摘要
目的:探讨乳腺影像报告和数据系统(BI-RADS)分类联合影像组学预测模型鉴别X线摄影不同表现类型乳腺病灶良性与恶性的效能。方法:回顾性分析东南大学附属中大医院2013年5月至2020年8月接受乳腺X线摄影检查并经病理证实的2055例女性患者。根据BI-RADS分类第5版将病灶分为肿块型及非肿块型,肿块型又分为小肿块(最大径≤2 cm)、大肿块(最大径>2 cm),非肿块型又分为非对称、钙化及结构扭曲。通过手动分割病灶感兴趣区提取影像组学特征并构建影像组学模型。使用受试者操作特征曲线及曲线下面积(AUC)评估BI-RADS分类、影像组学及两者联合鉴别诊断乳腺X线摄影不同表现类型良性与恶性病变的效能,采用DeLong检验比较3种模型的AUC。结果:BI-RADS分类、影像组学模型及BI-RADS分类联合影像组学模型诊断乳腺病灶良性与恶性AUC值分别为0.924±0.006、0.827±0.009及0.947±0.005;与BI-RADS分类、影像组学模型比较,联合模型的诊断的AUC最高,差异具有统计学意义(Z值分别为9.29、14.94,P<0.001)。联合模型鉴别大肿块、小肿块及非肿块乳腺病灶良性与恶性的AUC(分别为0.958±0.007、0.933±0.013、0.939±0.008)均高于BI-RADS分类(AUC分别为0.937±0.010、0.896±0.020、0.916±0.011,Z值分别为5.32、3.90、5.08,P<0.001)、影像组学模型(AUC分别为0.872±0.012、0.851±0.021、0.758±0.016,Z值分别为7.86、4.53、12.13,P<0.001)。联合模型诊断非对称乳腺病灶良性与恶性的AUC(0.897±0.017)高于BI-RADS分类(AUC为0.866±0.020,Z=4.27,P<0.001)、影像组学模型(AUC为0.633±0.029,Z=7.44,P<0.001);而联合模型诊断诊断钙化和结构扭曲乳腺病灶良性与恶性的AUC(分别为0.971±0.010、0.811±0.057)仅高于影像组学模型(AUC分别为0.827±0.021、0.586±0.075,Z值分别为7.40、3.15,P<0.001),与BI-RADS分类差异无统计学意义(AUC分别为0.959±0.012、0.800±0.061,Z分别为1.87、0.39,P>0.05)。结论:BI-RADS分类结合影像组学模型预测X线摄影不同表现类型乳腺病灶良性与恶性的效能较高,具有重要的临床应用价值。
Objective To evaluate the differential diagnostic efficacy of a predictive model of breast imaging reporting and data system(BI-RADS)classification combined with mammography radiomics classifier for various X-ray phenotype of breast lesions.Methods A retrospective analysis was performed on 2055 female patients who underwent mammography examination and were confirmed by pathology from May 2013 to August 2020 in Zhongda Hospital,Southeast University.Breast lesion was classified into mass or non-mass according to the fifth edition of BI-RADS.The mass was further divided into small mass(maximum diameter≤2 cm)and large mass(maximum diameter>2 cm),the non-mass was further divided into asymmetric,calcification and structural distortions.By manually segmenting the region of interest of the lesion,the radiomics features were extracted and the model was constructed.Receiver operating characteristic curve and area under the curve(AUC)were used to assess the diagnostic efficacy of the BI-RADS classification,the radiomics model and the combined model for various phenotypes of breast lesions.Differences among the AUC were analyzed by the DeLong test.Results The AUCs based on the BI-RADS classification,the radiomics model and the combined model were 0.924±0.006,0.827±0.009 and 0.947±0.005 respectively.Compared with BI-RADS classification and the radiomics model,AUC of the combined model was the highest,and the differences were statistically significant(Z=9.29,14.94,P<0.001).For large mass,small mass and non-mass,combined model(AUC=0.958±0.007,0.933±0.013,0.939±0.008)showed the best performance when compared to the BI-RADS classification(AUC=0.937±0.010,0.896±0.020,0.916±0.011;Z=5.32,3.90,5.08,P<0.001)or the radiomics model(AUC=0.872±0.012,0.851±0.021,0.758±0.016;Z=7.86,4.53,12.13,P<0.001).The AUC of the combined model for benign and malignant asymmetric breast lesions(0.897±0.017)was higher than that of the BI-RADS classification(AUC=0.866±0.020,Z=4.27,P<0.001)and the radiomics model(AUC=0.633±0.029,Z=7.44,P<0.001);however,the AUC of the combined model for benign and malignant calcification and structural distortion of breast lesions(0.971±0.010,0.811±0.057,respectively)was only higher than that of the radiomics model(AUC=0.827±0.021,0.586±0.075,Z=7.40,3.15,P<0.001),and there was no significant difference with the BI-RADS classification(AUC=0.959±0.012,0.800±0.061,Z=1.87,0.39,P>0.05).Conclusion The combined model shows better differential diagnostic performance,which is valued in the clinical application.
作者
赵晓慧
刘万花
彭程宇
叶媛媛
王瑞
高飞
张番栋
Zhao Xiaohui;Liu Wanhua;Peng Chengyu;Ye Yuanyuan;Wang Rui;Gao Fei;Zhang Fandong(Department of Radiology,Zhongda Hospital,Southeast University,Nanjing 210009,China;Deepwise AI Lab,Beijing 100080,China)
出处
《中华放射学杂志》
CAS
CSCD
北大核心
2022年第6期643-649,共7页
Chinese Journal of Radiology
基金
东南大学附属中大医院横向科研项目(2018010011)。
