Analysis On Positive Predictive Value Of Mammographic Calcification Features In BI-RADS 3-5 Categories Lesions

ObjectiveAccording to the mammographic calcification features,to analyze size,morphology and distribution of calcification for diagnostic meaning in breast lesion classified as BI-RADS?breast Imaging Reporting and Data System?3-5 categories,and to discuss the predictive value of breast cancer.Materials and MethodsReviewed 248 mammographically detected calcification lesions in 224 patients who underwent puncture biopsy or operation at the Department of Breast Surgery,affiliated hospital of Taishan medical college between June 2015 and December 2016,and 224patients were female aged 24⁷8?46.2±7.8?years old.All patients should irradiated Carnio-Caudal and Iateral oblique mammograms before biopsy or operation.Diagnostic mammograms were analyzed by 2 radiologists who have rich experience.Each lesion was classified according to BI-RADS descriptors for calcification?size,morphology and distribution?and was categorized by the BI-RADS 5^th assessment categories as category3,4,5.Through analysis of calcification features in different BI-RADS category the proportion of performance as well as benign and malignant breast calcification BI-RADS category diagnosis value.Results1.The 248 localizations yielded 183 malignant lesions and 65 benign lesions.There were 40 category 3 lesions,included 38 benign lesions and 2 malignant lesions,the PPV was 95%.There were 156 category 4 lesions,included 25 benign lesions and 131malignant lesions,the PPV was 83.79%.There were 52 category 5 lesions,included 2benign lesions and 50 malignant lesions,the PPV was 96.15%.The PPV in category 3,4,5was significant different?P<0.05?.2.The PPV of each calcification size descriptor was as follows:bulky calcification18.18%,microcalcification 97.26%,mixed calcification 85.29%.Microcalcification in malignant lesions had a high predictive value.Different size of calcification in BI-RADS3,4,5 of PPV compared,the results were statistically difference?P<0.05?.3.The PPV of each calcification morphologic descriptor was as follows:round or spotty,7.50%;others included thick stick,coarse or popcorn,annular,eggshell,et al,23.08%;amorphous,87.50%;coarse heterogeneous,84.31%;fine pleomorphic,95.65%;fine linear or small branching,97.62%.The four latter calcification was able to help stratify the probability of malignancy,and prediction accuracy from high to low was:fine linear,fine pleomorphic,amorphous,coarse heterogeneous.Statistical analysis results:amorphous,coarse heterogeneous and others had significant difference?P<0.05?.However,round or spotty,fine pleomorphic and fine linear had no statistical significance?P>0.05?.4.The PPV of each calcification distribution descriptor from high to low was as follows:segmental 88.89%,clustered 82.47%,regional 76.19%,linear 62.50%,scatterered8.70%.Statistical analysis results revealed a significant difference among these calcification distribution descriptor in BI-RADS 3,4,5 of PPV except linear?P<0.05?.Conclusion1.Different size of calcification and BI-RADS 3,4,5 of pathologic correlation,microcalcification prompt highly malignant risk.2.Amorphous,coarse heterogeneous and other morphological calcification and BI-RADS 3,4,5 of pathologic correlation.Round and spotty,fine pleomorphic and fine linear calcification and BI-RADS 3,4,5 of pathologic no correlation.3.The PPV of amorphous and coarse heterogeneous calcification is higher,it indicates a higher malignant risk.The PPV of fine pleomorphic and fine linear calcification is highest,it prompt highly malignant risk.4.Clustered,segmental,regional and scattered distribution descriptor of calcification and BI-RADS 3,4,5 of pathologic correlation.Linear distribution descriptor calcification and BI-RADS 3,4,5 of pathologic without correlation.5.The PPV of Clustered,segmental,regional and scattered distribution descriptor of calcification is higher,it can help to predict the risk of malignancy.6.BI-RADS categorization for breast microcalcification lesions can help to predict the risk of malignancy and help decision-making for biopsy.The microcalcification morphologic and distribution descriptors in BI-RADS can help to predict the risk of malignancy.