| 王鹏,王阳,吴华,陶立元,魏霞,车颖.基于声像图特点及人口学特征建立模型预测乳腺影像报告和数据系统分级[J].中国医学影像技术,2019,35(9):1341~1345 |
| 基于声像图特点及人口学特征建立模型预测乳腺影像报告和数据系统分级 |
| Establishing models based on ultrasonographic and demographic characteristics for predicting breast imaging reporting and data system classification |
| 投稿时间：2019-04-20 修订日期：2019-07-17 |
| DOI：10.13929/j.1003-3289.201904139 |
| 中文关键词: 乳腺肿瘤 超声检查 Logistic模型 |
| 英文关键词:breast neoplasms ultrasonography Logistic models |
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| 中文摘要: |
| 目的 探讨基于声像图特点及人口学特征的Logistic回归模型预测乳腺影像报告和数据系统（BI-RADS）分级的价值。方法 回顾性分析5 324名女性体检者乳腺超声及人口学资料，采用多因素Logistic回归分析分别建立基于乳腺声像图特点的模型1及基于乳腺声像图特点和人口学特征的模型2，以ROC曲线分析2种模型对BI-RADS ≥ 4a级乳腺病变的预测效能。结果 超声示5 019名（5 019/5 324，94.27%）BI-RADS分级≤ 3级，305名（305/5 324，5.73%）存在BI-RADS分级≥ 4a级乳腺病变。结节数量、形态、回声、血流信号、年龄和体质量指数（BMI）是BI-RADS ≥ 4a级的独立预测因子（P均<0.05）。基于结节数量、形态、回声和血流信号构建回归模型1，其诊断BI-RADS ≥ 4a级的AUC为0.821（P<0.05），特异度90.58%，敏感度61.25%，准确率88.13%。基于结节数量、形态、回声、血流信号、年龄和BMI构建回归模型2，其AUC为0.874（P<0.05），特异度93.69%，敏感度68.75%，准确率91.80%。结论 基于声像图特点及人口学特征的模型对BI-RADS分级有一定预测价值。 |
| 英文摘要: |
| Objective To explore the value of models based on characteristics of ultrasonography and demography for predicting breast imaging reporting and data system (BI-RADS) classification. Methods Breast ultrasonic data and demographic data of 5 324 females who underwent health screening were retrospectively analyzed. Multivariate Logistic regression analysis was used to establish model 1 based on breast ultrasonic characteristics, and model 2 based on breast ultrasonic characteristics as well as demographic characteristics. ROC curve was used to analyze the efficacy of the two models for BI-RADS ≥ 4a breast lesions. Results Ultrasound showed 5 019 (5 019/5 324, 94.27%) BI-RADS ≤ 3 and 305 (305/5 324, 5.73%) BI-RADS ≥ 4a grade breast lesions. Logistic regression analysis showed that the number of nodules, morphology, echo, blood flow signal, age and body mass index (BMI) were independent predictors of BI-RADS ≥ 4a lesions (all P<0.05). Regression model 1 was constructed based on nodule number, morphology, echo and blood flow signals, with AUC of predicting BI-RADS ≥ 4a grade 0.821 (P<0.05), specificity of 90.58%, sensitivity of 61.25% and accuracy of 88.13%. Regression model 2 was constructed based on nodule number, morphology, echo, blood flow signal, age and BMI, with AUC of predicting BI-RADS ≥ 4a grade 0.874 (P<0.05), the specificity, sensitivity and accuracy was 93.69%, 68.75%, and 91.80%, respectively. Conclusion Models based on ultrasonic features and demographic characteristics have certain predictive value for BI-RADS classification. |
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