Suppr超能文献

利用自动乳腺超声联合超声造影的定量参数对乳腺癌新辅助化疗反应进行早期预测

Early Prediction of Response to Neoadjuvant Chemotherapy Using Quantitative Parameters on Automated Breast Ultrasound Combined with Contrast-Enhanced Ultrasound in Breast Cancer.

作者信息

Xie Yongwei, Chen Yu, Wang Qiucheng, Li Bo, Shang Haitao, Jing Hui

机构信息

Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China.

Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China.

出版信息

Ultrasound Med Biol. 2023 Jul;49(7):1638-1646. doi: 10.1016/j.ultrasmedbio.2023.03.017. Epub 2023 Apr 24.

Abstract

OBJECTIVE

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.

METHODS

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.

RESULTS

∆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.

CONCLUSION

The CEUS-ABUS model could be used clinically to optimize the treatment of patients with breast cancer.

摘要

目的

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。

方法

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。

结果

∆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。

结论

CEUS-ABUS模型可在临床上用于优化乳腺癌患者的治疗。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验