基于对比增强能谱X线摄影的预测模型对乳腺肿块的诊断价值

Development of A CESM Predictive Model for the Diagnosis of Breast Masses

目的建立基于对比增强能谱X线摄影(CESM)的预测模型用于预测乳腺肿块的恶性概率,并评价其预测效能。方法回顾性纳入328例乳腺肿块患者临床及影像资料,随机分为训练组(235例)和测试组(93例)。以病理结果为金标准,采用单因素和多因素Logistic回归分析筛选预测因子,构建预测模型并绘制列线图。采用ROC的曲线下面积(AUC)、校准曲线和决策曲线分析评估预测模型的效能。结果328例乳腺肿块患者中恶性179例,良性149例。年龄、分叶征、毛刺征、早期(CC)强化程度、晚期(MLO)强化均匀性、强化曲线共6个独立预测因子用于建立预测模型。在训练组和验证组中,预测模型均具有较好的效能,AUC值分别为0.975、0.963。校准曲线及决策曲线提示预测结果接近实际结果,该预测模型有良好的临床应用价值。结论基于CESM的预测模型通能够协助医师预测乳腺肿块的恶性概率,为临床决策提供指导。

Objective To develop a CESM model for predicting malignant probability of breast masses,and to evaluate its predictive.Methods The clinical and imaging data of 328 patients were analyzed retrospectively,which were randomly divided into training set(235 cases)and verification set(93 cases).To pathology results for the gold standard,the predictors were screened by univariate and multivariate Logistic regression analysis.Then a predictive model was constructed based on the results with a nomogram drawn.Performances of predictive models were evaluated with area under the curve(AUC)of ROC,calibration curve,and decision curve analysis(DCA).Results A total of 328 women with breast neoplasia including 179 malignant and 149 benign was collected.Six predictive factors were harvested to construct the predictive model,which included age,lobulation,spiculation,early-phase(CC)enhancement degree,late-phase(MLO)enhancement heterogeneity or enhancement curve.The predictive model had good performance in both training set and verification set,with the AUC values of 0.975 and 0.963,respectively.The calibration and DCA curve showed that the predictive result was much closer to actual result and this model had good clinical application values.Conclusion The CESM predictive model can predict malignancy probability of breast masses,which can be used as a referable method for radiologist and to provide guidance for clinical practice.