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A Mammography-Based Nomogram for Prediction of Malignancy in Breast Suspicious Calcification

Authors :
Lu Chen
Xiao-min Tang
Zong-yu Xie
Li Yang
Jian-fang Chen
Zhi-zhen Gao
Hui-yu Duan
Cheng-cheng Ma
Source :
Academic Radiology. 29:1022-1028
Publication Year :
2022
Publisher :
Elsevier BV, 2022.

Abstract

Aim To establish a predictive nomogram for malignancy risk stratification of micro-calcifications (MCCs) detected on mammography. Materials and Methods Consecutive mammograms from January 2017 to March 2021 were retrospectively reviewed. Traditional clinical features were recorded and mammographic features were estimated according to the 5th BI-RADS. A nomogram was developed to graphically predict the malignancy risk based on multivariate logistic regression analysis. The discrimination and calibration performance of the prediction model was assessed. Results There were 123 cases of suspicious MCCs with final pathological results identified with a malignancy rate of 55.2%. The malignancy rates of subgroups divided according to the morphology and distribution of MCCs, age, menopausal status and the maximum diameter of MCCs were significantly different. Multivariate logistic analysis showed that a menopause status of postmenopausal, maximum diameters of MCCs ≥2 cm, the morphology of MCCs as fine pleomorphic or fine linear or branching, and the distribution of MCCs as linear or segmental were predictive of a higher probability of malignancy. A prediction nomogram was developed based on four risk factors, including menopausal status as well as the maximum diameters, distribution and morphology of the MCCs. The AUC of that nomogram was 0.839 (95%CI:0.771–0.903). Conclusion In mammography, the morphology, distribution and maximum diameter of MCCs, and the menopausal status are independent predictors of malignant suspicious MCCs and are readily available in the clinical setting. The nomogram developed in this study for individualized malignancy risk stratification of suspicious MCCs shows a reliable discrimination performance.

Details

ISSN :
10766332
Volume :
29
Database :
OpenAIRE
Journal :
Academic Radiology
Accession number :
edsair.doi.dedup.....0b60da9cd67b39cfe30cbe534c7d7f72