Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Department of Ultrasound, The First People's Hospital of Fuyang District, Hangzhou, China.
Clin Breast Cancer. 2023 Oct;23(7):693-703. doi: 10.1016/j.clbc.2023.06.002. Epub 2023 Jun 14.
To establish and validate a nomogram model, which can incorporate clinical data, and imaging features of ultrasound (US) and contrast-enhanced ultrasound (CEUS), for improving the diagnostic efficiency of solid breast lesions.
A total of 493 patients with solid breast lesions were randomly divided into training (n = 345) and validation (n = 148) cohorts with a ratio of 7:3 and, clinical data and image features of US and CEUS were reviewed and retrospectively analyzed. The breast lesions in both the training and validation cohorts were analyzed using the BI-RADS and nomogram models.
Five variables, including the shape and calcification features of conventional US, enhancement type and size after enhancement features of CEUS, and BI-RADS, were selected to construct the nomogram model. As compared to the BI-RADS model, the nomogram model demonstrated satisfactory discriminative function (area under the receiver operating characteristic [ROC] curves [AUC], 0.940; 95% confidence interval [CI], 0.909 to 0.971; sensitivity, 0.905; and specificity, 0.902 in the training cohort and AUC, 0.968; 95% CI, 0.941 to 0.995; sensitivity, 0.971; and specificity, 0.867 in the validation cohort). In addition, the nomogram model showed good consistency and clinical potential according to the calibration curve and DCA.
The nomogram model could identify benign from malignant breast lesions with good performance.
建立并验证一个列线图模型,该模型可以整合临床数据以及超声(US)和超声造影(CEUS)的影像学特征,以提高对实体性乳腺病变的诊断效率。
共 493 例实体性乳腺病变患者,按 7:3 的比例随机分为训练队列(n=345)和验证队列(n=148),回顾性分析其临床资料和 US、CEUS 的影像学特征。分别采用 BI-RADS 及列线图模型对两组患者的乳腺病变进行分析。
选择常规 US 的形态和钙化特征、CEUS 的增强类型和增强后大小特征以及 BI-RADS 共 5 个变量构建列线图模型。与 BI-RADS 模型相比,列线图模型具有较好的判别功能(训练队列的受试者工作特征曲线下面积[AUC]为 0.940[95%置信区间(CI):0.9090.971],敏感度为 0.905,特异度为 0.902;验证队列的 AUC 为 0.968[95%CI:0.9410.995],敏感度为 0.971,特异度为 0.867)。此外,根据校准曲线和决策曲线分析,该列线图模型具有良好的一致性和临床应用潜力。
该列线图模型能够很好地区分良恶性乳腺病变。