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Medline ® Abstract for Reference 55

of 'General principles of neoadjuvant therapy for breast cancer'

55
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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.
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Hylton NM, Gatsonis CA, Rosen MA, Lehman CD, Newitt DC, Partridge SC, Bernreuter WK, Pisano ED, Morris EA, Weatherall PT, Polin SM, Newstead GM, Marques HS, Esserman LJ, Schnall MD, ACRIN 6657 Trial Team and I-SPY 1 TRIAL Investigators
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Radiology. 2016;279(1):44. Epub 2015 Dec 1.
 
PURPOSE: To evaluate volumetric magnetic resonance (MR) imaging for predicting recurrence-free survival (RFS) after neoadjuvant chemotherapy (NACT) of breast cancer and to consider its predictive performance relative to pathologic complete response(PCR).
MATERIALS AND METHODS: This HIPAA-compliant prospective multicenter study was approved by institutional review boards with written informed consent. Women with breast tumors 3 cm or larger scheduled for NACT underwent dynamic contrast-enhanced MR imaging before treatment (examination 1), after one cycle (examination 2), midtherapy (examination 3), and before surgery (examination 4). Functional tumor volume (FTV), computed from MR images by using enhancement thresholds, and change from baseline (ΔFTV) were measured after one cycle and before surgery. Association of RFS with FTV was assessed by Cox regression and compared with association of RFS with PCR and residual cancer burden (RCB), while controlling for age, race, and hormone receptor (HR)/ human epidermal growth factor receptor type 2 (HER2) status. Predictive performance of models was evaluated by C statistics.
RESULTS: Female patients (n = 162) with FTV and RFS were included. At univariate analysis, FTV2, FTV4, andΔFTV4 had significant association with RFS, as did HR/HER2 status and RCB class. PCR approached significance at univariate analysis and was not significant at multivariate analysis. At univariate analysis, FTV2 and RCB class had the strongest predictive performance (C statistic = 0.67; 95% confidence interval [CI]: 0.58, 0.76), greater than for FTV4 (0.64; 95% CI: 0.53, 0.74) and PCR (0.57; 95% CI: 0.39, 0.74). At multivariate analysis, a model with FTV2,ΔFTV2, RCB class, HR/HER2 status, age, and race had the highest C statistic (0.72; 95% CI: 0.60, 0.84).
CONCLUSION: Breast tumor FTV measured by MR imaging is astrong predictor of RFS, even in the presence of PCR and RCB class. Models combining MR imaging, histopathology, and breast cancer subtype demonstrated the strongest predictive performance in this study.
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From the Departments of Radiology (N.M.H., D.C.N.) and Surgery (L.J.E.), University of California, San Francisco, 1600 Divisadero St, Room C250, Box 1667, San Francisco, CA 94115; Department of Biostatistics (C.A.G.) and Center for Statistical Sciences (C.A.G., H.S.M.), Brown University, Providence, RI; American College of Radiology Imaging Network (ACRIN), Philadelphia, Pa (C.A.G., H.S.M., M.D.S.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (M.A.R., M.D.S.); Department of Radiology, University of Washington, Seattle, Wash (C.D.L., S.C.P.); Department of Radiology, University of Alabama, Birmingham, Ala (W.K.B.); Department of Radiology, Medical College of South Carolina, Charleston, SC (E.D.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (E.A.M.); Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (P.T.W.); Department of Radiology, Georgetown University, Washington, DC (S.M
PMID