Evaluation of Computer-Aided Detection (CAD) in Screening Automated Breast Ultrasound Based on Characteristics of CAD Marks and False-Positive Marks

Abstract

The present study evaluated the effectiveness of computer-aided detection (CAD) system in screening automated breast ultrasound (ABUS) and analyzed the characteristics of CAD marks and the causes of false-positive marks. A total of 846 women who underwent ABUS for screening from January 2017 to December 2017 were included. Commercial CAD was used in all ABUS examinations, and its diagnostic performance and efficacy in shortening the reading time (RT) were evaluated. In addition, we analyzed the characteristics of CAD marks and the causes of false-positive marks. A total of 1032 CAD marks were displayed based on the patient and 534 CAD marks on the lesion. Five cases of breast cancer were diagnosed. The sensitivity, specificity, PPV, and NPV of CAD were 60.0%, 59.0%, 0.9%, and 99.6% for 846 patients. In the case of a negative study, it was less time-consuming and easier to make a decision. Among 530 false-positive marks, 459 were identified clearly for pseudo-lesions; the most common cause was marginal shadowing, followed by Cooper’s ligament shadowing, peri-areolar shadowing, rib, and skin lesions. Even though CAD does not improve the performance of ABUS and a large number of false-positive marks were detected, the addition of CAD reduces RT, especially in the case of negative screening ultrasound.

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“…AI can detect breast lumps (masses), mass segmentation, breast density, and the risk of breast cancer [172]. However, many scientists have identified challenging issues in these techniques [173]. miRNAs can also be useful for classifying different subtypes of BC; several studies in the literature have shown a particular pattern of expression of miRNAs for each subtype [174,175].…”

Ultrasound for Breast Cancer Screening in Resource-Limited Settings: Current Practice and Future Directions

“…Due to the considerable amount of ABUS images, reviewing a full ABUS examination can be burdensome and malignant lesions may be overlooked. CAD software has been introduced to assist radiologists in interpretating images and generating accurate diagnosis [ 78 , 79 , 80 , 81 ], which would be a promising solution in LMICs with a lack of healthcare staff. A study in China evaluated the role of CAD in decreasing ABUS reading times and increasing the diagnostic accuracy of junior radiologists [ 82 ].…”

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“…Further, with the potential to generate several hundred images per study ABUS is often viewed as time consuming. Algorithms may help decrease these barriers to ABUS implementation as AI-based solutions have shown promise to both increase detection and decrease reading time [ 15 , 18 ]. An article by Van Zelst et al showed that CAD software provided by Qview Medical Inc. (Los Altos, CA, USA) can be used to increase the cancer detection rate of a radiologist interpreting ABUS images, and another study by Yang et al found that both the performance and reading time of ABUS images can be improved by using AI-based software [ 15 , 19 ].…”

“…In addition to comparing across time points, radiologists also consider similarities and differences between the right and left breast when analyzing a study. Organ symmetry has been effectively employed in image analysis of other body regions, such as the mastoid air cells for detection of mastoiditis [ 18 ]. The integration of breast symmetry information in the academic literature is still evolving, however.…”