Yao Litong, Liu Xiaoyan, Wang Mozhi, Yu Keda, Xu Shouping, Qiu Pengfei, Lv Zhidong, Zhang Xinwen, Xu Yingying
Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, China.
Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China.
J Breast Cancer. 2023 Apr;26(2):136-151. doi: 10.4048/jbc.2023.26.e12. Epub 2023 Mar 16.
We aimed to identify effectiveness-associated indicators and evaluate the optimal tumor reduction rate (TRR) after two cycles of neoadjuvant chemotherapy (NAC) in patients with invasive breast cancer.
This retrospective case-control study included patients who underwent at least four cycles of NAC at the Department of Breast Surgery between February 2013 and February 2020. A regression nomogram model for predicting pathological responses was constructed based on potential indicators.
A total of 784 patients were included, of whom 170 (21.68%) reported pathological complete response (pCR) after NAC and 614 (78.32%) had residual invasive tumors. The clinical T stage, clinical N stage, molecular subtype, and TRR were identified as independent predictors of pCR. Patients with a TRR > 35% were more likely to achieve pCR (odds ratio, 5.396; 95% confidence interval [CI], 3.299-8.825). The receiver operating characteristic (ROC) curve was plotted using the probability value, and the area under the ROC curve was 0.892 (95% CI, 0.863-0.922).
TRR > 35% is predictive of pCR after two cycles of NAC, and an early evaluation model using a nomogram based on five indicators, age, clinical T stage, clinical N stage, molecular subtype, and TRR, is applicable in patients with invasive breast cancer.
我们旨在确定与疗效相关的指标,并评估浸润性乳腺癌患者新辅助化疗(NAC)两个周期后的最佳肿瘤缩小率(TRR)。
这项回顾性病例对照研究纳入了2013年2月至2020年2月期间在乳腺外科接受至少四个周期NAC的患者。基于潜在指标构建了预测病理反应的回归列线图模型。
共纳入784例患者,其中170例(21.68%)在NAC后报告病理完全缓解(pCR),614例(78.32%)有残留浸润性肿瘤。临床T分期、临床N分期、分子亚型和TRR被确定为pCR的独立预测因素。TRR>35%的患者更有可能实现pCR(优势比,5.396;95%置信区间[CI],3.299 - 8.825)。使用概率值绘制受试者工作特征(ROC)曲线,ROC曲线下面积为0.892(95%CI,0.863 - 0.922)。
TRR>35%可预测NAC两个周期后的pCR,并且基于年龄、临床T分期、临床N分期、分子亚型和TRR这五个指标的列线图早期评估模型适用于浸润性乳腺癌患者。