Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial

Change in apparent diffusion coefficient at diffusion-weighted MRI after 12 weeks of therapy is a noninvasive and quantitative imaging biomarker of response in women undergoing neoadjuvant chemotherapy for breast cancer.

Purpose

To determine if the change in tumor apparent diffusion coefficient (ADC) at diffusion-weighted (DW) MRI is predictive of pathologic complete response (pCR) to neoadjuvant chemotherapy for breast cancer.

Materials and Methods

In this prospective multicenter study, 272 consecutive women with breast cancer were enrolled at 10 institutions (from August 2012 to January 2015) and were randomized to treatment with 12 weekly doses of paclitaxel (with or without an experimental agent), followed by 12 weeks of treatment with four cycles of anthracycline. Each woman underwent breast DW MRI before treatment, at early treatment (3 weeks), at midtreatment (12 weeks), and after treatment. Percentage change in tumor ADC from that before treatment (ΔADC) was measured at each time point. Performance for predicting pCR was assessed by using the area under the receiver operating characteristic curve (AUC) for the overall cohort and according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype.

Results

The final analysis included 242 patients with evaluable serial imaging data, with a mean age of 48 years ± 10 (standard deviation); 99 patients had HR-positive (hereafter, HR+)/HER2-negative (hereafter, HER2-) disease, 77 patients had HR-/HER2- disease, 42 patients had HR+/HER2+ disease, and 24 patients had HR-/HER2+ disease. Eighty (33%) of 242 patients experienced pCR. Overall, ΔADC was moderately predictive of pCR at midtreatment/12 weeks (AUC = 0.60; 95% confidence interval [CI]: 0.52, 0.68; P = .017) and after treatment (AUC = 0.61; 95% CI: 0.52, 0.69; P = .013). Across the four disease subtypes, midtreatment ΔADC was predictive only for HR+/HER2- tumors (AUC = 0.76; 95% CI: 0.62, 0.89; P < .001). In a test subset, a model combining tumor subtype and midtreatment ΔADC improved predictive performance (AUC = 0.72; 95% CI: 0.61, 0.83) over ΔADC alone (AUC = 0.57; 95% CI: 0.44, 0.70; P = .032.).

Conclusion

After 12 weeks of therapy, change in breast tumor apparent diffusion coefficient at MRI predicts complete pathologic response to neoadjuvant chemotherapy.

© RSNA, 2018

Online supplemental material is available for this article.

References

  • 1. Chenevert TL, Stegman LD, Taylor JM, et al. Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst 2000;92(24):2029–2036. Crossref, MedlineGoogle Scholar
  • 2. Partridge SC, Nissan N, Rahbar H, Kitsch AE, Sigmund EE. Diffusion-weighted breast MRI: Clinical applications and emerging techniques. J Magn Reson Imaging 2017;45(2):337–355. Crossref, MedlineGoogle Scholar
  • 3. Galbán CJ, Ma B, Malyarenko D, et al. Multi-site clinical evaluation of DW-MRI as a treatment response metric for breast cancer patients undergoing neoadjuvant chemotherapy. PLoS One 2015;10(3):e0122151. Crossref, MedlineGoogle Scholar
  • 4. 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(1):104–113. Crossref, MedlineGoogle Scholar
  • 5. Li XR, Cheng LQ, Liu M, et al. DW-MRI ADC values can predict treatment response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. Med Oncol 2012;29(2):425–431. Crossref, MedlineGoogle Scholar
  • 6. Richard R, Thomassin I, Chapellier M, et al. Diffusion-weighted MRI in pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Eur Radiol 2013;23(9):2420–2431. Crossref, MedlineGoogle Scholar
  • 7. Li X, Abramson RG, Arlinghaus LR, et al. Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer. Invest Radiol 2015;50(4):195–204. Crossref, MedlineGoogle Scholar
  • 8. Liu S, Ren R, Chen Z, et al. Diffusion-weighted imaging in assessing pathological response of tumor in breast cancer subtype to neoadjuvant chemotherapy. J Magn Reson Imaging 2015;42(3):779–787. Crossref, MedlineGoogle Scholar
  • 9. Bufi E, Belli P, Costantini M, et al. Role of the apparent diffusion coefficient in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Clin Breast Cancer 2015;15(5):370–380. Crossref, MedlineGoogle Scholar
  • 10. Manton DJ, Chaturvedi A, Hubbard A, et al. Neoadjuvant chemotherapy in breast cancer: early response prediction with quantitative MR imaging and spectroscopy. Br J Cancer 2006;94(3):427–435 [Published correction appears in Br J Cancer 2006;94(10):1544.]. Crossref, MedlineGoogle Scholar
  • 11. Minarikova L, Bogner W, Pinker K, et al. Investigating the prediction value of multiparametric magnetic resonance imaging at 3 T in response to neoadjuvant chemotherapy in breast cancer. Eur Radiol 2017;27(5):1901–1911. Crossref, MedlineGoogle Scholar
  • 12. 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(3):354–360. Crossref, MedlineGoogle Scholar
  • 13. Chu W, Jin W, Liu D, et al. Diffusion-weighted imaging in identifying breast cancer pathological response to neoadjuvant chemotherapy: a meta-analysis. Oncotarget 2017;9(6):7088–7100. Crossref, MedlineGoogle Scholar
  • 14. DWI in assessing treatment response in patients with breast cancer receiving neoadjuvant chemotherapy (ACRIN6698). https://clinicaltrials.gov/ct2/show/NCT01564368. Accessed July 9, 2018. Google Scholar
  • 15. Rugo HS, Olopade OI, DeMichele A, et al. Adaptive randomization of veliparib-carboplatin treatment in breast cancer. N Engl J Med 2016;375(1):23–34. Crossref, MedlineGoogle Scholar
  • 16. Park JW, Liu MC, Yee D, et al. Adaptive randomization of neratinib in early breast cancer. N Engl J Med 2016;375(1):11–22. Crossref, MedlineGoogle Scholar
  • 17. I-SPY 2 trial: neoadjuvant and personalized adaptive novel agents to treat breast cancer (I-SPY 2). https://clinicaltrials.gov/ct2/show/NCT01042379. Accessed July 9, 2018. Google Scholar
  • 18. ACRIN 6698 trial protocol and imaging materials. https://www.acrin.org/PROTOCOLSUMMARYTABLE/PROTOCOL6698/6698ImagingMaterials.aspx. Accessed July 9, 2018. Google Scholar
  • 19. Stejskal EO, Tanner JE. Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J Chem Phys 1965;42(1):288–292. CrossrefGoogle Scholar
  • 20. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 1986;161(2):401–407. LinkGoogle Scholar
  • 21. Hylton NM, Blume JD, Bernreuter WK, et al. Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy—results from ACRIN 6657/I-SPY trial. Radiology 2012;263(3):663–672. LinkGoogle Scholar
  • 22. Hylton NM, Gatsonis CA, Rosen MA, et al. Neoadjuvant chemotherapy for breast cancer: functional tumor volume by MR imaging predicts recurrence-free survival-results from the ACRIN 6657/CALGB 150007 I-SPY 1 trial. Radiology 2016;279(1):44–55. LinkGoogle Scholar
  • 23. Symmans WF, Peintinger F, Hatzis C, et al. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol 2007;25(28):4414–4422. Crossref, MedlineGoogle Scholar
  • 24. U.S. Food and Drug Administration (FDA). Guidance for industry: pathological complete response in neoadjuvant treatment of high-risk early-stage breast cancer: use as an endpoint to support accelerated approval. https://www.fda.gov/downloads/drugs/guidances/ucm305501.pdf. Accessed July 9, 2018. Google Scholar
  • 25. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44(3):837–845. Crossref, MedlineGoogle Scholar
  • 26. Loo CE, Straver ME, Rodenhuis S, et al. Magnetic resonance imaging response monitoring of breast cancer during neoadjuvant chemotherapy: relevance of breast cancer subtype. J Clin Oncol 2011;29(6):660–666. Crossref, MedlineGoogle Scholar
  • 27. Ojeda-Fournier H, de Guzman J, Hylton N. Breast magnetic resonance imaging for monitoring response to therapy. Magn Reson Imaging Clin N Am 2013;21(3):533–546. Crossref, MedlineGoogle Scholar
  • 28. Park SH, Moon WK, Cho N, et al. Diffusion-weighted MR imaging: pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Radiology 2010;257(1):56–63. LinkGoogle Scholar
  • 29. Iacconi C, Giannelli M, Marini C, 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(2):303–308. Crossref, MedlineGoogle Scholar
  • 30. Fangberget A, Nilsen LB, Hole KH, 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(6):1188–1199. Crossref, MedlineGoogle Scholar
  • 31. Bedair R, Priest AN, Patterson AJ, et al. Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations. Eur Radiol 2017;27(7):2726–2736. Crossref, MedlineGoogle Scholar
  • 32. Che S, Zhao X, Ou Y, et al. Role of the intravoxel incoherent motion diffusion weighted imaging in the pre-treatment prediction and early response monitoring to neoadjuvant chemotherapy in locally advanced breast cancer. Medicine (Baltimore) 2016;95(4):e2420. Crossref, MedlineGoogle Scholar
  • 33. Rahbar H, Kurland BF, Olson ML, et al. Diffusion-weighted breast magnetic resonance imaging: a semiautomated voxel selection technique improves interreader reproducibility of apparent diffusion coefficient measurements. J Comput Assist Tomogr 2016;40(3):428–435. Crossref, MedlineGoogle Scholar
  • 34. Stephen RM, Jha AK, Roe DJ, et al. Diffusion MRI with semi-automated segmentation can serve as a restricted predictive biomarker of the therapeutic response of liver metastasis. Magn Reson Imaging 2015;33(10):1267–1273. Crossref, MedlineGoogle Scholar

Article History

Received: Feb 8 2018
Revision requested: Mar 28 2018
Revision received: July 12 2018
Accepted: July 18 2018
Published online: Sept 04 2018
Published in print: Dec 2018