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基于MRI影像组学模型对BI-RADS 4类乳腺肿块的诊断价值分析OA

Analysis for diagnostic value of MRI-based radiomics model for BI-RADS category 4 breast masses

中文摘要英文摘要

目的:探讨磁共振成像(MRI)影像组学模型对乳腺影像报告和数据系统(BI-RADS)4类乳腺肿块的诊断价值.方法:回顾性选取2023年2月至2025年2月中国人民解放军第八十三集团军医院及河南医药大学第一附属医院收治的157例BI-RADS 4类乳腺肿块患者,根据乳腺肿块性质将其分为良性组(33例)和恶性组(124例).提取乳腺肿块853项MRI影像组学特征,采用Mann-Whitney U检验筛选良恶性肿块间差异显著的特征,通过分层随机抽样法以7∶3比例,将157例BI-RADS 4类乳腺肿块患者分为训练集(110例)和测试集(47例),构建MRI多维度影像组学logistic回归模型(简称影像组学模型).依据BI-RADS 4类恶性风险差异,将157例患者分为4A组(89例,恶性风险为2%~10%)、4B组(45例,恶性风险10%~50%)和4C组(23例,恶性风险50%~95%)3个亚组,在测试集验证影像组学模型的诊断效能,并与传统BI-RADS分类影像组学诊断方法进行对比,采用受试者工作特征(ROC)曲线下面积(AUC)分析3个亚组的诊断效能.结果:筛选出56项差异显著特征,最终构建的模型独立预测10项乳腺肿块良恶性的独立影像组学特征.影像组学模型在测试集中的AUC为0.941,显著高于BI-RADS分类的0.785,差异有统计学意义(Z=3.856,P<0.001),影像组学模型的灵敏度、特异度和准确率分别为92.7%、84.8%和90.4%.3个亚组分析显示,影像组学模型在恶性风险的4A、4B和4C组中AUC分别为0.938、0.945和0.951,均>0.93,4C组准确率为95.7%,阳性预测值为100%.结论:MRI影像组学模型对BI-RADS 4类乳腺肿块具有较高诊断效能,尤其在高风险诊断中表现优异,可为临床精准鉴别提供参考.

Objective:To explore the diagnostic value of magnetic resonance imaging(MRI)radiomics models for breast imaging reporting and data system(BI-RADS)category 4 breast masses.Methods:A retrospective selection was conducted on 157 patients with BI-RADS category 4 breast masses who admitted to the 83th Group Military Hospital of Chinese People's Liberation Group Force,and the First Affiliated Hospital of Henan Medical University from February 2023 to February 2025.According to the features of breast tumor,they were divided into benign group(33 cases)and malignant group(124 cases).A total of 853 features of MRI radiomics of breast masses were extracted.The Mann-Whitney U test was adopted to screen the features with significant differences between benign and malignant masses.Using stratified random sampling,157 patients with BI-RADS category 4 breast masses were divided into a training set(110 cases)and a test set(47 cases)as a ratio of 7 to 3,which was used to construct a multi-dimensional logistic regression model with MRI radiomics(abbreviation:radiomics model).Based on the difference of the malignant risk of BI-RADS category 4,the 157 patients were divided further into 3 subgroups:group 4A(89 cases,with a malignant risk from 2%to 10%),group 4B(45 cases,with a malignant risk from 10%to 50%),and group 4C(23 cases,with a malignant risk from 50%to 95%).The diagnostic efficacy of the model was verified in the test set.Compared with the diagnostic method of conventional BI-RADS classification radiomics,the area under curve(AUC)of the receiver operating characteristic(ROC)curve was used to analyze the diagnostic efficacy of the three groups.Results:A total of 56 features with significant difference were selected,and the finally constructed model can independently predict 10 independent radiomics features of benign and malignant features of breast masses.The AUC value of the radiomics model was 0.941 in the test set,which was significantly higher than 0.785 of the BI-RADS classification,and the difference was statistically significant(Z=3.856,P<0.001).The sensitivity,specificity,and accuracy of the radiomics model were respectively 92.7%,84.8%and 90.4%.The analysis of three-group showed that the AUC values of the radiomics model were respectively 0.938,0.945 and 0.951 in the group 4A,4B,and 4C of malignant risk,and all of them were>0.93,and the accuracy and positive predictive value of the group 4C were respectively 95.7%and 100%.Conclusion:The MRI radiomics model has a high diagnostic efficacy for BI-RADS category 4 of breast masses,which has especially exceptional performance in diagnosing high risk.It can provide references for clinically precise identification.

王肖肖;梁长华;郭利茹;田方;孙运帮

河南医药大学第一临床学院 新乡 453000||中国人民解放军陆军第八十三集团军医院影像中心 新乡 453000河南医药大学第一临床学院 新乡 453000||河南医药大学第一附属医院放射科 卫辉 453100中国人民解放军陆军第八十三集团军医院影像中心 新乡 453000中国人民解放军陆军第八十三集团军医院影像中心 新乡 453000中国人民解放军陆军第八十三集团军医院影像中心 新乡 453000

医药卫生

乳腺肿块乳腺影像报告和数据系统(BI-RADS)4类磁共振成像(MRI)影像组学诊断

Breast massBreast imaging reporting and date system(BI-RADS)category 4Magnetic resonance imaging(MRI)RadiomicsDiagnosis

《中国医学装备》 2026 (2)

52-57,6

河南省医学科技攻关计划(LHGJ20240494) Henan Province Medical Science and Technology Research Program(LHGJ20240494)

10.3969/j.issn.1672-8270.2026.02.011

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