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锥形束乳腺计算机断层扫描用于预测乳腺癌新辅助化疗病理反应的评估:一项前瞻性研究

Assessment of Cone-Beam Breast Computed Tomography for Predicting Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer: A Prospective Study.

作者信息

Chen Shen, Li Sheng, Zhou Chunyan, He Ni, Chen Jieting, Pei Shengting, Li Jiao, Wu Yaopan, Cai Peiqiang

机构信息

Department of Medical Imaging and Image-Guided Therapy, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Dongfeng Dong Road, Guangzhou 510060, China.

Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, Guangdong Province 519000, China.

出版信息

J Oncol. 2022 Apr 29;2022:9321763. doi: 10.1155/2022/9321763. eCollection 2022.

Abstract

BACKGROUND

Response surveillance of neoadjuvant chemotherapy is needed to facilitate treatment decisions. We aimed to assess the imaging features of cone-beam breast computed tomography (CBBCT) for predicting the pathologic response of breast cancer after neoadjuvant chemotherapy.

METHODS

This prospective study included 81 women with locally advanced breast cancer who underwent neoadjuvant chemotherapy from August 2017 to January 2021. All patients underwent CBBCT before treatment, and 55 and 65 patients underwent CT examinations during the midtreatment (3 cycles) and late-treatment phases (7 cycles), respectively. Clinical information and quantitative parameters such as the diameter, volume, surface area, and CT density were compared between pathologic responders and nonresponders using the -test and the Mann-Whitney test. The performance of meaningful parameters was evaluated with the receiver operating characteristic curve, sensitivity, and specificity.

RESULTS

The quantitative results for the segmented volume, segmented surface area, segmented volume reduction, maximum enhancement ratio, wash-in rate and two-minute enhancement value in the mid- and late-treatment periods had predictive value for pathologic complete response. The area under the curve for the prediction model after multivariate regression analysis was 0.874.

CONCLUSION

After comparing the outcomes of each timepoint, mid- and late-treatment parameters can be used to predict pathologic outcome. The late-treatment parameters showed significant value with a predictive model.

摘要

背景

需要进行新辅助化疗的反应监测以辅助治疗决策。我们旨在评估锥形束乳腺计算机断层扫描(CBBCT)的成像特征,以预测新辅助化疗后乳腺癌的病理反应。

方法

这项前瞻性研究纳入了2017年8月至2021年1月期间接受新辅助化疗的81例局部晚期乳腺癌女性患者。所有患者在治疗前均接受了CBBCT检查,分别有55例和65例患者在治疗中期(3个周期)和治疗后期(7个周期)接受了CT检查。使用t检验和曼-惠特尼检验比较了病理反应者和无反应者之间的临床信息和定量参数,如直径、体积、表面积和CT密度。通过受试者工作特征曲线、敏感性和特异性评估有意义参数的性能。

结果

治疗中期和后期的分割体积、分割表面积、分割体积缩小、最大增强率、注入率和两分钟增强值的定量结果对病理完全缓解具有预测价值。多变量回归分析后预测模型的曲线下面积为0.874。

结论

在比较每个时间点的结果后,治疗中期和后期的参数可用于预测病理结果。治疗后期的参数在预测模型中显示出显著价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4b4/9076291/501c9ff549b4/JO2022-9321763.001.jpg

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