Undiagnosed Breast Cancer: Features at Supplemental Screening US

Song, Sung Eun; Cho, Nariya; Chu, A Jung; Shin, Sung Ui; Yi, Ann; Lee, Sang Ho; Kim, Won Hwa; Bae, Min Sun; Moon, Woo Kyung

doi:10.1148/radiol.2015142960

Cited by 25 publications

(21 citation statements)

References 24 publications

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“…The predisposition of breast lesions to localize to the fat-gland interface, as demonstrated in this study, reinforces the need for careful evaluation of this region when interpreting breast imaging studies using MR imaging, mammography, and ultrasound22232425. In one study, a retrospective evaluation of the prior MR imaging studies in patients with breast cancer detected a potential observer error in 47% of cases22.…”

Section: Discussionsupporting

confidence: 59%

The Spatial Relationship of Malignant and Benign Breast Lesions with Respect to the Fat-Gland Interface on Magnetic Resonance Imaging

Kim

Han

et al. 2016

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The fat-gland interface in the breast is noteworthy in that major vessels and lymphatic channels supplying the breast are located there; however, the relationship between breast lesion formation and the fat-gland interface is poorly understood. Here we evaluate the location of malignant and benign breast lesions with respect to the fat-gland interface in 881 women 50 years of age and younger, utilizing MR imaging. We find that most breast lesions are located in or near the interface in qualitative (89.7%) and quantitative (90.0%, 1 cm within the interface) analyses. This propensity for the fat-gland interface is not accounted for by breast anatomy, whereby 12.3% and 55.7% of breast volume is within 2 mm and 1 cm of the interface, respectively. Malignant lesions were located in or near the interface in significantly higher proportions than benign lesions in qualitative (94.3% vs. 67.3%, P < 0.001) and quantitative (49.7% vs. 34.5%, P < 0.001, 2 mm within the interface) analyses. This phenomenon may reflect a biological importance of the fat-gland interface in breast cancer development and progression.

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Section: Discussionsupporting

confidence: 59%

The Spatial Relationship of Malignant and Benign Breast Lesions with Respect to the Fat-Gland Interface on Magnetic Resonance Imaging

Kim

Han

et al. 2016

Sci Rep

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“…Supplemental US screening reduces the interval cancer rate in women with dense breasts [ 2 , 21 ], which in general is associated with improved outcome [ 6 ]. Unfortunately, 31% of cancers in supplemental US screening are found to be already visible on a prior screening US examination and could still have been detected earlier [ 22 ]. Reasons for non-detection in WBUS screening are usually misinterpretation and oversight errors.…”

Section: Discussionmentioning

confidence: 99%

Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts

et al. 2018

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ObjectivesTo determine the effect of computer-aided-detection (CAD) software for automated breast ultrasound (ABUS) on reading time (RT) and performance in screening for breast cancer.Material and methodsUnilateral ABUS examinations of 120 women with dense breasts were randomly selected from a multi-institutional archive of cases including 30 malignant (20/30 mammography-occult), 30 benign, and 60 normal cases with histopathological verification or ≥ 2 years of negative follow-up. Eight radiologists read once with (CAD-ABUS) and once without CAD (ABUS) with > 8 weeks between reading sessions. Readers provided a BI-RADS score and a level of suspiciousness (0-100). RT, sensitivity, specificity, PPV and area under the curve (AUC) were compared.ResultsAverage RT was significantly shorter using CAD-ABUS (133.4 s/case, 95% CI 129.2-137.6) compared with ABUS (158.3 s/case, 95% CI 153.0-163.3) (p < 0.001). Sensitivity was 0.84 for CAD-ABUS (95% CI 0.79-0.89) and ABUS (95% CI 0.78-0.88) (p = 0.90). Three out of eight readers showed significantly higher specificity using CAD. Pooled specificity (0.71, 95% CI 0.68-0.75 vs. 0.67, 95% CI 0.64-0.70, p = 0.08) and PPV (0.50, 95% CI 0.45-0.55 vs. 0.44, 95% CI 0.39-0.49, p = 0.07) were higher in CAD-ABUS vs. ABUS, respectively, albeit not significantly. Pooled AUC for CAD-ABUS was comparable with ABUS (0.82 vs. 0.83, p = 0.53, respectively).ConclusionCAD software for ABUS may decrease the time needed to screen for breast cancer without compromising the screening performance of radiologists.Key Points• ABUS with CAD software may speed up reading time without compromising radiologists’ accuracy. • CAD software for ABUS might prevent non-detection of malignant breast lesions by radiologists. • Radiologists reading ABUS with CAD software might improve their specificity without losing sensitivity.

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“…Cancers detected by ultrasound tend to be more invasive, node-negative, and smaller in size compared to mammographydetected cancers in screening (30), which may have a positive outcome on patient survival (3). A negative effect of supplemental breast ultrasound is an increase in recall rates, while up to 30% of cancers still could have been detected earlier (31). Choi et al and Vourtsis et al showed ABUS in asymptomatic women may outperform hand-held devices in terms of recall rate, but also in terms of cancer detection (9,32).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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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.

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Undiagnosed Breast Cancer: Features at Supplemental Screening US

Abstract: At supplemental screening breast US, close attention should be paid to the presence of a margin that is not circumscribed, and multiple lesions should be separately assessed to reduce the number of missed breast cancers.

Cited by 25 publications

References 24 publications

The Spatial Relationship of Malignant and Benign Breast Lesions with Respect to the Fat-Gland Interface on Magnetic Resonance Imaging

The Spatial Relationship of Malignant and Benign Breast Lesions with Respect to the Fat-Gland Interface on Magnetic Resonance Imaging

Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts

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

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