Shen Lijuan, Ma Xiaowen, Jiang Tingting, Shen Xigang, Yang Wentao, You Chao, Peng Weijun
Shanghai Institute of Medical Imaging, Shanghai, People's Republic of China.
Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.
Cancer Manag Res. 2021 Jan 12;13:235-245. doi: 10.2147/CMAR.S286269. eCollection 2021.
To explore the potential factors influencing the malignancy risk of amorphous calcifications and establish a predictive nomogram for malignancy risk stratification.
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.
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.
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.
探讨影响无定形钙化恶性风险的潜在因素,并建立恶性风险分层的预测列线图。
回顾性分析2013年1月至2018年12月连续的乳腺钼靶检查。记录传统临床特征,并根据第5版乳腺影像报告和数据系统(BI-RADS)评估钼靶特征。纳入的钙化灶被随机分为训练组和验证组。基于逐步多因素逻辑回归分析建立列线图以图形化预测恶性风险。在训练组和验证组中评估模型的区分度和校准性能。
最终,在907名女性中识别出1018个有最终病理结果的无定形钙化灶,恶性率为28.4%(95%可信区间:25.7%,31.3%)。根据钙化灶分布、钙化灶数量、年龄、绝经状态和癌症家族史划分的亚组恶性率有显著差异。训练组和验证组分别有712例和306例。最终基于年龄和分布、最大直径和钙化灶数量这四个风险因素建立了预测列线图。列线图在训练组中的曲线下面积(AUC)为0.799(95%可信区间:0.761,0.836),在验证组中为0.795(95%可信区间:0.738,0.852)。
在乳腺钼靶检查中,钙化灶的分布、最大直径和数量是无定形钙化灶恶性的独立预测因素,且可在临床中轻松获得。本研究建立的用于无定形钙化灶个体化恶性风险分层的列线图显示出良好的区分性能。