Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, P.R. China.
Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, P.R. China.
Acad Radiol. 2022 Jan;29 Suppl 1:S155-S163. doi: 10.1016/j.acra.2021.01.023. Epub 2021 Feb 13.
The study investigated the potential of the combined use of dynamic contrast-enhanced MRI and diffusion-weighted imaging in predicting the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) after two cycles of NAC.
Eighty-seven patients with breast cancer who underwent MR examination before and after two cycles of NAC were enrolled. The patients were randomly assigned to a training cohort and a validation cohort (3:1 ratio). MRI parameters including tumor longest diameter, time-signal intensity curve, early enhanced ratio (E), maximal enhanced ratio and ADC value were measured, and percentage change in MRI parameters were calculated. Univariate analysis and multivariate logistic regression analysis were used to evaluate independent predictors of pCR in the training cohort. The validation cohort was used to test the prediction model, and the nomogram was created based on the prediction model.
This study demonstrated that the ADC value after two cycles of NAC (OR = 1.041, 95% CI (1.002, 1.081); p = 0.037), percentage decrease in E (OR = 0.927, 95% CI (0.881, 0.977); p =0.004) and percentage decrease in tumor size (OR = 0.948, 95% CI (0.909, 0.988); p = 0.011) were significantly important for independently predicting pCR. The prediction model yielded AUC of 0.939 and 0.944 in the training cohort and the validation cohort, respectively.
The combined use of dynamic contrast-enhanced MRI and diffusion-weighted imaging could accurately predict pCR after two cycles of NAC. The prediction model and the nomogram had strong predictive value to NAC.
本研究旨在探讨动态对比增强 MRI 和弥散加权成像联合应用于预测新辅助化疗（NAC）两个周期后病理完全缓解（pCR）的潜力。
本研究共纳入 87 例接受 NAC 前和两个周期后 MRI 检查的乳腺癌患者。患者被随机分配到训练队列和验证队列（3:1 比例）。测量 MRI 参数，包括肿瘤最长直径、时间信号强度曲线、早期增强率（E）、最大增强率和 ADC 值，并计算 MRI 参数的百分比变化。使用单因素分析和多变量逻辑回归分析评估训练队列中 pCR 的独立预测因素。验证队列用于测试预测模型，并基于预测模型创建了列线图。
本研究表明，NAC 两个周期后的 ADC 值（OR=1.041，95%CI（1.002，1.081）；p=0.037）、E 百分比下降（OR=0.927，95%CI（0.881，0.977）；p=0.004）和肿瘤大小百分比下降（OR=0.948，95%CI（0.909，0.988）；p=0.011）是独立预测 pCR 的重要因素。预测模型在训练队列和验证队列中的 AUC 分别为 0.939 和 0.944。
动态对比增强 MRI 和弥散加权成像的联合应用可以准确预测 NAC 两个周期后的 pCR。预测模型和列线图对 NAC 具有很强的预测价值。
