Abstract： Objective:To explore the value of texture features based on dynamic contrast-enhanced MRI(DCE-MRI)images in preoperative prediction of molecular typing of breast cancer.Materials and Methods:The preoperative MRI images and clinicopathological data of 75 patients with breast cancer confirmed by postoperative pathology in the First People's Hospital of Yichang from October 2021 to October 2022 were retrospectively analyzed.The general data of patients were analyzed by chi-square test and variance analysis.Feature parameters were extracted from DCE-MRI images for molecular subtypes with yes and no as binary classification indicators.Dimension reduction of feature parameters was performed by standardized and optimal feature filters.Independent sample t-test or Mann-Whitney U test was used to identify the optimal texture parameters with statistically significant differences between different groups.The area under the ROC curve(AUC)was used to evaluate the diagnostic efficacy of texture analysis.In addition,a logistic regression classification model was constructed based on dynamic enhanced MRI texture features,and the ROC curve was drawn to evaluate the diagnostic efficacy of the model for different molecular subtypes.Results:There were 11 cases of Luminal A type,36 cases of Luminal B type,14 cases of human epidermal growth factor receptor 2(HER-2)overexpression type and 14 cases of triple negative breast cancer(TNBC).There was no significant difference in age,menopausal status,pathological classification,MRI enhancement and lymph node status among patients with different subtypes of breast cancer(P＞0.05).The AUC[95%confidence interval(CI)]values of Luminal A,Luminal B,HER-2 overexpression and TNBC were 0.92(0.77-1.00),0.83(0.62-1.00),0.83(0.55-1.00)and 0.72(0.43-1.00),respectively.There were statistically significant differences in the three texture parameters between Luminal A and non-Luminal A groups(P＜0.05).The AUC values of the three were 0.73,0.70 and 0.75,respectively.When the texture feature 3D grey level co-occurrence matrix cluster shadow(3D_glcm_CS)＞0.439,the diagnostic efficiency of Luminal A type was the highest.There were significant differences in the two texture features between Luminal B group and non-Luminal B group(P＜0.05).When original gray level co-occurrence matrix cluster shadow(o_glcm_CS)＞0.169,the diagnostic efficiency of Luminal B type was the best.There were statistically significant differences in the five texture features between the HER-2 overexpression group and the non-HER-2 overexpression group.The AUC values were 0.76,0.81,0.79,0.80 and 0.82,respectively.When 3D grey level size zone matrix small area low gray level emphasis(3D_glszm_SALGLE)≤-0.460,the diagnostic efficiency of HER-2 overexpression was the highest(AUC=0.82,P＜0.001).Only the difference of texture feature wavelet LH neighbouring gray tone difference matrix busyness(w-LH_ngtdm_B)between TNBC and non-TNBC was statistically significant,and the AUC value was 0.65.Conclusions:DCE-MRI texture analysis can noninvasively and effectively predict the molecular subtypes of breast cancer,which has important guiding value for the classification of preoperative molecular subtypes of breast cancer.