From the Department of Radiology, University of Washington, 825 Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics (Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown University, Providence, RI; American College of Radiology Imaging Network (ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N., J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn (P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group, Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham, Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and Science University, Portland, Ore (K.O.); Department of Radiology, University of Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of Women's Imaging, St Joseph's Women's Hospital, Tampa, Fla (J.S.D.).
Radiology. 2018 Dec;289(3):618-627. doi: 10.1148/radiol.2018180273. Epub 2018 Sep 4.
Purpose To determine if the change in tumor apparent diffusion coefficient (ADC) at diffusion-weighted (DW) MRI is predictive of pathologic complete response (pCR) to neoadjuvant chemotherapy for breast cancer. Materials and Methods In this prospective multicenter study, 272 consecutive women with breast cancer were enrolled at 10 institutions (from August 2012 to January 2015) and were randomized to treatment with 12 weekly doses of paclitaxel (with or without an experimental agent), followed by 12 weeks of treatment with four cycles of anthracycline. Each woman underwent breast DW MRI before treatment, at early treatment (3 weeks), at midtreatment (12 weeks), and after treatment. Percentage change in tumor ADC from that before treatment (ΔADC) was measured at each time point. Performance for predicting pCR was assessed by using the area under the receiver operating characteristic curve (AUC) for the overall cohort and according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype. Results The final analysis included 242 patients with evaluable serial imaging data, with a mean age of 48 years ± 10 (standard deviation); 99 patients had HR-positive (hereafter, HR+)/HER2-negative (hereafter, HER2-) disease, 77 patients had HR-/HER2- disease, 42 patients had HR+/HER2+ disease, and 24 patients had HR-/HER2+ disease. Eighty (33%) of 242 patients experienced pCR. Overall, ΔADC was moderately predictive of pCR at midtreatment/12 weeks (AUC = 0.60; 95% confidence interval [CI]: 0.52, 0.68; P = .017) and after treatment (AUC = 0.61; 95% CI: 0.52, 0.69; P = .013). Across the four disease subtypes, midtreatment ΔADC was predictive only for HR+/HER2- tumors (AUC = 0.76; 95% CI: 0.62, 0.89; P < .001). In a test subset, a model combining tumor subtype and midtreatment ΔADC improved predictive performance (AUC = 0.72; 95% CI: 0.61, 0.83) over ΔADC alone (AUC = 0.57; 95% CI: 0.44, 0.70; P = .032.). Conclusion After 12 weeks of therapy, change in breast tumor apparent diffusion coefficient at MRI predicts complete pathologic response to neoadjuvant chemotherapy. © RSNA, 2018 Online supplemental material is available for this article.
在接受新辅助化疗的乳腺癌患者中，通过弥散加权 MRI （DW-MRI）测量肿瘤表观弥散系数（ADC）的变化，以确定其是否能预测病理完全缓解（pCR）。
本前瞻性多中心研究纳入了 10 家机构（2012 年 8 月至 2015 年 1 月）的 272 例连续乳腺癌患者，这些患者被随机分为接受每周 12 个剂量紫杉醇（联合或不联合实验药物）治疗组，或接受 12 周的 4 个周期蒽环类药物治疗组。每位女性在治疗前、治疗早期（3 周）、治疗中期（12 周）和治疗后都进行了乳腺 DW-MRI 检查。在每个时间点测量肿瘤 ADC 相对于治疗前的变化（ΔADC）。使用整个队列的受试者工作特征曲线（ROC）下面积（AUC）和根据肿瘤激素受体（HR）/人表皮生长因子受体 2（HER2）疾病亚型评估预测 pCR 的性能。
最终的分析包括了 242 例有可评估的系列影像学数据的患者，这些患者的平均年龄为 48 岁±10（标准差）；99 例患者为 HR 阳性（HR+）/HER2 阴性（HER2-）疾病，77 例患者为 HR-/HER2-疾病，42 例患者为 HR+/HER2+疾病，24 例患者为 HR-/HER2+疾病。242 例患者中，80 例（33%）经历了 pCR。总体而言，治疗中期/12 周时的 ΔADC 对 pCR 有中度预测能力（AUC=0.60；95%置信区间：0.52，0.68；P=0.017），治疗后时的 ΔADC 也有中度预测能力（AUC=0.61；95%置信区间：0.52，0.69；P=0.013）。在四种疾病亚型中，治疗中期的 ΔADC 仅对 HR+/HER2-肿瘤具有预测能力（AUC=0.76；95%置信区间：0.62，0.89；P＜0.001）。在一个测试子集中，结合肿瘤亚型和治疗中期的 ΔADC 的模型改善了预测性能（AUC=0.72；95%置信区间：0.61，0.83），优于单独使用 ΔADC（AUC=0.57；95%置信区间：0.44，0.70；P=0.032）。
在 12 周的治疗后，乳腺肿瘤 ADC 的变化可以预测新辅助化疗的完全病理缓解。
