Diagnostic Value of Breast Lesions Between Deep Learning-Based Computer-Aided Diagnosis System and Experienced Radiologists: Comparison the Performance Between Symptomatic and Asymptomatic Patients

• Main Authors: Mengsu Xiao, Chenyang Zhao, Jianchu Li, Jing Zhang, He Liu, Ming Wang, Yunshu Ouyang, Yixiu Zhang, Yuxin Jiang, Qingli Zhu
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-07-01
• Series: Frontiers in Oncology
• Subjects: computer-aided diagnosis; deep learning; breast; ultrasound; symptomatic
• Online Access: https://www.frontiersin.org/article/10.3389/fonc.2020.01070/full

Purpose: The purpose of this study was to compare the diagnostic performance of breast lesions between deep learning-based computer-aided diagnosis (deep learning-based CAD) system and experienced radiologists and to compare the performance between symptomatic and asymptomatic patients.Methods: From January to December 2018, a total of 451 breast lesions in 389 consecutive patients were examined (mean age 46.86 ± 13.03 years, range 19–84 years) by both ultrasound and deep learning-based CAD system, all of which were biopsied, and the pathological results were obtained. The lesions were diagnosed by two experienced radiologists according to the fifth edition Breast Imaging Reporting and Data System (BI-RADS). The final deep learning-based CAD assessments were dichotomized as possibly benign or possibly malignant. The diagnostic performances of the radiologists and deep learning-based CAD were calculated and compared for asymptomatic patients and symptomatic patients.Results: There were 206 asymptomatic screening patients with 235 lesions (mean age 45.06 ± 10.90 years, range 21–73 years) and 183 symptomatic patients with 216 lesions (mean age 50.03 ± 14.97 years, range 19–84 years). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and area under the receiver operating characteristic curve (AUC) of the deep learning-based CAD in asymptomatic patients were 93.8, 83.9, 75.0, 96.3, 87.2, and 0.89%, respectively. In asymptomatic patients, the specificity (83.9 vs. 66.5%, p < 0.001), PPV (75.0 vs. 59.4%, p = 0.013), accuracy (87.2 vs. 76.2%, p = 0.002) and AUC (0.89 to 0.81, p = 0.0013) of CAD were all significantly higher than those of the experienced radiologists. The sensitivity (93.8 vs. 80.0%), specificity (83.9 vs. 61.8%,), accuracy (87.2 vs. 73.6%) and AUC (0.89 vs. 0.71) of CAD were all higher for asymptomatic patients than for symptomatic patients. If the BI-RADS 4a lesions diagnosed by the radiologists in asymptomatic patients were downgraded to BI-RADS 3 according to the CAD, then 54.8% (23/42) of the lesions would avoid biopsy without missing the malignancy.Conclusion: The deep learning-based CAD system had better performance in asymptomatic patients than in symptomatic patients and could be a promising complementary tool to ultrasound for increasing diagnostic specificity and avoiding unnecessary biopsies in asymptomatic screening patients.