A new nomogram for predicting the malignant diagnosis of Breast Imaging Reporting and Data System (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting
A new nomogram for predicting the malignant diagnosis of Breast Imaging Reporting and Data System (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting
复制标题
一种新的列线图，用于预测诊断环境中乳腺组织致密女性的乳腺影像报告和数据系统 (BI-RADS) 超声检查 4A 类病变的恶性诊断
DOI：
10.21037/qims-20-1203
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发表时间：
2021-07-01
影响因子：
2.8
通讯作者：
Gong, Chang
中科院分区：
文献类型：
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作者：
Yang, Yaping;Hu, Yue;Gong, Chang
背景：活检已被推荐用于乳腺成像报告和数据系统（BI-RADS）4类病变。然而，4A类病变的恶性率很低（2-10%）。因此，大多数4A类病变的活检是良性的，其结果一般会造成额外的医疗费用和患者anxiety.Methods：一个预测模型的基础上，418 BI-RADS超声（美国）4A类患者在中山纪念医院的分析。应用单变量和多变量逻辑回归分析来确定纳入最终列线图的重要变量。采用一致性指数（C指数）和标准曲线评价预测准确性和区分能力。来自广州医科大学附属第二医院的97名患者的独立队列用于外部验证。多因素分析显示，乳腺癌家族史是训练队列的独立危险因素（OR=4.588，P=0.004），US特征[边缘（OR=2.916，P=0.019），形态（不规则型与椭圆型，OR=2.474，P=0.044;圆形与椭圆形，OR=1.935，P=0.276），平行方向与不平行方向（OR =2.204，P=0.040）]、低可疑淋巴结（OR=7.664，P=0.019）和钼靶X线（MG）上可疑钙化（OR=6.736，P=0.001）。训练队列[0.813，95%置信区间（95% CI），0.733 - 0.893]和验证队列（0.765，95% CI，0.584 - 0.946）的C指数良好。校正曲线显示出诺模图预测与实际观察恶性肿瘤概率之间的最佳一致性。此外，临界值设定为100，用于区分高风险和低风险。该模型在辨别不同的风险groups.Conclusions：我们开发了一个良好的歧视和校准诺模图预测恶性的BI-RADS美国4A类病变致密乳腺组织，这可能有助于临床医生确定患者的风险较低或较高。
Background: Biopsy has been recommended for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. However, the malignancy rate of category 4A lesions is very low (2-10%). Therefore, most biopsies of category 4A lesions are benign, and the results will generally cause additional health care costs and patient anxiety.Methods: A prediction model was developed based on an analysis of 418 BI-RADS ultrasonography (US) category 4A patients at Sun Yat-sen Memorial Hospital. Univariate and multivariate logistic regression analyses were applied to identify significant variables for inclusion in the final nomogram. The predictive accuracy and discriminative ability were evaluated using the concordance index (C-index) and calibration curves. An independent cohort of 97 patients from the Second Affiliated Hospital of Guangzhou Medical University was used for external validation.Results: The independent risk factors from the multivariate analysis for the training cohort were family history of breast cancer (OR=4.588, P=0.004), US features [margin (OR=2.916, P=0.019), shape (irregular vs. oval, OR=2.474, P=0.044; round vs. oval, OR=1.935, P=0.276), parallel orientation vs. not parallel (OR =2.204, P=0.040)], low suspicious lymph nodes (OR=7.664, P=0.019), and suspicious calcifications on mammography (MG) (OR=6.736, P=0.001). The C-index was good in the training [0.813, 95% confidence interval (95% CI), 0.733 to 0.893] and validation cohorts (0.765, 95% CI, 0.584 to 0.946). The calibration curves showed optimal agreement between the nomogram prediction and actual observations for the probability of malignancy. Also, the cutoff score was set to 100 for discriminating high and low risk. The model performed well in discerning different risk groups.Conclusions: We developed a well-discriminated and calibrated nomogram to predict the malignancy of BI-RADS US category 4A lesions in dense breast tissue, which may help clinicians identify patients at lower or higher risk.