Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features

Abstract

Lijuan Shen,1,2,* Xiaowen Ma,2,3,* Tingting Jiang,2,3 Xigang Shen,2,3 Wentao Yang,3,4 Chao You,2,3 Weijun Peng2,3 1Shanghai Institute of Medical Imaging, Shanghai, People’s Republic of China; 2Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China; 3Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China; 4Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Weijun Peng; Chao YouFudan University Shanghai Cancer Center, No. 270, Dongan Road, Xuhui District, Shanghai 200032, People’s Republic of ChinaTel [phone]; [phone]Fax [phone]Email [email protected] : [email protected]: To explore the potential factors influencing the malignancy risk of amorphous calcifications and establish a predictive nomogram for malignancy risk stratification.Patients and Methods: Consecutive mammograms from January 2013 to December 2018 were retrospectively reviewed. Traditional clinical features were recorded, and mammographic features were estimated according to the 5th BI-RADS. Included calcifications were randomly divided into the training and validation cohorts. A nomogram was developed to graphically predict the risk of malignancy (risk) based on stepwise multivariate logistic regression analysis. The discrimination and calibration performance of the model were assessed in both the training and validation cohorts.Results: Finally, 1018 amorphous calcifications with final pathological results in 907 women were identified with a malignancy rate of 28.4% (95% CI: 25.7%, 31.3%). The malignancy rates of subgroups divided by the distribution of calcifications, quantity of calcifications, age, menopausal status and family history of cancer were significantly different. There were 712 cases and 306 cases in the training and validation cohorts. The prediction nomogram was finally developed based on four risk factors, including age and distribution, maximum diameter and quantity of calcifications. The AUC of the nomogram was 0.799 (95% CI: 0.761, 0.836) in the training cohort and 0.795 (95% CI: 0.738, 0.852) in the validation cohort.Conclusion: On mammography, the distribution, maximum diameter and quantity of calcifications are independent predictors of malignant amorphous calcifications and can be easily obtained in the clinic. The nomogram developed in this study for individualized malignancy risk stratification of amorphous calcifications shows good discrimination performance.Keywords: breast cancer, mammography, calcifications, malignancy risk stratification, nomogram

Keywords

• breast cancer
• mammography
• calcifications
• malignancy risk stratification
• nomogram