False-negative results on computer-aided detection software in preoperative automated breast ultrasonography of breast cancer patients

Abstract

Epub ahead of printPurpose: The purpose of this study was to measure the cancer detection rate of computer-aided detection (CAD) software in preoperative automated breast ultrasonography (ABUS) of breast cancer patients and to determine the characteristics associated with false-negative outcomes. Methods: A total of 129 index lesions (median size, 1.7 cm; interquartile range, 1.2 to 2.4 cm) from 129 consecutive patients (mean age±standard deviation, 53.4±11.8 years) who underwent preoperative ABUS from December 2017 to February 2018 were assessed. An index lesion was defined as a breast cancer confirmed by ultrasonography (US)-guided core needle biopsy. The detection rate of the index lesions, positive predictive value (PPV), and false-positive rate (FPR) of the CAD software were measured. Subgroup analysis was performed to identify clinical and US findings associated with false-negative outcomes. Results: The detection rate of the CAD software was 0.84 (109 of 129; 95% confidence interval, 0.77 to 0.90). The PPV and FPR were 0.41 (221 of 544; 95% CI, 0.36 to 0.45) and 0.45 (174 of 387; 95% CI, 0.40 to 0.50), respectively. False-negative outcomes were more frequent in asymptomatic patients (P<0.001) and were associated with the following US findings: smaller size (P=0.001), depth in the posterior third (P=0.002), angular or indistinct margin (P<0.001), and absence of architectural distortion (P<0.001). Conclusion: The CAD software showed a promising detection rate of breast cancer. However, radiologists should judge whether CAD software-marked lesions are true-or false-positive lesions, considering its low PPV and high FPR. Moreover, it would be helpful for radiologists to consider the characteristics associated with false-negative outcomes when reading ABUS with CAD.

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

“…Peri-areolar shadowing is defined as a nipple artifact [30]. Despite the promising detection rate with CAD software in breast cancer, radiologists should determine whether a CAD software-marked lesion is a true-or false-positive lesion, given its positive predictive value and high false-positive rate [17]. The knowledge of these artifacts improves the diagnostic performance of radiologists.…”

“…According to the manual from the manufacturer, FPR was defined as the total number of false-positive CAD markers in non-cancer volumes divided by the total number of non-cancer volumes. In this study, we set the FPR to 0.2 (i.e., 1 false-positive CAD marker in non-cancer volume per 5 non-cancer volumes), which was its default setting as in previous studies [16][17][18].…”

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 ].…”