The value of deep learning technology based on mammography in differentiating breast imaging reporting and data system category 3 and 4 lesions
10.3760/cma.j.cn112149-20220127-00080
- VernacularTitle:基于乳腺X线摄影的深度学习技术鉴别乳腺影像报告和数据系统3类与4类疾病的价值
- Author:
Rushan OUYANG
1
;
Lin LI
;
Xiaohui LIN
;
Xiaohui LAI
;
Zengyan LI
;
Jie MA
Author Information
1. 暨南大学第二临床医学院，深圳 518020
- Keywords:
Breast neoplasms;
Mammography;
Artificial intelligence;
Deep learning
- From:
Chinese Journal of Radiology
2023;57(2):166-172
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of deep learning technology based on mammography in differentiating for breast imaging reporting and data system (BI-RADS) category 3 and 4 lesions.Methods:The clinical and imaging data of 305 patients with 314 lesions assessed as BI-RADS category 3 and 4 by mammography were analyzed retrospectively in Shenzhen People′s Hospital and Shenzhen Luohu People′s Hospital from January to December 2020. All 305 patients were female, aged 21 to 83 (47±12) years. Two general radiologists (general radiologist A and general radiologist B) with 5 and 6 years of work experience and two professional breast imaging diagnostic radiologists (professional radiologist A and professional radiologist B) with 21 years of work experience and specialized breast imaging training were randomly assigned to read the imaging independently at a 1∶1 ratio, and then to read the imaging again in combination with the deep learning system. Finally, breast lesions were reclassified into BI-RADS category 3 or 4. The receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the diagnostic performance, and the differences of AUCs were compared by DeLong method.Results:The AUC of general radiologist A combined with deep learning system to reclassify BI-RADS category 3 and 4 breast lesions was significantly higher than that of general radiologist A alone (AUC=0.79, 0.63, Z=2.82, P=0.005, respectively). The AUC of general radiologist B combined with deep learning system to reclassify BI-RADS category 3 and 4 breast lesions was significantly higher than that of general radiologist B (AUC=0.83, 0.64, Z=3.32, P=0.001, respectively). There was no significant difference in the AUCs between professional radiologist A combined with deep learning system and professional radiologist A, and professional radiologist B combined with deep learning system and professional radiologist B in reclassifying BI-RADS category 3 and 4 breast lesions ( P>0.05). Conclusion:The deep learning system based on mammography is more effective in assisting general radiologists to differentiate between BI-RADS category 3 and 4 lesions.
