Xie Yongwei, Chen Yu, Wang Qiucheng, Li Bo, Shang Haitao, Jing Hui
Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China.
Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China.
Ultrasound Med Biol. 2023 Jul;49(7):1638-1646. doi: 10.1016/j.ultrasmedbio.2023.03.017. Epub 2023 Apr 24.
This prospective study was aimed at evaluating the role of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in the early prediction of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer.
Forty-three patients with pathologically confirmed invasive breast cancer treated with NAC were included. The standard for evaluation of response to NAC was based on surgery within 21 d of completing treatment. The patients were classified as having a pathological complete response (pCR) and a non-pCR. All patients underwent CEUS and ABUS 1 wk before receiving NAC and after two treatment cycles. The rising time (RT), time to peak (TTP), peak intensity (PI), wash-in slope (WIS) and wash-in area under the curve (Wi-AUC) were measured on the CEUS images before and after NAC. The maximum tumor diameters in the coronal and sagittal planes were measured on ABUS, and the tumor volume (V) was calculated. The difference (∆) in each parameter between the two treatment time points was compared. Binary logistic regression analysis was used to identify the predictive value of each parameter.
∆V, ∆TTP and ∆PI were independent predictors of pCR. The CEUS-ABUS model achieved the highest AUC (0.950), followed by those based on CEUS (0.918) and ABUS (0.891) alone.
The CEUS-ABUS model could be used clinically to optimize the treatment of patients with breast cancer.
本前瞻性研究旨在评估自动乳腺超声（ABUS）和超声造影（CEUS）在乳腺癌患者新辅助化疗（NAC）治疗反应早期预测中的作用。
纳入43例经病理确诊的浸润性乳腺癌患者，接受NAC治疗。NAC治疗反应的评估标准基于完成治疗后21天内的手术情况。患者分为病理完全缓解（pCR）组和非pCR组。所有患者在接受NAC治疗前1周和两个治疗周期后均接受CEUS和ABUS检查。在NAC治疗前后的CEUS图像上测量上升时间（RT）、达峰时间（TTP）、峰值强度（PI）、流入斜率（WIS）和流入曲线下面积（Wi-AUC）。在ABUS上测量冠状面和矢状面的最大肿瘤直径，并计算肿瘤体积（V）。比较两个治疗时间点各参数的差异（∆）。采用二元逻辑回归分析确定各参数的预测价值。
∆V、∆TTP和∆PI是pCR的独立预测因素。CEUS-ABUS模型的AUC最高（0.950），其次是单独基于CEUS（0.918）和ABUS（0.891）的模型。
CEUS-ABUS模型可在临床上用于优化乳腺癌患者的治疗。
