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

Abstract

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 Xiao et al. CAD in Breast Lesions 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.

Development and validation of a transformer-based CAD model for improving the consistency of BI-RADS category 3–5 nodule classification among radiologists: a multiple center study

“…These accurate category decreases assisted by the CAD model avoid unnecessary biopsies and reduce the medical costs and psychological burden of patients ( 24 ). Xiao et al demonstrated that CAD could improve the diagnostic performance of experienced radiologist and subsequently prevent the unnecessary biopsy of 54.76% of benign lesions ( 39 ); Choi et al also found that CAD could assist radiologists in correctly reclassifying BI-RADS 3 or 4a nodules ( 40 ). Conversely, an agreement rate (68/147, 46.26%) with pathology was obtained in those increases from category 3 to 4a or above.…”

Improving breast cancer diagnostics with deep learning for MRI

“…Previously, a few breast imaging studies explored the potential to downgrade BI-RADS 4 patients using predictive models, including AI-based algorithms. Works by Wang et al ( 23 ), Zhao et al ( 24 ) and Xiao et al ( 25 ) assessed a commercially available AI tool for breast ultrasound to downgrade category 4A cases to category 3. In the Wang et al, out of 43 category 4A studies, 14 were correctly downgraded.…”