Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI.
breast cancer
lesion heterogeneity
magnetic resonance
neoadjuvant chemotherapy
pathological response
Journal
Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
received:
07
08
2019
accepted:
25
10
2019
pubmed:
19
11
2019
medline:
15
5
2021
entrez:
19
11
2019
Statut:
ppublish
Résumé
Early prediction of nonresponse is essential in order to avoid inefficient treatments. To evaluate if parametrical response mapping (PRM)-derived biomarkers could predict early morphological response (EMR) and pathological complete response (pCR) 24-72 hours after initiation of chemotherapy treatment and whether concentric analysis of nonresponding PRM regions could better predict response. This was a retrospective analysis of prospectively acquired cohort, nonrandomized, monocentric, diagnostic study. Sixty patients were initially recruited, with 39 women participating in the final cohort. A 1.5T scanner was used for MRI examinations. Dynamic contrast-enhanced (DCE)-MR images were acquired at baseline (timepoint 1, TP1), 24-72 hours after the first chemotherapy (TP2), and after the end of anthracycline treatment (TP3). PRM was performed after fusion of T T-test, receiver operating characteristic (ROC) curves, and logistic regression were used for the analysis. PRM showed a statistical difference between pCR response groups (P < 0.01) and AUC of 0.88 for the prediction of non-pCR. Logistic regression analysis demonstrated that PRMdce+ and Grade II were significant (P < 0.01) for non-pCR prediction (AUC = 0.94). Peripheral tumor region demonstrated higher performance for the prediction of non-pCR (AUC = 0.85) than intermediate and central zones; however, statistical comparison showed no significant difference. PRM could be predictive of non-pCR 24-72 hours after initiation of chemotherapy treatment. Moreover, the peripheral region showed increased AUC for non-pCR prediction and increased signal intensity during treatment for non-pCR tumors, information that could be used for optimal tissue sampling. 1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;51:1403-1411.
Sections du résumé
BACKGROUND
Early prediction of nonresponse is essential in order to avoid inefficient treatments.
PURPOSE
To evaluate if parametrical response mapping (PRM)-derived biomarkers could predict early morphological response (EMR) and pathological complete response (pCR) 24-72 hours after initiation of chemotherapy treatment and whether concentric analysis of nonresponding PRM regions could better predict response.
STUDY TYPE
This was a retrospective analysis of prospectively acquired cohort, nonrandomized, monocentric, diagnostic study.
POPULATION
Sixty patients were initially recruited, with 39 women participating in the final cohort.
FIELD STRENGTH/SEQUENCE
A 1.5T scanner was used for MRI examinations.
ASSESSMENT
Dynamic contrast-enhanced (DCE)-MR images were acquired at baseline (timepoint 1, TP1), 24-72 hours after the first chemotherapy (TP2), and after the end of anthracycline treatment (TP3). PRM was performed after fusion of T
STATISTICAL TESTS
T-test, receiver operating characteristic (ROC) curves, and logistic regression were used for the analysis.
RESULTS
PRM showed a statistical difference between pCR response groups (P < 0.01) and AUC of 0.88 for the prediction of non-pCR. Logistic regression analysis demonstrated that PRMdce+ and Grade II were significant (P < 0.01) for non-pCR prediction (AUC = 0.94). Peripheral tumor region demonstrated higher performance for the prediction of non-pCR (AUC = 0.85) than intermediate and central zones; however, statistical comparison showed no significant difference.
DATA CONCLUSION
PRM could be predictive of non-pCR 24-72 hours after initiation of chemotherapy treatment. Moreover, the peripheral region showed increased AUC for non-pCR prediction and increased signal intensity during treatment for non-pCR tumors, information that could be used for optimal tissue sampling.
LEVEL OF EVIDENCE
1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;51:1403-1411.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1403-1411Informations de copyright
© 2019 International Society for Magnetic Resonance in Medicine.
Références
Makris A, Powles TJ, Ashley SE, et al. A reduction in the requirements for mastectomy in a randomized trial of neoadjuvant chemoendocrine therapy in primary breast cancer. Ann Oncol 1998;9:1179-1184.
Faneyte IF, Schrama JG, Peterse JL, et al. Breast cancer response to neoadjuvant chemotherapy: Predictive markers and relation with outcome. Br J Cancer 2003;88:406-412.
Goldie JH, Coldman AJ. A mathematic model for relating the drug sensitivity of tumors to their spontaneous mutation rate. Cancer Treat Rep 1979;63:1727-1733.
Bear HD, Anderson S, Brown A, et al. The effect on tumor response of adding sequential preoperative docetaxel to preoperative doxorubicin and cyclophosphamide: Preliminary results from National Surgical Adjuvant Breast and Bowel Project Protocol B-27. J Clin Oncol 2003;21:4165-4174
Levine MN, Pritchard KI, Bramwell VH, et al. Randomized trial comparing cyclophosphamide, epirubicin, and fluorouracil with cyclophosphamide, methotrexate, and fluorouracil in premenopausal women with node-positive breast cancer: Update of National Cancer Institute of Canada Clinical Trials Group Trial MA5. J Clin Oncol 2005;23:5166-5170.
Minotti G, Menna P, Salvatorelli E, Cairo G, Gianni L. Anthracyclines: Molecular advances and pharmacologic developments in antitumor activity and cardiotoxicity. Pharmacol Rev 2004;56:185-229.
Rowinsky, MD, Eric K. The development and clinical utility of the taxane class of antimicrotubule chemotherapy agents. Annu Rev Med 1997;48:353-374.
Bines J, Earl H, Buzaid A, Saad E. Anthracyclines and taxanes in the neo/adjuvant treatment of breast cancer: Does the sequence matter? Ann Oncol 2014;25:1079-1085.
Drooger J, Heemskerk-Gerritsen B, Smallenbroek N, et al. Toxicity of (neo)adjuvant chemotherapy for BRCA1- and BRCA2-associated breast cancer Breast Cancer Res Treat 2016;156:557-566.
Li SP, Makris A, Beresford MJ, et al. Use of dynamic contrast-enhanced MR imaging to predict survival in patients with primary breast cancer undergoing neoadjuvant chemotherapy. Radiology 2011;260:68-78.
Padhani A, Hayes C, Assersohn L, et al. Prediction of clinicopathologic response of breast cancer to primary chemotherapy at contrast-enhanced MR imaging: Initial clinical results. Radiology 2006;239:361-374.
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;257:56-63.
Shin HJ, Baek HM, Ahn JH, et al. Prediction of pathologic response to neoadjuvant chemotherapy in patients with breast cancer using diffusion-weighted imaging and MRS. NMR Biomed 2012;25:1349-1359.
Drisis 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;48:982-993.
Hockel M, Knoop C, Schlenger K, et al. Intratumoral pO2 predicts survival in advanced cancer of the uterine cervix. Radiother Oncol 1993;26:45-50.
Yang Z, Tang LH, Klimstra DS. Effect of tumor heterogeneity on the assessment of Ki67 labeling index in well differentiated neuroendocrine tumors metastatic to the liver: Implications for prognostic stratification. Am J Surg Pathol 2011;35:853-860.
Davnall F1, Yip CS, Ljungqvist G, et al. Assessment of tumor heterogeneity: An emerging imaging tool for clinical practice? Insights Imaging 2012;3:573-589.
Li X, Kang H, Arlinghaus LR, et al. Analyzing spatial heterogeneity in DCE- and DW MRI parametric maps to optimize prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Transl Oncol 2014;7:14-22.
Harrison LC, Luukkaala T, Pertovaara H, et al. Non-Hodgkin lymphoma response evaluation with MRI texture classification. J Exp Clin Cancer Res 2009;28:87.
O'Connor JP, Rose CJ, Jackson A, et al. DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6. Br J Cancer 2011;105:139-145.
Jia G, O'Dell C, Heverhagen JT. Colorectal liver metastases: Contrast agent diffusion coefficient for quantification of contrast enhancement heterogeneity at MR imaging. Radiology 2008;248:901-909.
Deoni S, Peters T, Rutt B. Determination of optimal angles for variable nutation proton magnetic spin-lattice, T1, and spin-spin, T2, relaxation times measurement. Magn Reson Med 2004;51:194-199.
El Adoui M, Drisis S, Benjelloun MA. PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR images. Int J Comput Assist Radiol Surg 2018 [Epub ahead of print] https://doi.org/10.1007/s11548-018-1790-y
Boes JL, Hoff BA, Hylton N, et al. Image registration for quantitative parametric response mapping of cancer treatment response. Transl Oncol 2014;7:101-110.
Symmans WF, Peintinger F, Hatzis C, et al. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol 2007;25:4414-4422.
Altman DG, Gore SM, Gardner MJ, et al. Statistical guidelines for contributors to medical journals. Br Med J 1983;286:1489-1493.
Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prevent Vet Med 2000;45:23-41.
Hosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression, 3rd ed. Hoboken, NJ: John Wiley & Sons 2013.
Jose R. Teruel, Mariann G. Heldahl, et al. Dynamic contrast enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. NMR Biomed 2014;27:887-896.
Ahmed A, Gibbs P, Pickles M. Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging 2013;38:89-101.
Wu J, Gong G, Cui Y, et al. Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy. J Magn Reson Imaging 2016;44:1107-1115.
Cho N, Im SA, Park IA. Breast cancer: Early prediction of response to neoadjuvant chemotherapy using Parametric response maps for MR imaging. Radiology 2014;272:385-396.
Li X, Kang H, Arlinghaus LR, et al. Analyzing spatial heterogeneity in DCE- and DW-MRI parametric maps to optimize prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Transl Oncol 2014;7:14-22.
Andre F, Berrada N, Desmedt C. Implication of tumor microenvironment in the resistance to chemotherapy in breast cancer patients. Curr Opin Oncol 2010;22:547-551.
Jain RK. Normalization of tumor vasculature: An emerging concept in antiangiogenic therapy. Science 2005;307:58-62.
Nieto Y1, Woods J, Nawaz F, et al. Prognostic analysis of tumour angiogenesis, determined by microvessel density and expression of vascular endothelial growth factor, in high risk primary breast cancer patients treated with high-dose chemotherapy. Br J Cancer 2007;97:391-397.