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

被引:3
作者
Chen, Shen [1 ]
Li, Sheng [1 ]
Zhou, Chunyan [1 ]
He, Ni [1 ]
Chen, Jieting [2 ]
Pei, Shengting [1 ]
Li, Jiao [1 ]
Wu, Yaopan [1 ]
Cai, Peiqiang [1 ]
机构
[1] Sun Yat Sen Univ Canc Ctr, Collaborat Innovat Ctr Canc Med, Dept Med Imaging & Image Guided Therapy, State Key Lab Oncol South China, Dongfeng Dong Rd, Guangzhou 510060, Peoples R China
[2] Sun Yat Sen Univ, Guangdong Prov Key Lab Biomed Imaging, Zhuhai 519000, Guangdong, Peoples R China
关键词
OPTIMAL ACQUISITION TIME; DCE-MRI; MAMMOGRAPHY; CT; DISCRIMINATION; SUBTYPES; VOLUME;
D O I
10.1155/2022/9321763
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
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 T-test and the Mann-Whitney U 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.
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页数:11
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