The value of breast imaging reporting and data system classification combined with radiomics in differentiating benign from malignant breast lesions with different X-ray phenotypes
10.3760/cma.j.cn112149-20210621-00589
- VernacularTitle:乳腺影像报告和数据系统分类联合影像组学模型鉴别不同X线表型乳腺病灶良性与恶性的价值
- Author:
Xiaohui ZHAO
1
;
Wanhua LIU
;
Chengyu PENG
;
Yuanyuan YE
;
Rui WANG
;
Fei GAO
;
Fandong ZHANG
Author Information
1. 东南大学附属中大医院放射科，南京 210009
- Keywords:
Breast neoplasms;
Radiomics;
Mammography;
Diagnosis, differential
- From:
Chinese Journal of Radiology
2022;56(6):643-649
- CountryChina
- Language:Chinese
-
Abstract:
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 2 055 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.
