基于超声构建的列线图模型在鉴别非平行位乳腺小肿块良恶性中的应用价值
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南京医科大学第一附属医院超声诊断科,江苏 南京 210029

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R445.1;R737.9

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国家自然科学基金(82371979)


The value of ultrasound ⁃ based nomogram for predicting malignancy in small and non ⁃ parallel breast lesions
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Department of Ultrasound,the First Affiliated Hospital of Nanjing Medical University,Nanjing 210029 ,China

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    摘要:

    目的:基于临床及超声特点构建预测模型,以鉴别超声非平行位生长、乳腺影像报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)分为4A、4B类乳腺小肿块的良恶性,并评估其应用价值。方法:回顾性分析2020年6月— 2024年5月在南京医科大学第一附属医院就诊的327例乳腺肿块患者,筛选超声诊断为BI-RADS 4A、4B类,非平行位生长、直径≤10 mm的肿块,按7∶3的比例随机分为训练集(n=229)和验证集(n=98)。应用Logistic回归分析鉴别乳腺良、恶性肿块的风险因素并构建诊断预测模型。以受试者工作特征(receiver operating characteristic,ROC)曲线和决策曲线分析(decision curve analysis,DCA)评估模型的鉴别效能和临床意义。结果:327例患者中恶性比例为36.1%(118/327)。经单因素和多因素Logistic 回归分析,年龄、边缘、弹性和超声BI-RADS分类为恶性肿块的独立危险因素。纳入这4个变量建立诊断模型,并以列线图的形式展现。训练集和验证集ROC的曲线下面积分别为0.846和0.798;DCA证实应用模型预测肿块良恶性风险可以使患者获益。此外,基于列线图计算的风险评分对所有患者进行危险分层,风险评分≥0.7(后20%)的患者被列为高危组;风险评分≤0.1 (前20%)的患者被列为低危组,低、高危组恶性率分别为8.8%和82.1%。结论:根据临床及超声特点构建的预测模型,可有效鉴别超声BI-RADS 4A、4B类非平行位乳腺小肿块的良恶性。根据列线图计算风险评分进行危险分层,低危组的恶性率仅 8.8%,而高危组的恶性率为82.1%,具有较高的临床价值。

    Abstract:

    Objective:To develop a predictive model based on clinical and ultrasound characteristics for differentiating between benign from malignant small breast masses with non-parallel growth and classified by the Breast Imaging Reporting and Data System(BI -RADS)as BI-RADS 4A and 4B,and to evaluate its application value. Methods:For this retrospective study,327 patients with breast masses were recruited in the First Affiliated Hospital of Nanjing Medical University from June 2020 to May 2024. These patients were screened and diagnosed with BI-RADS 4A and 4B classifications by ultrasound,with non-parallel growth and a diameter of ≤10 mm. They were randomly divided into a training set(n=229)and a validation set(n=98)at the ratio of 7∶3. Logistic regression analysis was used to identify risk factors for differentiating benign from malignant breast lesions and to construct a diagnostic predictive model. The effectiveness of the model was evaluated by the receiver operating characteristic(ROC)curve and the decision curve analysis(DCA). Results:Among the 327 cases,36.1%(118/327)were malignant. Univariate and multivariate logistic regression analyses identified age,margin,elasticity assessment,and US - BI - RADS classification as independent risk factors for malignant masses. A diagnostic model incorporating these four variables was established and presented as a nomogram. The area under curve(AUC)of the ROC was 0.846 and 0.798 for the training and validation sets,respectively. DCA confirmed that the model’s prediction of the risk of benign and malignant masses could benefit patients. Moreover,risk stratification based on nomogram-calculated risk scores for all patients showed that those with risk scores ≥0.7(top 20%)were classified as high-risk,while those with scores ≤0.1(bottom 20%)were classified as low - risk,with malignant rates of 8.8% and 82.1%,respectively. Conclusion:The predictive model constructed based on clinical and ultrasound characteristics effectively differentiates benign from malignant small breast masses of BI-RADS 4A and 4B categories with non-parallel growth. Risk stratification based on nomogram-calculated risk scores indicates a malignant rate of only 8.8% in the low- risk group and 82.1% in the high-risk group,demonstrating significant clinical value.

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李沁,丁志颖,宗晴晴,李奥,邓晶.基于超声构建的列线图模型在鉴别非平行位乳腺小肿块良恶性中的应用价值[J].南京医科大学学报(自然科学版),2025,45(8):1178-1185

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  • 收稿日期:2025-01-19
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  • 在线发布日期: 2025-08-13
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