乳腺癌新辅助化疗：通过磁共振成像测量的功能性肿瘤体积可预测无复发生存率——来自ACRIN 6657/CALGB 150007 I-SPY 1试验的结果
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.
作者信息
Hylton Nola M, Gatsonis Constantine A, Rosen Mark A, Lehman Constance D, Newitt David C, Partridge Savannah C, Bernreuter Wanda K, Pisano Etta D, Morris Elizabeth A, Weatherall Paul T, Polin Sandra M, Newstead Gillian M, Marques Helga S, Esserman Laura J, Schnall Mitchell D
机构信息
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.P.); and Department of Radiology, University of Chicago, Chicago, Ill (G.M.N.).
出版信息
Radiology. 2016 Apr;279(1):44-55. doi: 10.1148/radiol.2015150013. 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, 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.
目的
评估容积磁共振（MR）成像在预测乳腺癌新辅助化疗（NACT）后无复发生存期（RFS）方面的价值，并探讨其相对于病理完全缓解（PCR）的预测性能。
材料与方法
本符合健康保险流通与责任法案（HIPAA）的前瞻性多中心研究经机构审查委员会批准，并获得书面知情同意。计划接受NACT且乳腺肿瘤直径3 cm或更大的女性患者在治疗前（检查1）、一个周期后（检查2）、治疗中期（检查3）及手术前（检查4）接受动态对比增强MR成像检查。在一个周期后及手术前，根据MR图像利用增强阈值计算功能肿瘤体积（FTV）及相对于基线的变化量（ΔFTV）。通过Cox回归评估RFS与FTV的相关性，并与RFS与PCR及残留癌负荷（RCB）的相关性进行比较，同时对年龄、种族及激素受体（HR）/人表皮生长因子受体2（HER2）状态进行校正。通过C统计量评估模型的预测性能。
结果
纳入了162例有FTV及RFS数据的女性患者。单因素分析显示，FTV2、FTV4及ΔFTV4与RFS显著相关，HR/HER2状态及RCB分级也与RFS显著相关。PCR在单因素分析中接近显著水平，在多因素分析中不显著。单因素分析中，FTV2及RCB分级的预测性能最强（C统计量 = 0.67；95%置信区间[CI]：0.58，0.76），高于FTV4（0.64；95% CI：0.53，0.74）及PCR（0.57；95% CI：0.39，0.74）。多因素分析中，包含FTV2、ΔFTV2、RCB分级、HR/HER2状态、年龄及种族的模型C统计量最高（0.72；95% CI：0.60，0.84）。
结论
MR成像测量的乳腺肿瘤FTV是RFS的有力预测指标，即使存在PCR及RCB分级。在本研究中，结合MR成像、组织病理学及乳腺癌亚型的模型预测性能最强。
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