CME Article
Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer
Beatriu Reig MD, MPH,
Laura Heacock MD,
Alana Lewin MD,
Nariya Cho MD,
Linda Moy MD,
Corresponding Author
Beatriu Reig MD, MPH
Department of Radiology, New York University Grossman School of Medicine, New York, New York USA
New York University Laura and Isaac Perlmutter Cancer Center, New York, New York USA
Address reprint request to: B.R., 160 East 34th Street, 3rd Floor, New York, New York 10016, USA. E-mail: [email protected]Search for more papers by this authorLaura Heacock MD
Department of Radiology, New York University Grossman School of Medicine, New York, New York USA
New York University Laura and Isaac Perlmutter Cancer Center, New York, New York USA
Search for more papers by this authorAlana Lewin MD
Department of Radiology, New York University Grossman School of Medicine, New York, New York USA
New York University Laura and Isaac Perlmutter Cancer Center, New York, New York USA
Search for more papers by this authorNariya Cho MD
Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
Search for more papers by this authorLinda Moy MD
Department of Radiology, New York University Grossman School of Medicine, New York, New York USA
New York University Laura and Isaac Perlmutter Cancer Center, New York, New York USA
Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York USA
Center for Advanced Imaging Innovation and Research (CAI2 R), New York University Grossman School of Medicine, New York, New York USA
Search for more papers by this authorBeatriu Reig MD, MPH,
Laura Heacock MD,
Alana Lewin MD,
Nariya Cho MD,
Linda Moy MD,
Corresponding Author
Beatriu Reig MD, MPH
Department of Radiology, New York University Grossman School of Medicine, New York, New York USA
New York University Laura and Isaac Perlmutter Cancer Center, New York, New York USA
Address reprint request to: B.R., 160 East 34th Street, 3rd Floor, New York, New York 10016, USA. E-mail: [email protected]Search for more papers by this authorLaura Heacock MD
Department of Radiology, New York University Grossman School of Medicine, New York, New York USA
New York University Laura and Isaac Perlmutter Cancer Center, New York, New York USA
Search for more papers by this authorAlana Lewin MD
Department of Radiology, New York University Grossman School of Medicine, New York, New York USA
New York University Laura and Isaac Perlmutter Cancer Center, New York, New York USA
Search for more papers by this authorNariya Cho MD
Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
Search for more papers by this authorLinda Moy MD
Department of Radiology, New York University Grossman School of Medicine, New York, New York USA
New York University Laura and Isaac Perlmutter Cancer Center, New York, New York USA
Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York USA
Center for Advanced Imaging Innovation and Research (CAI2 R), New York University Grossman School of Medicine, New York, New York USA
Search for more papers by this authorAbstract
The goals of imaging after neoadjuvant therapy for breast cancer are to monitor the response to therapy and facilitate surgical planning. MRI has been found to be more accurate than mammography, ultrasound, or clinical exam in evaluating treatment response. However, MRI may both overestimate and underestimate residual disease. The accuracy of MRI is dependent on tumor morphology, histology, shrinkage pattern, and molecular subtype. Emerging MRI techniques that combine functional information such as diffusion, metabolism, and hypoxia may improve MR accuracy. In addition, machine-learning techniques including radiomics and radiogenomics are being studied with the goal of predicting response on pretreatment imaging. This article comprehensively reviews response assessment on breast MRI and highlights areas of ongoing research.
Level of Evidence
3
Technical Efficacy Stage
3 J. MAGN. RESON. IMAGING 2020;52:1587–1606.
References
- 1King TA, Morrow M. Surgical issues in patients with breast cancer receiving neoadjuvant chemotherapy. Nat Rev Clin Oncol 2015; 12(6): 335-343.
- 2Cortazar P, Zhang L, Untch M, et al. Pathological complete response and long-term clinical benefit in breast cancer: The CTNeoBC pooled analysis. Lancet 2014; 384(9938): 164-172.
- 3 National Comprehensive Cancer Network. Breast cancer (version 2.2020). Accessed March 1, 2020. Available from: https://www.nccn.org/professionals/physician_gls/pdf/breast_blocks.pdf.
- 4Haque W, Verma V, Hatch S, Suzanne Klimberg V, Brian Butler E, Teh BS. Response rates and pathologic complete response by breast cancer molecular subtype following neoadjuvant chemotherapy. Breast Cancer Res Treat 2018; 170(3): 559-567.
- 5Sparano JA, Gray RJ, Makower DF, et al. Prospective validation of a 21-gene expression assay in breast cancer. N Engl J Med 2015; 373(21): 2005-2014.
- 6Marinovich ML, Macaskill P, Irwig L, et al. Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: Individual patient data meta-analysis. BMC Cancer 2015; 15: 662.
- 7Scheel JR, Kim E, Partridge SC, et al. MRI, clinical examination, and mammography for preoperative assessment of residual disease and pathologic complete response after neoadjuvant chemotherapy for breast cancer: ACRIN 6657 trial. AJR Am J Roentgenol 2018; 210(6): 1376-1385.
- 8Mann RM, Cho N, Moy L. Breast MRI: State of the art. Radiology 2019; 292(3): 520-536.
- 9AMB, Greene FL, Edge SB, et al. AJCC cancer staging manual. 8th ed. New York: Springer; 2017.
- 10Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer 2009; 45(2): 228-247.
- 11Abramson RG, Arlinghaus LR, Weis JA, et al. Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy. Breast Cancer (Dove Med Press) 2012; 2012(4): 139-154.
- 12Hylton 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.
- 13Hylton 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.
- 14Didonato R, Shapiro N, Koenigsberg T, D'Alfonso T, Jaffer S, Fineberg S. Invasive mucinous carcinoma of the breast and response patterns after neoadjuvant chemotherapy (NAC). Histopathology 2018; 72(6): 965-973.
- 15Kim SY, Cho N, Shin SU, et al. 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 2018; 28(7): 2986-2995.
- 16Ah-See ML, Makris A, Taylor NJ, et al. Early changes in functional dynamic magnetic resonance imaging predict for pathologic response to neoadjuvant chemotherapy in primary breast cancer. Clin Cancer Res 2008; 14(20): 6580-6589.
- 17Pickles MD, Lowry M, Manton DJ, Gibbs P, Turnbull LW. Role of dynamic contrast enhanced MRI in monitoring early response of locally advanced breast cancer to neoadjuvant chemotherapy. Breast Cancer Res Treat 2005; 91(1): 1-10.
- 18Dogan BE, Yuan Q, Bassett R, et al. Comparing the performances of magnetic resonance imaging size vs pharmacokinetic parameters to predict response to neoadjuvant chemotherapy and survival in patients with breast cancer. Curr Probl Diagn Radiol 2019; 48(3): 235-240.
- 19Li X, Arlinghaus LR, Ayers GD, et al. DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: Pilot study findings. Magn Reson Med 2014; 71(4): 1592-1602.
- 20Yu HJ, Chen JH, Mehta RS, Nalcioglu O, Su MY. MRI measurements of tumor size and pharmacokinetic parameters as early predictors of response in breast cancer patients undergoing neoadjuvant anthracycline chemotherapy. J Magn Reson Imaging 2007; 26(3): 615-623.
- 21Jun W, Cong W, Xianxin X, Daqing J. Meta-analysis of quantitative dynamic contrast-enhanced MRI for the assessment of neoadjuvant chemotherapy in breast cancer. Am Surg 2019; 85(6): 645-653.
- 22Marinovich ML, Sardanelli F, Ciatto S, et al. Early prediction of pathologic response to neoadjuvant therapy in breast cancer: Systematic review of the accuracy of MRI. Breast 2012; 21(5): 669-677.
- 23Kang H, Hainline A, Arlinghaus LR, et al. Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: Preliminary results. J Med Imaging (Bellingham) 2018; 5(1):011015.
- 24Li 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.
- 25Yuan L, Li JJ, Li CQ, et al. Diffusion-weighted MR imaging of locally advanced breast carcinoma: The optimal time window of predicting the early response to neoadjuvant chemotherapy. Cancer Imaging 2018; 18(1): 38.
- 26Goorts B, Dreuning KMA, Houwers JB, et al. MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer. Breast Cancer Res 2018; 20(1): 34.
- 27Chen JH, Bahri S, Mehta RS, et al. Impact of factors affecting the residual tumor size diagnosed by MRI following neoadjuvant chemotherapy in comparison to pathology. J Surg Oncol 2014; 109(2): 158-167.
- 28Fowler AM, Mankoff DA, Joe BN. Imaging neoadjuvant therapy response in breast cancer. Radiology 2017; 285(2): 358-375.
- 29Hylton NM. Residual disease after neoadjuvant therapy for breast cancer: Can MRI help? Radiology 2018; 289(2): 335-336.
- 30US Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research. Guidance for industry: Pathological complete response in neoadjuvant treatment of high-risk early-stage breast cancer (version 2014). Accessed March 1, 2020. Available from: http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm.
- 31Kim SY, Cho N, Park IA, et al. Dynamic contrast-enhanced breast MRI for evaluating residual tumor size after neoadjuvant chemotherapy. Radiology 2018; 289(2): 327-334.
- 32Shin SU, Cho N, Lee HB, et al. Neoadjuvant chemotherapy and surgery for breast cancer: Preoperative MRI features associated with local recurrence. Radiology 2018; 289(1): 30-38.
- 33Mukhtar RA, Yau C, Rosen M, et al. Clinically meaningful tumor reduction rates vary by prechemotherapy MRI phenotype and tumor subtype in the I-SPY 1 TRIAL (CALGB 150007/150012; ACRIN 6657). Ann Surg Oncol 2013; 20(12): 3823-3830.
- 34Ko ES, Han BK, Kim RB, et al. Analysis of factors that influence the accuracy of magnetic resonance imaging for predicting response after neoadjuvant chemotherapy in locally advanced breast cancer. Ann Surg Oncol 2013; 20(8): 2562-2568.
- 35Choi WJ, Kim HH, Cha JH, Shin HJ, Chae EY, Yoon GY. Complete response on MR imaging after neoadjuvant chemotherapy in breast cancer patients: Factors of radiologic-pathologic discordance. Eur J Radiol 2019; 118: 114-121.
- 36Chen JH, Bahri S, Mehta RS, et al. Breast cancer: Evaluation of response to neoadjuvant chemotherapy with 3.0-T MR imaging. Radiology 2011; 261(3): 735-743.
- 37Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature 2000; 406(6797): 747-752.
- 38 American Cancer Society. Breast Cancer Facts & Figures 2019-2020. Atlanta: American Cancer Society, Inc. 2019.
- 39Kim Y, Sim SH, Park B, et al. Magnetic resonance imaging (MRI) assessment of residual breast cancer after neoadjuvant chemotherapy: Relevance to tumor subtypes and MRI interpretation threshold. Clin Breast Cancer 2018; 18(6): 459-467 e451.
- 40Bouzon A, Iglesias A, Acea B, Mosquera C, Santiago P, Mosquera J. Evaluation of MRI accuracy after primary systemic therapy in breast cancer patients considering tumor biology: Optimizing the surgical planning. Radiol Oncol 2019; 53(2): 171-177.
- 41De Los Santos JF, Cantor A, Amos KD, et al. 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 2013; 119(10): 1776-1783.
- 42McGuire KP, Toro-Burguete J, Dang H, et al. MRI staging after neoadjuvant chemotherapy for breast cancer: Does tumor biology affect accuracy? Ann Surg Oncol 2011; 18(11): 3149-3154.
- 43Loo 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.
- 44Fukada I, Araki K, Kobayashi K, et al. Pattern of tumor shrinkage during neoadjuvant chemotherapy is associated with prognosis in low-grade luminal early breast cancer. Radiology 2018; 286(1): 49-57.
- 45Murphy C, Mukaro V, Tobler R, et al. Evaluating the role of magnetic resonance imaging post-neoadjuvant therapy for breast cancer in the NEONAB trial. Intern Med J 2018; 48(6): 699-705.
- 46Bouzon A, Acea B, Soler R, et al. Diagnostic accuracy of MRI to evaluate tumour response and residual tumour size after neoadjuvant chemotherapy in breast cancer patients. Radiol Oncol 2016; 50(1): 73-79.
- 47Truin W, Vugts G, Roumen RM, et al. Differences in response and surgical management with neoadjuvant chemotherapy in invasive lobular versus ductal breast cancer. Ann Surg Oncol 2016; 23(1): 51-57.
- 48Kim TH, Kang DK, Yim H, Jung YS, Kim KS, Kang SY. Magnetic resonance imaging patterns of tumor regression after neoadjuvant chemotherapy in breast cancer patients: Correlation with pathological response grading system based on tumor cellularity. J Comput Assist Tomogr 2012; 36(2): 200-206.
- 49Tozaki M, Kobayashi T, Uno S, et al. Breast-conserving surgery after chemotherapy: Value of MDCT for determining tumor distribution and shrinkage pattern. AJR Am J Roentgenol 2006; 186(2): 431-439.
- 50Bahri S, Chen JH, Mehta RS, et al. Residual breast cancer diagnosed by MRI in patients receiving neoadjuvant chemotherapy with and without bevacizumab. Ann Surg Oncol 2009; 16(6): 1619-1628.
- 51Moy L. Do tumor shrinkage patterns at breast MR imaging predict survival? Radiology 2018; 286(1): 58-59.
- 52Ballesio L, Gigli S, Di Pastena F, et al. Magnetic resonance imaging tumor regression shrinkage patterns after neoadjuvant chemotherapy in patients with locally advanced breast cancer: Correlation with tumor biological subtypes and pathological response after therapy. Tumour Biol 2017; 39(3):1010428317694540.
10.1177/1010428317694540 Google Scholar
- 53Heacock L, Lewin A, Ayoola A, et al. Dynamic contrast-enhanced MRI evaluation of pathologic complete response in human epidermal growth factor receptor 2 (HER2)-positive breast cancer after HER2-targeted therapy. Acad Radiol 2019; [epub ahead of print]. https://doi.org/10.1016/j.acra.2019.07.011
- 54Luengo-Gil G, Gonzalez-Billalabeitia E, Chaves-Benito A, et al. Effects of conventional neoadjuvant chemotherapy for breast cancer on tumor angiogenesis. Breast Cancer Res Treat 2015; 151(3): 577-587.
- 55Schrading S, Kuhl CK. Breast cancer: Influence of taxanes on response assessment with dynamic contrast-enhanced MR imaging. Radiology 2015; 277(3): 687-696.
- 56Santamaria G, Bargallo X, Fernandez PL, Farrus B, Caparros X, Velasco M. Neoadjuvant systemic therapy in breast cancer: Association of contrast-enhanced MR imaging findings, diffusion-weighted imaging findings, and tumor subtype with tumor response. Radiology 2017; 283(3): 663-672.
- 57Dong JM, Wang HX, Zhong XF, et al. Changes in background parenchymal enhancement in HER2-positive breast cancer before and after neoadjuvant chemotherapy: Association with pathologic complete response. Medicine (Baltimore) 2018; 97(43):e12965.
- 58You C, Peng W, Zhi W, et al. Association between background parenchymal enhancement and pathologic complete remission throughout the neoadjuvant chemotherapy in breast cancer patients. Transl Oncol 2017; 10(5): 786-792.
- 59Hattangadi J, Park C, Rembert J, et al. Breast stromal enhancement on MRI is associated with response to neoadjuvant chemotherapy. AJR Am J Roentgenol 2008; 190(6): 1630-1636.
- 60Preibsch H, Wanner L, Bahrs SD, et al. Background parenchymal enhancement in breast MRI before and after neoadjuvant chemotherapy: Correlation with tumour response. Eur Radiol 2016; 26(6): 1590-1596.
- 61Oh SJ, Chae EY, Cha JH, Shin HJ, Choi WJ, Kim HH. Relationship between background parenchymal enhancement on breast MRI and pathological tumor response in breast cancer patients receiving neoadjuvant chemotherapy. Br J Radiol 2018; 91(1088):20170550.
- 62Pilewskie M, Morrow M. Axillary nodal management following neoadjuvant chemotherapy: A review. JAMA Oncol 2017; 3(4): 549-555.
- 63Boughey JC, Suman VJ, Mittendorf EA, et al. Sentinel lymph node surgery after neoadjuvant chemotherapy in patients with node-positive breast cancer: The ACOSOG Z1071 (Alliance) clinical trial. JAMA 2013; 310(14): 1455-1461.
- 64Kuehn T, Bauerfeind I, Fehm T, et al. Sentinel-lymph-node biopsy in patients with breast cancer before and after neoadjuvant chemotherapy (SENTINA): A prospective, multicentre cohort study. Lancet Oncol 2013; 14(7): 609-618.
- 65Boughey JC, Ballman KV, Le-Petross HT, et al. Identification and resection of clipped node decreases the false-negative rate of sentinel lymph node surgery in patients presenting with node-positive breast cancer (T0-T4, N1-N2) who receive neoadjuvant chemotherapy: Results from ACOSOG Z1071 (Alliance). Ann Surg 2016; 263(4): 802-807.
- 66Weber JJ, Jochelson MS, Eaton A, et al. MRI and prediction of pathologic complete response in the breast and axilla after neoadjuvant chemotherapy for breast cancer. J Am Coll Surg 2017; 225(6): 740-746.
- 67Hieken TJ, Boughey JC, Jones KN, Shah SS, Glazebrook KN. Imaging response and residual metastatic axillary lymph node disease after neoadjuvant chemotherapy for primary breast cancer. Ann Surg Oncol 2013; 20(10): 3199-3204.
- 68You S, Kang DK, Jung YS, An YS, Jeon GS, Kim TH. Evaluation of lymph node status after neoadjuvant chemotherapy in breast cancer patients: Comparison of diagnostic performance of ultrasound, MRI and (1)(8)F-FDG PET/CT. Br J Radiol 2015; 88(1052):20150143.
- 69Moo TA, Jochelson MS, Zabor EC, et al. Is clinical exam of the axilla sufficient to select node-positive patients who downstage after NAC for SLNB? A comparison of the accuracy of clinical exam versus MRI. Ann Surg Oncol 2019; 26(13): 4238-4243.
- 70Kim WH, Lee SW, Kim HJ, et al. Prediction of advanced axillary lymph node metastases (ypN2-3) using breast MR imaging and PET/CT after neoadjuvant chemotherapy in invasive ductal carcinoma patients. Sci Rep 2018; 8(1): 3181.
- 71Hyun SJ, Kim EK, Moon HJ, Yoon JH, Kim MJ. Preoperative axillary lymph node evaluation in breast cancer patients by breast magnetic resonance imaging (MRI): Can breast MRI exclude advanced nodal disease? Eur Radiol 2016; 26(11): 3865-3873.
- 72Jin X, Jiang YZ, Chen S, Shao ZM, Di GH. A nomogram for predicting the pathological response of axillary lymph node metastasis in breast cancer patients. Sci Rep 2016; 6:32585.
- 73Choi HJ, Ryu JM, Kim I, et al. Nomogram for accurate prediction of breast and axillary pathologic response after neoadjuvant chemotherapy in node positive patients with breast cancer. Ann Surg Treat Res 2019; 96(4): 169-176.
- 74Kim R, Chang JM, Lee HB, et al. Predicting axillary response to neoadjuvant chemotherapy: Breast MRI and US in patients with node-positive breast cancer. Radiology 2019; 293(1): 49-57.
- 75Kim HS, Shin MS, Kim CJ, et al. Improved model for predicting axillary response to neoadjuvant chemotherapy in patients with clinically node-positive breast cancer. J Breast Cancer 2017; 20(4): 378-385.
- 76Ouldamer L, Chas M, Arbion F, et al. Risk scoring system for predicting axillary response after neoadjuvant chemotherapy in initially node-positive women with breast cancer. Surg Oncol 2018; 27(2): 158-165.
- 77Osorio-Silla I, Gomez Valdazo A, Sanchez Mendez JI, et al. Is it always necessary to perform an axillary lymph node dissection after neoadjuvant chemotherapy for breast cancer? Ann R Coll Surg Engl 2019; 101(3): 186-192.
- 78Ha R, Chang P, Karcich J, et al. Predicting post neoadjuvant axillary response using a novel convolutional neural network algorithm. Ann Surg Oncol 2018; 25(10): 3037-3043.
- 79Partridge 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.
- 80Pereira NP, Curi C, Osorio C, et al. Diffusion-weighted magnetic resonance imaging of patients with breast cancer following neoadjuvant chemotherapy provides early prediction of pathological response — A prospective study. Sci Rep 2019; 9(1):16372.
- 81Partridge SC, Zhang Z, Newitt DC, et al. Diffusion-weighted MRI findings predict pathologic response in neoadjuvant treatment of breast cancer: The ACRIN 6698 multicenter trial. Radiology 2018; 289(3): 618-627.
- 82Park SH, Moon WK, Cho N, et al. 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 2012; 22(1): 18-25.
- 83Chu W, Jin W, Liu D, et al. Diffusion-weighted imaging in identifying breast cancer pathological response to neoadjuvant chemotherapy: A meta-analysis. Oncotarget 2018; 9(6): 7088-7100.
- 84Liu 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.
- 85Richard 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.
- 86Bufi 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.
- 87Wu 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(1): 17-28.
- 88Li W, Newitt DC, Wilmes LJ, et al. Additive value of diffusion-weighted MRI in the I-SPY 2 TRIAL. J Magn Reson Imaging 2019; 50(6): 1742-1753.
- 89Baltzer P, Mann RM, Iima M, et al. Diffusion-weighted imaging of the breast—A consensus and mission statement from the EUSOBI international breast diffusion-weighted imaging working group. Eur Radiol 2020; 30(3): 1436-1450.
- 90Furman-Haran E, Nissan N, Ricart-Selma V, Martinez-Rubio C, Degani H, Camps-Herrero J. Quantitative evaluation of breast cancer response to neoadjuvant chemotherapy by diffusion tensor imaging: Initial results. J Magn Reson Imaging 2018; 47(4): 1080-1090.
- 91Wilmes LJ, Li W, Shin HJ, et al. Diffusion tensor imaging for assessment of response to neoadjuvant chemotherapy in patients with breast cancer. Tomography 2016; 2(4): 438-447.
- 92Che 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.
- 93Kim Y, Kim SH, Lee HW, et al. Intravoxel incoherent motion diffusion-weighted MRI for predicting response to neoadjuvant chemotherapy in breast cancer. Magn Reson Imaging 2018; 48: 27-33.
- 94Cho GY, Gennaro L, Sutton EJ, et al. Intravoxel incoherent motion (IVIM) histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients. Eur J Radiol Open 2017; 4: 101-107.
- 95Fardanesh R, Marino MA, Avendano D, Leithner D, Pinker K, Thakur SB. Proton MR spectroscopy in the breast: Technical innovations and clinical applications. J Magn Reson Imaging 2019; 50(4): 1033-1046.
- 96Tozaki M, Sakamoto M, Oyama Y, Maruyama K, Fukuma E. Predicting pathological response to neoadjuvant chemotherapy in breast cancer with quantitative 1H MR spectroscopy using the external standard method. J Magn Reson Imaging 2010; 31(4): 895-902.
- 97Jacobs MA, Stearns V, Wolff AC, et al. Multiparametric magnetic resonance imaging, spectroscopy and multinuclear ((2)(3)Na) imaging monitoring of preoperative chemotherapy for locally advanced breast cancer. Acad Radiol 2010; 17(12): 1477-1485.
- 98Meisamy S, Bolan PJ, Baker EH, et al. Neoadjuvant chemotherapy of locally advanced breast cancer: Predicting response with in vivo (1)H MR spectroscopy—A pilot study at 4 T. Radiology 2004; 233(2): 424-431.
- 99Bolan PJ, Kim E, Herman BA, et al. MR spectroscopy of breast cancer for assessing early treatment response: Results from the ACRIN 6657 MRS trial. J Magn Reson Imaging 2017; 46(1): 290-302.
- 100Baek HM, Chen JH, Nie K, et al. Predicting pathologic response to neoadjuvant chemotherapy in breast cancer by using MR imaging and quantitative 1H MR spectroscopy. Radiology 2009; 251(3): 653-662.
- 101Zhou J, Qiao PG, Zhang HT, Li GJ, Jiang ZF. Predicting neoadjuvant chemotherapy in nonconcentric shrinkage pattern of breast cancer using 1H-magnetic resonance spectroscopic imaging. J Comput Assist Tomogr 2018; 42(1): 12-18.
- 102Drisis S, Flamen P, Ignatiadis M, et al. Total choline quantification measured by 1H MR spectroscopy as early predictor of response after neoadjuvant treatment for locally advanced breast cancer: The impact of immunohistochemical status. J Magn Reson Imaging 2018; 48(4): 982-993.
- 103Krikken E, van der Kemp WJM, van Diest PJ, et al. Early detection of changes in phospholipid metabolism during neoadjuvant chemotherapy in breast cancer patients using phosphorus magnetic resonance spectroscopy at 7T. NMR Biomed 2019; 32(6):e4086.
- 104O'Flynn EA, DeSouza NM. Functional magnetic resonance: Biomarkers of response in breast cancer. Breast Cancer Res 2011; 13(1): 204.
- 105Dregely I, Prezzi D, Kelly-Morland C, Roccia E, Neji R, Goh V. Imaging biomarkers in oncology: Basics and application to MRI. J Magn Reson Imaging 2018; 48(1): 13-26.
- 106Stadlbauer A, Zimmermann M, Bennani-Baiti B, et al. Development of a non-invasive assessment of hypoxia and neovascularization with magnetic resonance imaging in benign and malignant breast tumors: Initial results. Mol Imaging Biol 2019; 21(4): 758-770.
- 107Ruan K, Song G, Ouyang G. Role of hypoxia in the hallmarks of human cancer. J Cell Biochem 2009; 107(6): 1053-1062.
- 108Vaupel P. Hypoxia and aggressive tumor phenotype: Implications for therapy and prognosis. Oncologist 2008; 13(Suppl 3): 21-26.
- 109Jiang L, Weatherall PT, McColl RW, Tripathy D, Mason RP. Blood oxygenation level-dependent (BOLD) contrast magnetic resonance imaging (MRI) for prediction of breast cancer chemotherapy response: A pilot study. J Magn Reson Imaging 2013; 37(5): 1083-1092.
- 110Reig B, Heacock L, Geras KJ, Moy L. Machine learning in breast MRI. J Magn Reson Imaging 2019 (in press. https://doi.org/10.1002/jmri.26852.)
- 111Eun NL, Kang D, Son EJ, et al. Texture analysis with 3.0-T MRI for association of response to neoadjuvant chemotherapy in breast cancer. Radiology 2020; 294(1): 31-41.
- 112Braman NM, Etesami M, Prasanna P, et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 2017; 19(1): 57.
- 113Cain EH, Saha A, Harowicz MR, Marks JR, Marcom PK, Mazurowski MA. Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: A study using an independent validation set. Breast Cancer Res Treat 2019; 173(2): 455-463.
- 114Liu Z, Li Z, Qu J, et al. Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: A multicenter study. Clin Cancer Res 2019; 25(12): 3538-3547.
- 115Braman N, Prasanna P, Whitney J, et al. Association of peritumoral radiomics with tumor biology and pathologic response to preoperative targeted therapy for HER2 (ERBB2)-positive breast cancer. JAMA Netw Open 2019; 2(4):e192561.
- 116Aghaei F, Tan M, Hollingsworth AB, Zheng B. Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy. J Magn Reson Imaging 2016; 44(5): 1099-1106.
- 117Wu J, Cao G, Sun X, et al. Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy. Radiology 2018; 288(1): 26-35.
- 118Aghaei F, Tan M, Hollingsworth AB, Qian W, Liu H, Zheng B. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy. Med Phys 2015; 42(11): 6520-6528.
- 119Tahmassebi A, Wengert GJ, Helbich TH, et al. Impact of machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in breast cancer patients. Invest Radiol 2019; 54(2): 110-117.
- 120Drukker K, Edwards A, Doyle C, Papaioannou J, Kulkarni K, Giger ML. Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients. J Med Imaging (Bellingham) 2019; 6(3):034502.
- 121Ha R, Chin C, Karcich J, et al. Prior to initiation of chemotherapy, can we predict breast tumor response? Deep learning convolutional neural networks approach using a breast MRI tumor dataset. J Digit Imaging 2019; 32(5): 693-701.
- 122Ravichandran K, Braman N, Janowczyk A, Madabhushi A. A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI: Proc SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750C. Accessed February 27, 2018. https://doi.org/10.1117/12.2294056.
- 123Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic analysis of breast cancer: Luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology 2014; 273(2): 365-372.
- 124Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging 2015; 42(4): 902-907.
- 125Woodard GA, Ray KM, Joe BN, Price ER. Qualitative radiogenomics: Association between oncotype DX test recurrence score and BI-RADS mammographic and breast MR imaging features. Radiology 2018; 286(1): 60-70.
- 126Yamamoto S, Han W, Kim Y, et al. Breast cancer: radiogenomic biomarker reveals associations among dynamic contrast-enhanced MR imaging, long noncoding RNA, and metastasis. Radiology 2015; 275(2): 384-392.
- 127Park AY, Han MR, Park KH, et al. Radiogenomic analysis of breast cancer by using B-mode and vascular US and RNA sequencing. Radiology 2020; 295(1):191368.
- 128Mehta S, Hughes NP, Li S, et al. Radiogenomics monitoring in breast cancer identifies metabolism and immune checkpoints as early actionable mechanisms of resistance to anti-angiogenic treatment. EBioMedicine 2016; 10: 109-116.
- 129Siamakpour-Reihani S, Owzar K, Jiang C, et al. Genomic profiling in locally advanced and inflammatory breast cancer and its link to DCE-MRI and overall survival. Int J Hyperthermia 2015; 31(4): 386-395.
- 130Heil J, Kummel S, Schaefgen B, et al. Diagnosis of pathological complete response to neoadjuvant chemotherapy in breast cancer by minimal invasive biopsy techniques. Br J Cancer 2015; 113(11): 1565-1570.
- 131Kuerer HM, Rauch GM, Krishnamurthy S, et al. A clinical feasibility trial for identification of exceptional responders in whom breast cancer surgery can be eliminated following neoadjuvant systemic therapy. Ann Surg 2018; 267(5): 946-951.
- 132Kuerer HM, Vrancken Peeters M, Rea DW, Basik M, De Los SJ, Heil J. Nonoperative management for invasive breast cancer after neoadjuvant systemic therapy: Conceptual basis and fundamental international feasibility clinical trials. Ann Surg Oncol 2017; 24(10): 2855-2862.
- 133Noordaa MVD, Duijnhoven F, Loo C, et al. Towards omitting breast cancer surgery in select patient groups: Assessment of pathologic complete response after neoadjuvant systemic therapy using biopsies—The MICRA trial. J Clin Oncol 2017; 35(15_suppl): TPS593-TPS593.
- 134Basik M, Costantino JP, Santos JFDL, et al. NRG oncology BR005: Phase II trial assessing accuracy of tumor bed biopsies (Bx) in predicting pathologic response in patients (pts) with clinical/radiological complete response (CR) after neoadjuvant chemotherapy (NCT) in order to explore the feasibility of breast-conserving treatment (BCT) without surgery. J Clin Oncol 2018; 36(15_suppl): TPS604-TPS604.
- 135Gampenrieder SP, Peer A, Weismann C, et al. Radiologic complete response (rCR) in contrast-enhanced magnetic resonance imaging (CE-MRI) after neoadjuvant chemotherapy for early breast cancer predicts recurrence-free survival but not pathologic complete response (pCR). Breast Cancer Res 2019; 21(1): 19.
- 136van Ramshorst MS, Loo CE, Groen EJ, et al. MRI predicts pathologic complete response in HER2-positive breast cancer after neoadjuvant chemotherapy. Breast Cancer Res Treat 2017; 164(1): 99-106.
- 137Loo CE, Rigter LS, Pengel KE, et al. Survival is associated with complete response on MRI after neoadjuvant chemotherapy in ER-positive HER2-negative breast cancer. Breast Cancer Res 2016; 18(1): 82.
