The predictive value of calcification for the grading of ductal carcinoma in situ in Chinese patients

Abstract

High-grade ductal carcinoma in situ (DCIS) requires resection due to the high risk of developing invasive breast cancer. The predictive powers of noninvasive predictors for high-grade DCIS remain contradictory. This study aimed to explore the predictive value of calcification for high-grade DCIS in Chinese patients.This was a retrospective study of Chinese DCIS patients recruited from the Women's Hospital, School of Medicine, Zhejiang University between January and December 2018. The patients were divided into calcification and non-calcification groups based on the mammography results. The correlation of calcification with the pathologic stage of DCIS was evaluated using the multivariable analysis. The predictive value of calcification for DCIS grading was examined using the receiver operating characteristics (ROC) curve.The pathologic grade of DCIS was not associated with calcification morphology (P = .902), calcification distribution (P = .252), or breast density (P = .188). The multivariable analysis showed that the presence of calcification was independently associated with high pathologic grade of DCIS (OR = 3.206, 95% CI = 1.315-7.817, P = .010), whereas the age, hypertension, menopause, and mammography BI-RADS were not (all P > .05) associated with the grade of DCIS. The ROC analysis of the predictive value of calcification for DCIS grading showed that the area under the curve was 0.626 (P = .019), with a sensitivity of 73.1%, specificity of 52.2%, positive predictive value of 72.2%, and negative predictive value of 53.3%.The presence of calcification is independently associated with high pathologic grade of DCIS and could predict high-grade DCIS in Chinese patients.

Figure 1

Receiver operating characteristics curve analysis of calcification in predicting the grade of ductal carcinoma in situ. The area under the curve was 0.626 (P = .019), with a sensitivity of 73.1%, specificity of 52.2%, positive predictive value of 72.2%, and negative predictive value of 53.3%. ROC = receiver operating characteristics, AUC = area under the curve.