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基于MRI列线图模型对乳腺良恶性结节的预测价值研究

Study on the Predictive Diagnostic Value of Benign and Malignant BreastNodules Based on the MRI Nomogram Model
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摘要 采用多因素Logistic回归分析构建乳腺结节的MRI多参数诊断预测模型,并验证该模型的诊断效能。回顾性分析乳腺病变患者共205个病灶,其中恶性病灶113个,良性病灶92个。观察良恶性病灶的形态学及动力学特征。以恶性组为实验组,以良性组为对照组,将205例病灶随机分为训练集样本(174例)和外部验证测试集样本(31例)。通过统计学分析筛选指标,用训练集数据构建预测模型并绘制其列线图;采用外部验证测试集样本验证模型诊断的一致性;绘制受试者操作特征(ROC)曲线并通过计算曲线下面积(AUC)来验证模型的区分度,以预测模型在乳腺良恶性结节鉴别诊断中的敏感度、特异度及准确度来评价其诊断效能。经过筛选后纳入预测模型的独立危险因素为患者年龄以及病灶的ADC值、TIC曲线、病灶大小及强化特征5项指标。经研究,预测模型在此次样本中对乳腺良恶性结节预测准确率达92.2%。因此,基于Logistic回归分析法构建的乳腺多参数MRI预测模型,其列线图对乳腺良恶性结节的预测具有较高的参考价值。 Multivariate Logistic regression analysis was used to construct an MRI multiparametric diagnostic prediction model of breast nodules and to verify the diagnostic efficacy of this model.A total of 205 lesions were retrospective analysis in patients with breast lesions,including 113 malignant lesions and 92 benign lesions.The morphological and kinetic characteristics of the benign and malignant lesions were observed.Using the malignant group as the experimental group and the benign group as the control group,205 lesions were randomly divided into 174 samples in the training set and 31 samples in the external validation test set.Through statistical analysis,build the prediction model with the training set data and draw its nomogram;use the external verification test set sample to verify the consistency of model diagnosis;draw the receiver operator characteristic(ROC)curve and verify the differentiation of the model by calculating the area under the curve(AUC)to evaluate the sensitivity,specificity and accuracy of the model in the differential diagnosis of benign and malignant breast nodules.After screening,the independent risk factors included in the prediction model were patient age and lesion ADC value,TIC curve,lesion size,and enhancement characteristics. After the study, the prediction accuracy of the prediction model for benign and malignant breast nodules reached 92.2% in this sample. Therefore, the nomogram based on Logistic regression analysis has a high reference value for the prediction of benign and malignant breast nodules.
作者 杨巧飞 杨普 毕孝杨 李正亮 杨璐帆 唐艳隆 YANG Qiaofei;YANG Pu;BI Xiaoyang;LI Zhengliang;YANG Lufan;TANG Yanlong(Department of Radiology,the First Affiliated Hospital of Dali University,Dali 671000,Yunnan,P.R.China;Department of Radiology,No.2 People’s Hospital of Dali,Dali 671003,Yunnan,P.R.China)
出处 《影像科学与光化学》 CAS 北大核心 2023年第6期338-344,共7页 Imaging Science and Photochemistry
基金 云南省省校合作地方高校联合专项项目(202001BA070001-149) 云南省卫生健康委员会医学后备人才培养计划( H-2018010 )。
关键词 乳腺癌 动态增强磁共振成像 列线图 预测模型 breast cancer dynamic enhancement of the MRI nomogram prediction model
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