Kang Doo Kyoung, Kim Tae Hee, Han Tae Sun, Kim Ku Sang, Yim Hyunee
Departments of Radiology, Ajou University School of Medicine, Suwon, Gyeonggi, South Korea.
J Comput Assist Tomogr. 2013 May-Jun;37(3):432-9. doi: 10.1097/RCT.0b013e31828386ae.
This study examined the ability of magnetic resonance imaging (MRI) enhancement features to predict the response to neoadjuvant chemotherapy (NAC) in patients with breast cancer.
This retrospective study included 107 patients with breast cancer. All patients underwent a baseline breast MRI before NAC and follow-up MRI a mean of 3.7 months later. Breast MRI scans were evaluated using the Breast Imaging Reporting and Data System MRI lexicon. In addition, whole-breast vascularity (WBV) in the cancer-bearing breast was graded according to increased vessel number in comparison with the contralateral breast. Histopathologic tumor regression was graded semiquantitatively based on the Miller-Payne grading system. The ability of each MRI feature to predict the response was evaluated using a logistic regression analysis. Correlations between changes in MRI features and response were also evaluated using the Spearman rank correlation test.
There were 73 responders (68%), including 59 partial and 14 complete responders. No significant difference in baseline MRI features was found between the responders and nonresponders, except for tumor size (P = 0.044). No dynamic enhancement feature on baseline MRI was useful for the early prediction of a response. In addition, an increased WBV did not predict a response, and the WBV change on the follow-up MRI was not correlated with the response. However, the change in the initial enhancement pattern (P = 0.007) and kinetic curve type (P = 0.003) were significantly correlated with response.
No baseline MRI feature described using the Breast Imaging Reporting and Data System MRI lexicon was useful for early prediction of the response to NAC.
本研究探讨磁共振成像（MRI）增强特征预测乳腺癌患者新辅助化疗（NAC）疗效的能力。
本回顾性研究纳入107例乳腺癌患者。所有患者在接受NAC前均进行了基线乳腺MRI检查，并在平均3.7个月后进行了随访MRI检查。采用乳腺影像报告和数据系统MRI词典对乳腺MRI扫描进行评估。此外，根据患侧乳房与对侧乳房血管数量增加情况对患侧乳房的全乳血管密度（WBV）进行分级。基于Miller-Payne分级系统对组织病理学肿瘤退缩进行半定量分级。采用逻辑回归分析评估每个MRI特征预测疗效的能力。还使用Spearman等级相关检验评估MRI特征变化与疗效之间的相关性。
有73例反应者（68%），包括59例部分反应者和14例完全反应者。除肿瘤大小外（P = 0.044），反应者与无反应者在基线MRI特征上未发现显著差异。基线MRI上的动态增强特征对早期预测反应均无帮助。此外，WBV增加并不能预测反应，随访MRI上的WBV变化与反应无关。然而，初始增强模式的变化（P = 0.007）和动力学曲线类型的变化（P = 0.003）与反应显著相关。
使用乳腺影像报告和数据系统MRI词典描述的基线MRI特征均无助于早期预测NAC的疗效。
