The Auxiliary Diagnostic Value Of The Radiomics Model Of Multimodal X-ray Applying In The Differentiation Of Benign And Malignant BI-RADS 4 Breast Masses

Objective:To investigate the value of the radiomics model of multimodal X-ray applying in the differentiation of benign and malignant BI-RADS 4 breast masses.Methods:From August 2017 to April 2020,120 female patients with BI-RADS 4 breast lesions[4A,n=41（34 cases of benign,7 cases of malignant）;4B,n=30（11 cases of benign,19 cases of malignant）;4C,n=49（4 cases of benign,45 cases of malignant）)]diagnosed by full-field digital mammography（FFDM）and digital breast tomosynthesis（DBT）and confirmed by pathology in The first affiliated hospital of shandong first medical university（Shandong province qianfushan hospital）were analyzed retrospectively.Among them,49 patients were benign and 71 were malignant,aging from 20 to 86 years（mean age,51.94±13.94years）.Both craniocaudal（CC）and mediolateral oblique（MLO）mammographic views were taken for all the patients using FFDM and DBT.The regions of interest（ROI）of the lesions were delineated on FFDM and DBT images at CC and MLO views,respectively,among which five DBT images with the lesions most clear were delineated.Analysis was performed using omics software（Radcloud,Huiying Medical Technology Co.,Ltd.）,with 2,818 eigenvalues extracted from each sequence,and feature dimension reduced by Anova and Lasso,successively.With the pathological results of the patients as the golden standard,the positive predictive value（PPV）of artificial subtype classifications were calculated,and the diagnostic efficacy of different subtypes was evaluated using ROC curve.FFDM,DBT and FFDM+DBT classification-based prediction models were established,respectively.Machine learning was carried out using the k-nearest neighbor（KNN）model.The diagnostic efficiency of the three prediction models ware verified,and comparative analysis of the three prediction models was conducted by pairwise comparison of ROC curve（Z statistics）.Statistical analysis was performed using SPSS23.0.P<0.05 was considered as statistically significant.Result:The PPV of subtypes BI-RADS 4A,4B and 4C were 17%（7/41）,63.3%（19/30）and 91.8%（45/49）,respectively.The AUC of overall prediction was 0.87,and the diagnostic sensitivity and specificity were 83%and 86%,respectively.Among the FFDM,DBT and FFDM+DBT prediction models,the AUC value（0.84）and specificity（70%）of the DBT group were higher than those of the FFDM group（0.80,60%）,and the AUC value（0.91）and specificity（90%）of the FFDM+DBT group were higher than those of the DBT group and the FFDM group.The sensitivity of the three prediction models were same,both of which were 80%.Pairwise comparison of the three prediction models only showed that there was statistically significant difference between the FFDM+DBT group and the FFDM group（P<0.05）.No statistical difference was found between the other two groups.Conclusion:The radiomics model of multimodal X-ray with FFDM combined with DBT is helpful for the differentiation of benign and malignant BI-RADS 4 breast masses,and its diagnostic efficiency is higher than that of clinical subtype classification.