Validation of radiologists’ findings by computer-aided detection (CAD) software in breast cancer detection with automated 3D breast ultrasound: a concept study in implementation of artificial intelligence software

Abstract

Background Computer-aided detection software for automated breast ultrasound has been shown to have potential in improving the accuracy of radiologists. Alternative ways of implementing computer-aided detection, such as independent validation or preselecting suspicious cases, might also improve radiologists’ accuracy. Purpose To investigate the effect of using computer-aided detection software to improve the performance of radiologists by validating findings reported by radiologists during screening with automated breast ultrasound. Material and Methods Unilateral automated breast ultrasound exams were performed in 120 women with dense breasts that included 60 randomly selected normal exams, 30 exams with benign lesions, and 30 malignant cases (20 mammography-negative). Eight radiologists were instructed to detect breast cancer and rate lesions using BI-RADS and level-of-suspiciousness scores. Computer-aided detection software was used to check the validity of radiologists' findings. Findings found negative by computer-aided detection were not included in the readers’ performance analysis; however, the nature of these findings were further analyzed. The area under the curve and the partial area under the curve for an interval in the range of 80%–100% specificity before and after validation of computer-aided detection were compared. Sensitivity was computed for all readers at a simulation of 90% specificity. Results Partial AUC improved significantly from 0.126 (95% confidence interval [CI] = 0.098–0.153) to 0.142 (95% CI = 0.115–0.169) ( P = 0.037) after computer-aided detection rejected mostly benign lesions and normal tissue scored BI-RADS 3 or 4. The full areas under the curve (0.823 vs. 0.833, respectively) were not significantly different ( P = 0.743). Four cancers detected by readers were completely missed by computer-aided detection and four other cancers were detected by both readers and computer-aided detection but falsely rejected due to technical limitations of our implementation of computer-aided detection validation. In this study, validation of computer-aided detection discarded 42.6% of findings that were scored BI-RADS ≥3 by the radiologists, of which 85.5% were non-malignant findings. Conclusion Validation of radiologists’ findings using computer-aided detection software for automated breast ultrasound has the potential to improve the performance of radiologists. Validation of computer-aided detection might be an efficient tool for double-reading strategies by limiting the amount of discordant cases needed to be double-read.

Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network

“…Commercially available AI CAD software based on minimum intensity projection visualization (MinIP) improves sensitivity of the readout by 5.2–10.6%, but may come with a possible decrease of specificity in the range of 1.4–5.7%, while additionally requiring parameter adjustment for readout that affects sensitivity [ 36 ]. Even though proven to work in a clinical environment, those methods come with limitations in regard to irregularities and various lesion sizes what may lead to an increased number of false-positives [ 37 ] and may require parameter fine-tuning and extensive image preprocessing [ 38 ]. The presented method provides improved generalization and allows a possibility for customization for specific manufacturers and adjustment to internal work processes.…”

Automated Breast Ultrasound: Technical Aspects, Impact on Breast Screening, and Future Perspectives

“…The reading time was significantly reduced by 15% overall [46]. CAD discarded 42.6% of BI-RADS ≥3 lesions, 85.5% of which were benign, suggesting that CAD software could improve accuracy and potentially reduce unnecessary recalls [47].…”

The development of breast radiology: the Acta Radiologica perspective

“…a high number of false-positive markings (95)(96)(97). Machine-learning-based methods for the detection of breast cancer are now promoted as the "new radiologists" with applications extended from mammography to DBT as well as to three-dimensional whole breast ultrasound (98,99).…”