影像科学与光化学2023，Vol.41Issue(6)：338-344,7.DOI:10.7517/issn.1674-0475.230814
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 Breast Nodules Based on the MRI Nomogram Model
摘要
Abstract
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.关键词
乳腺癌/动态增强磁共振成像/列线图/预测模型Key words
breast cancer/dynamic enhancement of the MRI/nomogram/prediction model引用本文复制引用
杨巧飞,杨普,毕孝杨,李正亮,杨璐帆,唐艳隆..基于MRI列线图模型对乳腺良恶性结节的预测价值研究[J].影像科学与光化学,2023,41(6):338-344,7.基金项目
云南省省校合作地方高校联合专项项目(202001BA070001-149) （202001BA070001-149）
云南省卫生健康委员会医学后备人才培养计划(H-2018010) （H-2018010）
