Skip to main content

Advertisement

SpringerLink
Log in

Contrast-free MRI quantitative parameters for early prediction of pathological response to neoadjuvant chemotherapy in breast cancer

  • Breast
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To assess early changes in synthetic relaxometry after neoadjuvant chemotherapy (NAC) for breast cancer and establish a model with contrast-free quantitative parameters for early prediction of pathological response.

Methods

From March 2019 to January 2021, breast MRI were performed for a primary cohort of women with breast cancer before (n = 102) and after the first (n = 93) and second (n = 90) cycle of NAC. Tumor size, synthetic relaxometry (T1/T2 relaxation time [T1/T2], proton density), and ADC were obtained, and the changes after treatment were calculated. Prediction models were established by multivariate logistic regression; evaluated with discrimination, calibration, and clinical application; and compared with Delong tests, net reclassification (NRI), and integrated discrimination index (IDI). External validation was performed from February to June 2021 with an independent cohort of 35 patients.

Results

In the primary cohort, all parameters changed after early treatment. Synthetic relaxometry decreased to a greater degree in major histologic responders (MHR, Miller–Payne G4-5) compared with non-MHR (Miller–Payne G1-3). A model combining ADC after treatment, changes in T1 and tumor size, and cancer subtype achieved the highest AUC after the first (primary/validation cohort, 0.83/0.82) and second cycles (primary/validation cohort, 0.85/0.84). No difference of AUC (p ≥ 0.27), NRI (p ≥ 0.31), and IDI (p ≥ 0.32) was found between models with different cycles and size-measured sequences. Model calibration and decision curves demonstrated a good fitness and clinical benefit, respectively.

Conclusions

Early reduction in synthetic relaxometry indicated pathological response to NAC. Contrast-free T1 and ADC combined with size and cancer subtype predicted effectively pathological response after one NAC cycle.

Key Points

• Synthetic MRI relaxometry changed after early neoadjuvant chemotherapy, which demonstrated pathological response for mass-like breast cancers.

• Contrast-free quantitative parameters including T1 relaxation time and apparent diffusion coefficient, combined with tumor size and cancer subtype, stratified major histologic responders.

• A contrast-free model predicted an early pathological response after the first treatment cycle of neoadjuvant chemotherapy.

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
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the ROC curve

DCE:

Dynamic contrast enhanced

DWI:

Diffusion-weighted imaging

MHR:

Major histologic responders

NAC:

Neoadjuvant chemotherapy

PD:

Proton density

ROC:

Receiver operating characteristic

T1:

T1 relaxation time

T2:

T2 relaxation time

References

  1. Thompson AM, Moulder-Thompson SL (2012) Neoadjuvant treatment of breast cancer. Ann Oncol 23(Suppl 10):×231–×236

    Article  PubMed  PubMed Central  Google Scholar 

  2. Derks MGM, van de Velde CJH (2018) Neoadjuvant chemotherapy in breast cancer: more than just downsizing. Lancet Oncol 19:2–3

    Article  PubMed  Google Scholar 

  3. Haque W, Verma V, Hatch S, Suzanne Klimberg V, Brian Butler E, Teh BS (2018) Response rates and pathologic complete response by breast cancer molecular subtype following neoadjuvant chemotherapy. Breast Cancer Res Treat 170:559–567

    Article  CAS  PubMed  Google Scholar 

  4. Cortazar P, Zhang L, Untch M et al (2014) Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384:164–172

    Article  PubMed  Google Scholar 

  5. Mann RM, Kuhl CK, Kinkel K, Boetes C (2008) Breast MRI: guidelines from the European Society of Breast Imaging. Eur Radiol 18:1307–1318

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247

    Article  CAS  PubMed  Google Scholar 

  7. Li X, Arlinghaus LR, Ayers GD et al (2014) DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: pilot study findings. Magn Reson Med 71:1592–1602

    Article  PubMed  Google Scholar 

  8. Padhani AR, Hayes C, Assersohn L et al (2006) Prediction of clinicopathologic response of breast cancer to primary chemotherapy at contrast-enhanced MR imaging: initial clinical results. Radiology 239:361–374

    Article  PubMed  Google Scholar 

  9. Ramalho J, Semelka RC, Ramalho M, Nunes RH, AlObaidy M, Castillo M (2016) Gadolinium-based contrast agent accumulation and toxicity: an update. AJNR Am J Neuroradiol 37:1192–1198

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Conte G, Preda L, Cocorocchio E et al (2017) Signal intensity change on unenhanced T1-weighted images in dentate nucleus and globus pallidus after multiple administrations of gadoxetate disodium: an intraindividual comparative study. Eur Radiol 27:4372–4378

    Article  PubMed  Google Scholar 

  11. Jung JW, Kang HR, Kim MH et al (2012) Immediate hypersensitivity reaction to gadolinium-based MR contrast media. Radiology 264:414–422

    Article  PubMed  Google Scholar 

  12. 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 289:618–627

    Article  PubMed  Google Scholar 

  13. Yuan L, Li JJ, Li CQ et al (2018) Diffusion-weighted MR imaging of locally advanced breast carcinoma: the optimal time window of predicting the early response to neoadjuvant chemotherapy. Cancer Imaging 18:38

    Article  PubMed  PubMed Central  Google Scholar 

  14. Pereira NP, Curi C, Osorio C et al (2019) Diffusion-weighted magnetic resonance imaging of patients with breast cancer following neoadjuvant chemotherapy provides early prediction of pathological response - a prospective study. Sci Rep 9:16372

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Cavallo Marincola B, Telesca M, Zaccagna F et al (2019) Can unenhanced MRI of the breast replace contrast-enhanced MRI in assessing response to neoadjuvant chemotherapy? Acta Radiol 60:35–44

    Article  PubMed  Google Scholar 

  16. Liu L, Yin B, Shek K et al (2018) Role of quantitative analysis of T2 relaxation time in differentiating benign from malignant breast lesions. J Int Med Res 46:1928–1935

    Article  PubMed  PubMed Central  Google Scholar 

  17. Seo M, Ryu JK, Jahng GH et al (2017) Estimation of T2* relaxation time of breast cancer: correlation with clinical, imaging and pathological features. Korean J Radiol 18:238–248

    Article  PubMed  PubMed Central  Google Scholar 

  18. Tan PC, Pickles MD, Lowry M, Manton DJ, Turnbull LW (2008) Lesion T(2) relaxation times and volumes predict the response of malignant breast lesions to neoadjuvant chemotherapy. Magn Reson Imaging 26:26–34

    Article  PubMed  Google Scholar 

  19. Liu L, Yin B, Geng DY, Lu YP, Peng WJ (2016) Changes of T2 relaxation time from neoadjuvant chemotherapy in breast cancer lesions. Iran J Radiol 13:e24014

    PubMed  PubMed Central  Google Scholar 

  20. Jung Y, Gho SM, Back SN, Ha T, Kang DK, Kim TH (2018) The feasibility of synthetic MRI in breast cancer patients: comparison of T2 relaxation time with multiecho spin echo T2 mapping method. Br J Radiol. https://doi.org/10.1259/bjr.20180479:20180479

  21. Tanenbaum LN, Tsiouris AJ, Johnson AN et al (2017) Synthetic MRI for clinical neuroimaging: results of the magnetic resonance image compilation (MAGiC) prospective, multicenter,multireader trial. AJNR Am J Neuroradiol 38:1103–1110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Jiang Y, Yu L, Luo X et al (2020) Quantitative synthetic MRI for evaluation of the lumbar intervertebral disk degeneration in patients with chronic low back pain. Eur J Radiol 124:108858

    Article  PubMed  Google Scholar 

  23. Cui Y, Han S, Liu M et al (2020) Diagnosis and grading of prostate cancer by relaxation maps from synthetic MRI. J Magn Reson Imaging 52:552–564

    Article  PubMed  Google Scholar 

  24. Hammond ME, Hayes DF, Dowsett M et al (2010) American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol 28:2784–2795

    Article  PubMed  PubMed Central  Google Scholar 

  25. Wolff AC, Hammond MEH, Allison KH et al (2018) Human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline focused update. J Clin Oncol 36:2105–2122

    Article  CAS  PubMed  Google Scholar 

  26. Bustreo S, Osella-Abate S, Cassoni P et al (2016) Optimal Ki67 cut-off for luminal breast cancer prognostic evaluation: a large case series study with a long-term follow-up. Breast Cancer Res Treat 157:363–371

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Curigliano G, Burstein HJ, Winer EP et al (2017) De-escalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017. Ann Oncol 28:1700–1712

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Gradishar WJ, Anderson BO, Balassanian R et al (2018) Breast cancer, Version 4.2017, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 16:310–320

    Article  PubMed  Google Scholar 

  29. Breast cancer professional committee of Chinese Anti-cancer Association (2019) Guidelines and standards for the diagnosis and treatment of breast cancer by the Chinese Anti-Cancer Association (2019 Edition). Chin J Cancer 29:609–680

    Google Scholar 

  30. Morrison CK, Henze Bancroft LC, DeMartini WB et al (2017) Novel high spatiotemporal resolution versus standard-of-care dynamic contrast-enhanced breast MRI: comparison of image quality. Invest Radiol 52:198–205

    Article  PubMed  PubMed Central  Google Scholar 

  31. Ogston KN, Miller ID, Payne S et al (2003) A new histological grading system to assess response of breast cancers to primary chemotherapy: prognostic significance and survival. Breast 12:320–327

    Article  PubMed  Google Scholar 

  32. Yu Y, Jiang Q, Miao Y et al (2010) Quantitative analysis of clinical dynamic contrast-enhanced MR imaging for evaluating treatment response in human breast cancer. Radiology 257:47–55

    Article  PubMed  PubMed Central  Google Scholar 

  33. Vickers AJ, Elkin EB (2006) Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 26:565–574

    Article  PubMed  PubMed Central  Google Scholar 

  34. Zormpas-Petridis K, Poon E, Clarke M et al (2020) Noninvasive MRI native T1 mapping detects response to MYCN-targeted therapies in the Th-MYCN model of neuroblastoma. Cancer Res 80:3424–3435

    Article  CAS  PubMed  Google Scholar 

  35. Weidensteiner C, Allegrini PR, Sticker-Jantscheff M, Romanet V, Ferretti S, McSheehy PM (2014) Tumour T1 changes in vivo are highly predictive of response to chemotherapy and reflect the number of viable tumour cells--a preclinical MR study in mice. BMC Cancer 14:88

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. McSheehy PM, Weidensteiner C, Cannet C et al (2010) Quantified tumor t1 is a generic early-response imaging biomarker for chemotherapy reflecting cell viability. Clin Cancer Res 16:212–225

    Article  CAS  PubMed  Google Scholar 

  37. Calamante F, Lythgoe MF, Pell GS et al (1999) Early changes in water diffusion, perfusion, T1, and T2 during focal cerebral ischemia in the rat studied at 8.5 T. Magn Reson Med 41:479–485

    Article  CAS  PubMed  Google Scholar 

  38. Kerr JF, Wyllie AH, Currie AR (1972) Apoptosis: a basic biological phenomenon with wide-ranging implications in tissue kinetics. Br J Cancer 26:239–257

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Duvvuri U, Poptani H, Feldman M et al (2001) Quantitative T1rho magnetic resonance imaging of RIF-1 tumors in vivo: detection of early response to cyclophosphamide therapy. Cancer Res 61:7747–7753

    CAS  PubMed  Google Scholar 

  40. Ellingson BM, Cloughesy TF, Lai A et al (2012) Quantification of edema reduction using differential quantitative T2 (DQT2) relaxometry mapping in recurrent glioblastoma treated with bevacizumab. J Neurooncol 106:111–119

    Article  CAS  PubMed  Google Scholar 

  41. Hattingen E, Jurcoane A, Daneshvar K et al (2013) Quantitative T2 mapping of recurrent glioblastoma under bevacizumab improves monitoring for non-enhancing tumor progression and predicts overall survival. Neuro Oncol 15:1395–1404

  42. Li X, Abramson RG, Arlinghaus LR et al (2015) Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer. Invest Radiol 50:195–204

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Minarikova L, Bogner W, Pinker K et al (2017) Investigating the prediction value of multiparametric magnetic resonance imaging at 3 T in response to neoadjuvant chemotherapy in breast cancer. Eur Radiol 27:1901–1911

    Article  PubMed  Google Scholar 

  44. 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 263:663–672

    Article  PubMed  PubMed Central  Google Scholar 

  45. Belli P, Bufi E, Bonatesta A et al (2016) Unenhanced breast magnetic resonance imaging: detection of breast cancer. Eur Rev Med Pharmacol Sci 20:4220–4229

    CAS  PubMed  Google Scholar 

  46. Rizzo V, Moffa G, Kripa E, Caramanico C, Pediconi F, Galati F (2021) Preoperative staging in breast cancer: intraindividual comparison of unenhanced MRI combined with digital breast tomosynthesis and dynamic contrast enhanced-MRI. Front Oncol 11:661945

    Article  PubMed  PubMed Central  Google Scholar 

  47. Kul S, Metin Y, Kul M, Metin N, Eyuboglu I, Ozdemir O (2018) Assessment of breast mass morphology with diffusion-weighted MRI: beyond apparent diffusion coefficient. J Magn Reson Imaging 48:1668–1677

    Article  PubMed  Google Scholar 

  48. Chinese expert group on neoadjuvant therapy for breast cancer (2019) Expert consensus on neoadjuvant therapy for breast cancer in china (2019 edition). Chin J Cancer 29:390–400

    Google Scholar 

  49. Fujioka T, Mori M, Oyama J et al (2021) Investigating the image quality and utility of synthetic MRI in the breast. Magn Reson Med Sci 20:431–438

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank Fei Bie and Yan Guo from GE Healthcare for the technical support, and AiMi Academic Services (www.aimieditor.com) for the English language editing and review services.

Funding

This study has received funding from the National Financial Appropriation Research Project [grant number 2017YFC1309100], National Scientific Foundation of China [grant number 81971695], and the "Set Sail” Project of the First Affiliated Hospital of China Medical University [grant number 36028].

Author information

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China

    Siyao Du, Si Gao, Ruimeng Zhao, Xixun Qi, Shu Li, Jibin Cao & Lina Zhang

  2. Department of Epidemiology and Medical Statistics, School of Public Health, China Medical University, No. 77, Puhe Road, Shenyang North Xin Area, Shenyang City, 110122, Liaoning Province, China

    Hongbo Liu

  3. Department of Pathology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China

    Yan Wang

Authors
  1. Siyao Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Si Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruimeng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongbo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xixun Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jibin Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Lina Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lina Zhang.

Ethics declarations

Ethics approval

Institutional Review Board approval was obtained from the Ethics Committee of Medical Science Research in the First Affiliated Hospital of China Medical University (No. 2019-33-2).

Informed Consent

Written informed consent was obtained from all subjects (patients) in this study.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Guarantor

The scientific guarantor of this publication is Lina Zhang.

Statistics and Biometry

One of the authors (Hongbo Liu) has significant statistical expertise.

Methodology

  • prospective

  • diagnostic or prognostic study

  • performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

ESM 1

(DOCX 54 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, S., Gao, S., Zhao, R. et al. Contrast-free MRI quantitative parameters for early prediction of pathological response to neoadjuvant chemotherapy in breast cancer. Eur Radiol 32, 5759–5772 (2022). https://doi.org/10.1007/s00330-022-08667-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-022-08667-w

Keywords

Search

Navigation