Automated breast ultrasound system (ABUS): reproducibility of mass localization, size measurement, and characterization on serial examinations

Chang, Jung Min; Hee, Joo; Park, Jeong Seon; Kim, Seung Ja; Moon, Woo Kyung

doi:10.1177/0284185114551565

Cited by 42 publications

(28 citation statements)

References 26 publications

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“…ABVS has emerged as a digital and automatic3D US technology withtheadvantages of less operatordependence and high reproducibility [14].It can reconstruct entire breast images from coronal and sagittal views, allowing multiplanar display and true US tomography of the breast [13]. A meta-analysis showed high sensitivity and specificity of ABVS in differentiating benign and malignant breast lesions (92% and 84.9%, respectively) [24], and other studies demonstrated equivalent diagnostic accuracy of ABVS compared with conventional US [8,9,10,25].…”

Section: Discussionmentioning

confidence: 99%

“…Because of the similar usefulness of BI-RADS-US descriptors in diagnosing breast lesions, it is significant to know how retraction phenomenon performed compared with BI-RADS-US descriptors in the differential diagnoses of breast lesions. Although the literature concerning the clinical application of ABVS has been currently increasing, studies have mainly focused on thediagnostic value of ABVS [11,12,13], its reproducibility [14,15,16], itscomparison with conventional US or mammography [8,9,10,17], disease size measurement [18,19], and its application to other organs or tissues [20,21]. To our knowledge, no investigators have compared the diagnostic performance of retraction phenomenon with BI-RADS-US descriptors using an ABVS.…”

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confidence: 99%

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Comparison of retraction phenomenon and BI-RADS-US descriptors in differentiating benign and malignant breast masses using an automated breast volume scanner

Zheng

Yan

Huang

et al. 2015

European Journal of Radiology

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Comparison of retraction phenomenon and BI-RADS-US descriptors in differentiating benign and malignant breast masses using an automated breast volume scanner

Zheng

Yan

Huang

et al. 2015

European Journal of Radiology

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“…Handheld ultrasound (HHUS) is a standard imaging modality of the breast; however, HHUS has several drawbacks, such as a long examination time and low rate of reproducibility [3][4][5]. Automated breast ultrasound (ABUS) can overcome the shortcomings of HHUS [6][7][8] while maintaining diagnostic performance [9][10][11][12]. Furthermore, ABUS provides a wide field of view and coronal sections of the breast, which has the advantage of showing spiculated margins of breast cancer and architectural distortion [10,13].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…In addition, the generated coronal plane may offer new diagnostic information (4). It was proposed that ABUS may be used to monitor breast cancer patients treated with NAC (5). …”

Section: Introductionmentioning

confidence: 99%

Early prediction of pathological outcomes to neoadjuvant chemotherapy in breast cancer patients using automated breast ultrasound

Wang

Huo

et al. 2016

Chinese Journal of Cancer Research

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ObjectiveEarly assessment of response to neoadjuvant chemotherapy (NAC) for breast cancer allows therapy to be individualized. The optimal assessment method has not been established. We investigated the accuracy of automated breast ultrasound (ABUS) to predict pathological outcomes after NAC.MethodsA total of 290 breast cancer patients were eligible for this study. Tumor response after 2 cycles of chemotherapy was assessed using the product change of two largest perpendicular diameters (PC) or the longest diameter change (LDC). PC and LDC were analyzed on the axial and the coronal planes respectively. Receiver operating characteristic (ROC) curves were used to evaluate overall performance of the prediction methods. Youden's indexes were calculated to select the optimal cut-off value for each method. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) and the area under the ROC curve (AUC) were calculated accordingly.ResultsypT0/is was achieved in 42 patients (14.5%) while ypT0 was achieved in 30 patients (10.3%) after NAC. All four prediction methods (PC on axial planes, LDC on axial planes, PC on coronal planes and LDC on coronal planes) displayed high AUCs (all>0.82), with the highest of 0.89 [95% confidence interval (95% CI), 0.83-0.95] when mid-treatment ABUS was used to predict final pathological complete remission (pCR). High sensitivities (85.7%-88.1%) were observed across all four prediction methods while high specificities (81.5%-85.1%) were observed in two methods used PC. The optimal cut-off values defined by our data replicate the WHO and the RECIST criteria. Lower AUCs were observed when mid-treatment ABUS was used to predict poor pathological outcomes.ConclusionsABUS is a useful tool in early evaluation of pCR after NAC while less reliable when predicting poor pathological outcomes.

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Automated breast ultrasound system (ABUS): reproducibility of mass localization, size measurement, and characterization on serial examinations

Abstract: The ABUS provided reproducible images for mass localization, size measurement, and characterization, which may be useful for follow-up studies.

Cited by 42 publications

References 26 publications

Comparison of retraction phenomenon and BI-RADS-US descriptors in differentiating benign and malignant breast masses using an automated breast volume scanner

Comparison of retraction phenomenon and BI-RADS-US descriptors in differentiating benign and malignant breast masses using an automated breast volume scanner

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

Early prediction of pathological outcomes to neoadjuvant chemotherapy in breast cancer patients using automated breast ultrasound

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