MRI Performance in Detecting pCR After Neoadjuvant Chemotherapy by Molecular Subtype of Breast Cancer

  • Nancy Yu
  • Vivian W. Y. Leung
  • Sarkis MeterissianEmail author
Scientific Review



MRI performance in detecting pathologic complete response (pCR) post-neoadjuvant chemotherapy (NAC) in breast cancer has been previously explored. However, since tumor response varies by molecular subtype, it is plausible that imaging performance also varies. Therefore, we performed a literature review on subtype-specific MRI performance in detecting pCR post-NAC.


Two reviewers searched Cochrane, PubMed, and EMBASE for articles published between 2013 and 2018 that examined MRI performance in detecting pCR post-NAC. After filtering, ten primary research articles were included. Statistical metrics, such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were extracted per study for triple negative, HR+/HER2−, and HER2+ patients.


Ten studies involving 2310 patients were included. In triple negative breast cancer, MRI showed NPV (58–100%) and PPV (72.7–94.7%) across 446 patients and sensitivity (45.5–100%) and specificity (49–94.4%) in 375 patients. In HR+/HER2− breast cancer patients, MRI showed NPV (29.4–100%) and PPV (21.4–95.1%) across 851 patients and sensitivity (43–100%) and specificity (45–93%) across 780 patients. In HER2+-enriched subtype, MRI showed NPV (62–94.6%) and PPV (34.9–72%) in 243 patients and sensitivity (36.2–83%) and specificity (47–90%) in 255 patients.


MRI accuracy in detecting pCR post-NAC by subtype is not as consistent, nor as high, as individual studies suggest. Larger studies using standardized pCR definition with appropriate timing of surgery and MRI need to be conducted. This study has shown that MRI is in fact not an accurate prediction of pCR, and thus, clinicians may need to rely on other approaches such as biopsies of the tumor bed.


Compliance with ethical standards

Conflict of interests

The authors declare that they have no conflict of interest.

Supplementary material

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Supplementary material 1 (PNG 145 kb)


  1. 1.
    Choi EK, Yoo IR, Kim SH et al (2018) The value of pre- and post-neoadjuvant chemotherapy F-18 FDG PET/CT scans in breast cancer: comparison with MRI. Acta Radiol 59:41–49CrossRefGoogle Scholar
  2. 2.
    Gu YL, Pan SM, Ren J et al (2017) Role of magnetic resonance imaging in detection of pathologic complete remission in breast cancer patients treated with neoadjuvant chemotherapy: a meta-analysis. Clin Breast Cancer 17:245–255CrossRefGoogle Scholar
  3. 3.
    Kim SY, Cho N, Shin SU et al (2018) Contrast-enhanced MRI after neoadjuvant chemotherapy of breast cancer: lesion-to-background parenchymal signal enhancement ratio for discriminating pathological complete response from minimal residual tumour. Eur Radiol 28:2986–2995CrossRefGoogle Scholar
  4. 4.
    Schaefgen B, Mati M, Sinn HP et al (2016) Can routine imaging after neoadjuvant chemotherapy in breast cancer predict pathologic complete response? Ann Surg Oncol 23:789–795CrossRefGoogle Scholar
  5. 5.
    Schmitz AMT, Teixeira SC, Pengel KE et al (2017) Monitoring tumor response to neoadjuvant chemotherapy using MRI and 18F-FDG PET/CT in breast cancer subtypes. PLoS ONE 12:e0176782CrossRefGoogle Scholar
  6. 6.
    Sun YS, He YJ, Li J et al (2016) Predictive value of DCE-MRI for early evaluation of pathological complete response to neoadjuvant chemotherapy in resectable primary breast cancer: a single-center prospective study. Breast 30:80–86CrossRefGoogle Scholar
  7. 7.
    Sheikhbahaei S, Trahan TJ, Xiao J et al (2016) FDG-PET/CT and MRI for evaluation of pathologic response to neoadjuvant chemotherapy in patients with breast cancer: a meta-analysis of diagnostic accuracy studies. Oncologist 21:931–939CrossRefGoogle Scholar
  8. 8.
    Choi WJ, Kim WK, Shin HJ et al (2018) Evaluation of the tumor response after neoadjuvant chemotherapy in breast cancer patients: correlation between dynamic contrast-enhanced magnetic resonance imaging and pathologic tumor cellularity. Clin Breast Cancer 18:e115–e121CrossRefGoogle Scholar
  9. 9.
    Chen L, Yang Q, Bao J et al (2017) Direct comparison of PET/CT and MRI to predict the pathological response to neoadjuvant chemotherapy in breast cancer: a meta-analysis. Sci Rep 7:8479CrossRefGoogle Scholar
  10. 10.
    De Los Santos JF, Cantor A, Amos KD et al (2013) Magnetic resonance imaging as a predictor of pathologic response in patients treated with neoadjuvant systemic treatment for operable breast cancer. Translational breast cancer research consortium trial 017. Cancer 119:1776–1783CrossRefGoogle Scholar
  11. 11.
    Eun NL, Gweon HM, Son EJ et al (2018) Pretreatment MRI features associated with diagnostic accuracy of post-treatment MRI after neoadjuvant chemotherapy. Clin Radiol 73:676CrossRefGoogle Scholar
  12. 12.
    Fatayer H, Sharma N, Manuel D et al (2016) Serial MRI scans help in assessing early response to neoadjuvant chemotherapy and tailoring breast cancer treatment. Eur J Surg Oncol 42:965–972CrossRefGoogle Scholar
  13. 13.
    Dialani V, Chadashvili T, Slanetz PJ (2015) Role of imaging in neoadjuvant therapy for breast cancer. Ann Surg Oncol 22:1416–1424CrossRefGoogle Scholar
  14. 14.
    Hylton NM, Blume JD, Bernreuter WK et al (2012) Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy—results from ACRIN 6657/I-SPY TRIAL. Radiology 263:663–672CrossRefGoogle Scholar
  15. 15.
    Chagpar AB, Middleton LP, Sahin AA et al (2006) Accuracy of physical examination, ultrasonography, and mammography in predicting residual pathologic tumor size in patients treated with neoadjuvant chemotherapy. Ann Surg 243:257–264CrossRefGoogle Scholar
  16. 16.
    Hollingsworth AB, Stough RG, O’Dell CA et al (2008) Breast magnetic resonance imaging for preoperative locoregional staging. Am J Surg 196:389–397CrossRefGoogle Scholar
  17. 17.
    Lobbes MB, Prevos R, Smidt M et al (2013) The role of magnetic resonance imaging in assessing residual disease and pathologic complete response in breast cancer patients receiving neoadjuvant chemotherapy: a systematic review. Insights Imaging 4:163–175CrossRefGoogle Scholar
  18. 18.
    Marinovich ML, Houssami N, Macaskill P et al (2013) Meta-analysis of magnetic resonance imaging in detecting residual breast cancer after neoadjuvant therapy. J Natl Cancer Inst 105:321–333CrossRefGoogle Scholar
  19. 19.
    Marinovich ML, Macaskill P, Irwig L et al (2015) Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: individual patient data meta-analysis. BMC Cancer 15:662CrossRefGoogle Scholar
  20. 20.
    Namura M, Tsunoda H, Yagata H et al (2018) Discrepancies between pathological tumor responses and estimations of complete response by magnetic resonance imaging after neoadjuvant chemotherapy differ by breast cancer subtype. Clin Breast Cancer 18:128–134CrossRefGoogle Scholar
  21. 21.
    Fukuda T, Horii R, Gomi N et al (2016) Accuracy of magnetic resonance imaging for predicting pathological complete response of breast cancer after neoadjuvant chemotherapy: association with breast cancer subtype. Springerplus 5:152CrossRefGoogle Scholar
  22. 22.
    Vriens BE, de Vries B, Lobbes MB et al (2016) Ultrasound is at least as good as magnetic resonance imaging in predicting tumour size post-neoadjuvant chemotherapy in breast cancer. Eur J Cancer 52:67–76CrossRefGoogle Scholar
  23. 23.
    Okamoto S, Yamada T, Kanemaki Y, Kojima Y, Tsugawa K, Nakajima Y (2016) Magnetic resonance examination to predict pathological complete response following neoadjuvant chemotherapy: when is it appropriate for HER2-positive and triple-negative breast cancers? Breast Cancer 23:789–796CrossRefGoogle Scholar
  24. 24.
    Kim MJ, Kim EK, Park S, Moon HJ, Kim SI, Park BW (2015) Evaluation with 3.0-T MR imaging: predicting the pathological response of triple-negative breast cancer treated with anthracycline and taxane neoadjuvant chemotherapy. Acta Radiol 56:1069–1077CrossRefGoogle Scholar
  25. 25.
    Michishita S, Kim SJ, Shimazu K, Sota Y, Naoi Y, Maruyama N, Kagara N, Shimoda M, Shimomura A, Noguchi S (2015) Prediction of pathological complete response to neoadjuvant chemotherapy by magnetic resonance imaging in breast cancer patients. Breast 24:159–165CrossRefGoogle Scholar
  26. 26.
    Charehbili A, Wasser MN, Smit VT et al (2014) Accuracy of MRI for treatment response assessment after taxane- and anthracycline-based neoadjuvant chemotherapy in HER2-negative breast cancer. Eur J Surg Oncol 40:1216–1221CrossRefGoogle Scholar
  27. 27.
    Kaise H, Shimizu F, Akazawa K, Hasegawa Y, Horiguchi J, Miura D, Kohno N, Ishikawa T (2018) Prediction of pathological response to neoadjuvant chemotherapy in breast cancer patients by imaging. J Surg Res 225:175–180CrossRefGoogle Scholar
  28. 28.
    Lindenberg MA, Miquel-Cases A, Retel VP, Sonke GS, Wesseling J, Stokkel MPM, van Harten WH (2017) Imaging performance in guiding response to neoadjuvant therapy according to breast cancer subtypes: a systematic literature review. Crit Rev Oncol Hematol 112:198–207CrossRefGoogle Scholar
  29. 29.
    Kurosumi M (2006) Significance and problems in evaluations of pathological responses to neoadjuvant therapy for breast cancer. Breast Cancer 13:254–259CrossRefGoogle Scholar
  30. 30.
    Li W, Arasu V, Newitt DC et al (2016) Effect of MR imaging contrast thresholds on prediction of neoadjuvant chemotherapy response in breast cancer subtypes: a subgroup analysis of the ACRIN 6657/I-SPY 1 TRIAL. Tomography 2:378–387CrossRefGoogle Scholar
  31. 31.
    Partridge SC, Zhang Z, Newitt DC et al (2018) Diffusion-weighted MRI Findings predict pathologic response in neoadjuvant treatment of breast cancer: the ACRIN 6698 multicenter trial. Radiology 289:618–627CrossRefGoogle Scholar
  32. 32.
    Scheel JR, Kim E, Partridge SC et al (2018) c. AJR Am J Roentgenol 210:1376–1385CrossRefGoogle Scholar
  33. 33.
    Weber JJ, Jochelson MS, Eaton A et al (2017) MRI and prediction of pathologic complete response in the breast and axilla after neoadjuvant chemotherapy for breast cancer. J Am Coll Surg 225:740–746CrossRefGoogle Scholar

Copyright information

© Société Internationale de Chirurgie 2019

Authors and Affiliations

  • Nancy Yu
    • 1
  • Vivian W. Y. Leung
    • 1
  • Sarkis Meterissian
    • 1
    • 2
    • 3
    • 4
    Email author
  1. 1.Faculty of MedicineMcGill UniversityMontréalCanada
  2. 2.Department of OncologyMcGill UniversityMontréalCanada
  3. 3.Department of SurgeryMcGill UniversityMontréalCanada
  4. 4.Research Institute of MUHCMontrealCanada

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