Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Yanjiang Road. west No.107, YueXiu district, Guangzhou, Guangdong, 510120, People's Republic of China.
Department of Radiology, Sun Yat-sen University Cancer Center, Dongfeng East Road No.651, Yuexiu District, Guangzhou, Guangdong, 510060, People's Republic of China.
Cancer Imaging. 2019 Jul 3;19(1):46. doi: 10.1186/s40644-019-0229-1.
Mammography (MG) is highly sensitive for detecting microcalcifications, but has low specificity. This study investigates whether establishing a preoperative nomogram including ultrasonographic findings can help predict the likelihood of malignancy in patients with mammographic microcalcification.
Between May 2012 and January 2017, 475 patients with suspicious microcalcifications detected on MG underwent ultrasonography (US). The χ test was used to screen risk factors among the variables. Then, a multivariate logistic regression analysis was performed to identify independent predictors of malignant microcalcifications. A mammographic nomogram (M nomogram) and mammographic-ultrasonographic nomogram (M-U nomogram) were established based on multivariate logistic regression models. The discriminatory ability and clinical utility of both nomograms were compared by the receiver operating characteristics curve and decision curve analysis. The calibration ability was evaluated using a calibration curve.
Among the cases, 68.2% (324/475) were pathologically diagnosed as breast cancer and 31.8% (151/475) were benign lesions. Based on multivariate logistic regression analysis, age, clinical manifestation, morphology and distribution of microcalcifications on MG and lesions associated with microcalcifications on US were confirmed as independent predictors of malignant microcalcifications. In terms of discrimination ability, the C-index of the M-U nomogram was significantly higher than that of the M nomogram (0.917 vs 0.897, p = 0.006). The bias-corrected curve was close to the ideal line in the calibration curve. Decision curve analysis suggested that the M-U nomogram was superior to M nomogram.
Combining mammographic parameters with ultrasonographic findings in a nomogram provided better performance than an M nomogram alone, especially for dense breasts, which suggests the value of ultrasonographic finding for individualized prediction of malignancy in patients with microcalcifications.
乳腺 X 线摄影(MG)对微钙化的检测具有很高的敏感性,但特异性较低。本研究旨在探讨建立包括超声表现在内的术前列线图是否有助于预测 MG 微钙化患者恶性肿瘤的可能性。
2012 年 5 月至 2017 年 1 月,对 475 例 MG 可疑微钙化患者进行超声检查(US)。采用卡方检验筛选变量中的危险因素。然后,进行多变量逻辑回归分析,以确定恶性微钙化的独立预测因素。基于多变量逻辑回归模型建立了乳腺 X 线摄影列线图(M 列线图)和乳腺 X 线摄影-超声列线图(M-U 列线图)。通过受试者工作特征曲线和决策曲线分析比较两种列线图的鉴别能力和临床实用性。通过校准曲线评估校准能力。
在这些病例中,68.2%(324/475)为病理诊断为乳腺癌,31.8%(151/475)为良性病变。基于多变量逻辑回归分析,MG 上微钙化的年龄、临床表现、形态和分布以及与微钙化相关的 US 上的病变被确定为恶性微钙化的独立预测因素。在鉴别能力方面,M-U 列线图的 C 指数明显高于 M 列线图(0.917 比 0.897,p=0.006)。校准曲线中的偏倚校正曲线接近理想线。决策曲线分析表明,M-U 列线图优于 M 列线图。
将乳腺 X 线摄影参数与超声表现相结合建立列线图,比单独使用 M 列线图具有更好的性能,特别是对于致密乳腺,这表明超声表现对微钙化患者恶性肿瘤的个体化预测具有价值。