of response to neoadjuvant breast cancer treatment benefits of MRI. Breast Cancer Res Treat 72(2):145–152

    Article  PubMed  CAS  Google Scholar 

  53. Esserman L, Kaplan E, Partridge S et al (2001) MRI phenotype is associated with response to doxorubicin and cyclophosphamide neoadjuvant chemotherapy in stage III breast cancer. Ann Surg Oncol 8(6):549–559

    Article  PubMed  CAS  Google Scholar 

  54. Jaeschke R, Guyatt GH, Sackett DL (1994) Users’ guides to the medical literature. III. How to use an article about a diagnostic test. B. What are the results and will they help me in caring for my patients? The Evidence-Based Medicine Working Group. JAMA 271:703–707

    Article  PubMed  CAS  Google Scholar 

  55. Yuan Y, Chen XS, Liu SY, Shen KW (2010) Accuracy of MRI in prediction of pathologic complete remission in breast cancer after preoperative therapy: a meta-analysis. AJR Am J Roentgenol 195(1):260–268

    Article  PubMed  Google Scholar 

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Acknowledgments

Financial support: Shanghai Leading Academic Discipline Project, No. S30203 and Shanghai Jiaotong University School of Medicine Leading Academic Discipline Project.

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Authors and Affiliations

  1. Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, No. 1630, Dongfang Road, Pudong, Shanghai, 200127, China

    Lian-Ming Wu, Hai-Yan Gu, Jia Hua, Jie Chen & Jian-Rong Xu

  2. Department of Radiology, Wayne State University, Detroit, MI, 48201, USA

    Jia-Ni Hu

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  1. Lian-Ming Wu
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Correspondence to Jian-Rong Xu.

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Wu, LM., Hu, JN., Gu, HY. et al. 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 135, 17–28 (2012). https://doi.org/10.1007/s10549-012-2033-5

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