MRI features combined with serum inflammatory factors can predict the pathological grading of breast invasive ductal carcinoma

WANG Jie; WANG Manman; GENG Qinan

doi:10.12122/j.issn.1674-4500.2025.09.15

Abstract：Objective To investigate the diagnostic efficacy of preoperative MRI features combined with serum inflammatory factors in predicting the pathological grading of breast invasive ductal carcinoma.Methods A retrospective analysis was conducted on 127 patients diagnosed with breast invasive ductal carcinoma at Yancheng First People's Hospital from December 2021 to March 2024.Based on pathological grading,the cohort was stratified into a low-grade group(n=76)and a high-grade group(n=51).Patients underwent preoperative MRI and blood routine examinations.Clinical indicators including age,lymphocyte,monocyte,neutrophil and platelet counts were collected,and derived indicators neutrophil-lymphocyte ratio(NLR),platelet-lymphocyte ratio(PLR),lymphocyte-monocyte ratio(LMR)and systemic immune-inflammation index(SII)were calculated.Radiological features included maximum tumor diameter,tumor morphology,location,quadrant,parenchymal enhancement characteristics,MRI-reported axillary lymph node status,time-signal intensity curve type,and BI-RADS classification.The differences in clinical and radiological features between the two groups were compared,and Spearman correlation were used to analyze the correlation between clinical-radiological features and pathological grading of breast cancer.Results There were statistically significant differences in NLR,SII,maximum tumor diameter and BI-RADS classification between the low and high grade groups(P<0.05),and all of them were positively correlated with pathological grading(P<0.05).Among them,SII had the highest diagnostic efficacy in predicting the pathological grading of breast cancer,with an AUC of 0.663.The diagnostic efficacy was improved by combining the four features,with an AUC of 0.750.Conclusion The combination of MRI features and serum inflammatory factors has clinical value in predicting the pathological grading of breast cancer.