Study on early prediction of neoadjuvant chemotherapy efficacy in breast cancer using multi-time-point ultrafast dynamic contrast-enhanced MRI

DU Lihong; CAO Ying; LI Tian; ZHOU Langping; WANG Xiaoxia; ZHANG Jiuquan

doi:10.19300/j.2025.L22567

Abstract：Objective To investigate the value of ultrafast dynamic contrast-enhanced MRI(DCE-MRI)performed before treatment and after the early treatment stage in predicting the efficacy of neoadjuvant chemotherapy(NAC)for breast cancer.Methods A total of 101 female patients with breast cancer undergoing NAC were prospectively enrolled(mean age,49±8 years).Based on postoperative pathological results,patients were divided into the pathological complete response(pCR)group(26 cases)and the non-pCR group(75 cases).All patients underwent ultrafast DCE-MRI and diffusion-weighted imaging(DWI)both before treatment and after two treatment cycles.Clinicopathological characteristics were recorded,and ultrafast DCE-MRI parameters and apparent diffusion coefficient(ADC)values were measured at both time points.Differences in clinicopathological variables and MRI parameters between groups were analyzed using the Chi-square test,independent-sample t test,or Mann-Whitney U test.Multivariate logistic regression analysis was performed to identify independent predictors of pCR and to construct prediction models.The predictive performance of each model was evaluated using receiver operating characteristic(ROC)and the area under the curve(AUC),with DeLong test used for comparisons between models.Results Significant differences were observed between the two groups in ER,PR,HER2,Ki67,lymph node metastasis,and clinical stage(all P＜0.05).Compared with baseline,Ktrans,wash in slope(WIS),peak enhancement intensity(PEI),and initial area under the curve in 60 s(iAUC)values after two treatment cycles were significantly decreased,while time-to-peak(TTP)was significantly increased(all P＜0.05).Multivariate Logistic regression analysis identified PR status,post-treatment PEI,and ΔWIS as independent predictors of pCR.Four models were established based on different predictors:a clinicopathological model,an ultrafast DCE-MRI model,fusion model 1(combining post-treatment ADC value and PR),and fusion model 2(integrating PR,post-treatment PEI,and ΔWIS).The diagnostic performance of fusion model 2(AUC=0.887)was superior to that of the clinicopathological model(AUC=0.702,Z=5.398,P＜0.001)and fusion model 1(AUC=0.764,Z=2.561,P=0.011).Conclusion Ultrafast DCE-MRI combined with clinicopathological features significantly improves the early prediction of NAC in breast cancer.