TY - JOUR
T1 - Neoadjuvant chemotherapy for breast cancer
T2 - Functional tumor volume by MR imaging predicts recurrencefree survival-results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL
AU - ACRIN 6657 Trial Team and ISPY 1 TRIAL Investigators
AU - Hylton, Nola M.
AU - Gatsonis, Constantine A.
AU - Rosen, Mark A.
AU - Lehman, Constance D.
AU - Newitt, David C.
AU - Partridge, Savannah C.
AU - Bernreuter, Wanda K.
AU - Pisano, Etta D.
AU - Morris, Elizabeth A.
AU - Weatherall, Paul T.
AU - Polin, Sandra M.
AU - Newstead, Gillian M.
AU - Marques, Helga S.
AU - Esserman, Laura J.
AU - Schnall, Mitchell D.
N1 - Funding Information:
Supported by the American College of Radiology Imaging Network (grants CA079778 and CA080098) and Cancer and Leukemia Group B (grants CA31964 and CA33601).
Publisher Copyright:
© 2015 RSNA.
PY - 2016/4
Y1 - 2016/4
N2 - 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 contrastenhanced 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 (DFTV) 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, and δFTV4 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 a strong 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.
AB - 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 contrastenhanced 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 (DFTV) 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, and δFTV4 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 a strong 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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U2 - 10.1148/radiol.2015150013
DO - 10.1148/radiol.2015150013
M3 - Article
C2 - 26624971
AN - SCOPUS:84962800293
SN - 0033-8419
VL - 279
SP - 44
EP - 55
JO - RADIOLOGY
JF - RADIOLOGY
IS - 1
ER -