医学
乳腺癌
完全响应
动态对比度
动态增强MRI
化疗
磁共振成像
乳房磁振造影
癌症
新辅助治疗
放射科
内科学
肿瘤科
乳腺摄影术
作者
Ying Cao,Xiaoxia Wang,Lan Li,Jinfang Shi,Xiangfei Zeng,Yao Huang,Huifang Chen,Fujie Jiang,Ting Yin,Dominik Nickel,Jiuquan Zhang
标识
DOI:10.1016/j.diii.2023.07.003
摘要
The purpose of this study was to evaluate the temporal trends of ultrafast dynamic contrast-enhanced (DCE)-MRI during neoadjuvant chemotherapy (NAC) and to investigate whether the changes in DCE-MRI parameters could early predict pathologic complete response (pCR) of breast cancer.This longitudinal study prospectively recruited consecutive participants with breast cancer who underwent ultrafast DCE-MRI examinations before treatment and after two, four, and six NAC cycles between February 2021 and February 2022. Five ultrafast DCE-MRI parameters (maximum slope [MS], time-to-peak [TTP], time-to-enhancement [TTE], peak enhancement intensity [PEI], and initial area under the curve in 60 s [iAUC]) and tumor size were measured at each timepoint. The changes in parameters between each pair of adjacent timepoints were additionally measured and compared between the pCR and non-pCR groups. Longitudinal data were analyzed using generalized estimating equations. The performance for predicting pCR was assessed using area under the receiver operating characteristic curve (AUC).Sixty-seven women (mean age, 50 ± 8 [standard deviation] years; age range: 25-69 years) were included, 19 of whom achieved pCR. MS, PEI, iAUC, and tumor size decreased, while TTP increased during NAC (all P < 0.001). The AUC (0.92; 95% confidence interval [CI]: 0.83-0.97) of the model incorporating ultrafast DCE-MRI parameter change values (from timepoints 1 to 2) and clinicopathologic characteristics was greater than that of the clinical model (AUC, 0.79; 95% CI: 0.68-0.88) and ultrafast DCE-MRI parameter model at timepoint 2 when combined with clinicopathologic characteristics (AUC, 0.82; 95% CI: 0.71-0.90) (P = 0.01 and 0.02).Early changes in ultrafast DCE-MRI parameters after NAC combined with clinicopathologic characteristics could serve as predictive markers of pCR of breast cancer.
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