Arasu Vignesh A, Kim Paul, Li Wen, Strand Fredrik, McHargue Cody, Harnish Roy, Newitt David C, Jones Ella F, Glymour M Maria, Kornak John, Esserman Laura J, Hylton Nola M
University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, CA.
University of California San Francisco, Department of Epidemiology and Biostatistics, San Francisco, CA.
J Breast Imaging. 2020 Aug;2(4):352-360. doi: 10.1093/jbi/wbaa028. Epub 2020 Jul 22.
Women with advanced HER2- breast cancer have limited treatment options. Breast MRI functional tumor volume (FTV) is used to predict pathologic complete response (pCR) to improve treatment efficacy. In addition to FTV, background parenchymal enhancement (BPE) may predict response and was explored for HER2- patients in the I-SPY-2 TRIAL.
Women with HER2- stage II or III breast cancer underwent prospective serial breast MRIs during four neoadjuvant chemotherapy timepoints. BPE was quantitatively calculated using whole-breast manual segmentation. Logistic regression models were systematically explored using pre-specified and optimized predictor selection based on BPE or combined with FTV.
A total of 352 MRI examinations in 88 patients (29 with pCR, 59 non-pCR) were evaluated. Women with hormone receptor (HR)+HER2- cancers who achieved pCR demonstrated a significantly greater decrease in BPE from baseline to pre-surgery compared to non-pCR patients (odds ratio 0.64, 95% confidence interval (CI): 0.39-0.92, = 0.04). The associated BPE area under the curve (AUC) was 0.77 (95% CI: 0.56-0.98), comparable to the range of FTV AUC estimates. Among multi-predictor models, the highest cross-validated AUC of 0.81 (95% CI: 0.73-0.90) was achieved with combined FTV+HR predictors, while adding BPE to FTV+HR models had an estimated AUC of 0.82 (95% CI: 0.74-0.92).
Among women with HER2- cancer, BPE alone demonstrated association with pCR in women with HR+HER2- breast cancer, with similar diagnostic performance to FTV. BPE predictors remained significant in multivariate FTV models, but without added discrimination for pCR prediction. This may be due to small sample size limiting ability to create subtype-specific multivariate models.
HER2阴性晚期乳腺癌女性的治疗选择有限。乳腺MRI功能肿瘤体积（FTV）用于预测病理完全缓解（pCR）以提高治疗效果。除FTV外，背景实质强化（BPE）可能预测反应，本研究在I-SPY-2试验中对HER2阴性患者进行了探索。
HER2阴性II期或III期乳腺癌女性在四个新辅助化疗时间点接受前瞻性系列乳腺MRI检查。使用全乳手动分割定量计算BPE。基于BPE或结合FTV，使用预先指定和优化的预测因子选择系统地探索逻辑回归模型。
共评估了88例患者的352次MRI检查（29例pCR，59例非pCR）。与非pCR患者相比，达到pCR的激素受体（HR）阳性HER2阴性癌症女性从基线到术前的BPE下降显著更大（比值比0.64，95%置信区间（CI）：0.39-0.92，P = 0.04）。相关的BPE曲线下面积（AUC）为0.77（95%CI：0.56-0.98），与FTV AUC估计范围相当。在多预测因子模型中，FTV+HR预测因子组合的交叉验证AUC最高，为0.81（95%CI：0.73-0.90），而在FTV+HR模型中加入BPE后的估计AUC为0.82（95%CI：0.74-0.92）。
在HER2阴性癌症女性中，单独的BPE在HR阳性HER2阴性乳腺癌女性中显示出与pCR相关，诊断性能与FTV相似。BPE预测因子在多变量FTV模型中仍然显著，但对pCR预测没有额外的判别能力。这可能是由于样本量小限制了创建亚型特异性多变量模型的能力。
