Radiologists’ performance in the detection of benign and malignant masses with 3D automated breast ultrasound (ABUS)

Chang, Jung Min; Moon, Woo Kyung; Cho, Nariya; Park, Jeong Seon; Kim, Seung Ja

doi:10.1016/j.ejrad.2011.01.074

Cited by 54 publications

(46 citation statements)

References 21 publications

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“…Although Wang et al (16) reported a superior sensitivity of ABUS for lesions less than 1 cm in diameter, in our study, the mean diameter of the ABUS-detected lesions was larger than that of the undetected lesions, and with statistical significance. This finding is consistent with those of several other reports (10,11,20). And the higher the lesion's BI-RADS category (i.e.…”

Section: Discussionsupporting

confidence: 95%

Comparison of automated breast ultrasonography to handheld ultrasonography in detecting and diagnosing breast lesions

et al. 2015

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Section: Discussionsupporting

confidence: 95%

Comparison of automated breast ultrasonography to handheld ultrasonography in detecting and diagnosing breast lesions

et al. 2015

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“…In some studies, ABUS exhibited a sensitivity that was as high as that of HHUS [9,10]. Other studies showed lower sensitivity than expected [12,13]. The high false-positive rate of ABUS is also problematic [9,10,12].…”

Section: Introductionmentioning

confidence: 99%

Computer-aided detection system for masses in automated whole breast ultrasonography: development and evaluation of the effectiveness

et al. 2014

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Purpose:The aim of this study was to evaluate the performance of a proposed computer-aided detection (CAD) system in automated breast ultrasonography (ABUS).Methods:Eighty-nine two-dimensional images (20 cysts, 42 benign lesions, and 27 malignant lesions) were obtained from 47 patients who underwent ABUS (ACUSON S2000). After boundary detection and removal, we detected mass candidates by using the proposed adjusted Otsu's threshold; the threshold was adaptive to the variations of pixel intensities in an image. Then, the detected candidates were segmented. Features of the segmented objects were extracted and used for training/testing in the classification. In our study, a support vector machine classifier was adopted. Eighteen features were used to determine whether the candidates were true lesions or not. A five-fold cross validation was repeated 20 times for the performance evaluation. The sensitivity and the false positive rate per image were calculated, and the classification accuracy was evaluated for each feature.Results:In the classification step, the sensitivity of the proposed CAD system was 82.67% (SD, 0.02%). The false positive rate was 0.26 per image. In the detection/segmentation step, the sensitivities for benign and malignant mass detection were 90.47% (38/42) and 92.59% (25/27), respectively. In the five-fold cross-validation, the standard deviation of pixel intensities for the mass candidates was the most frequently selected feature, followed by the vertical position of the centroids. In the univariate analysis, each feature had 50% or higher accuracy.Conclusion:The proposed CAD system can be used for lesion detection in ABUS and may be useful in improving the screening efficiency.

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“…However, the values obtained from the assessment of breast masses were inconsistent. Chang et al (2011) reported that both automated breast US and handheld US had high sensitivity (both 100%) and high specificity (95.0% and 85.0%, respectively) for 69 lesions. In addition, the new technique had a higher diagnostic accuracy (97.1%) than handheld ultrasound (91.4%) for breast masses.…”

Section: Introductionmentioning

confidence: 97%

Comparative Study of Automated Breast 3-D Ultrasound and Handheld B-Mode Ultrasound for Differentiation of Benign and Malignant Breast Masses

Chen¹,

Chen²,

Diao³

et al. 2013

Ultrasound in Medicine & Biology

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Radiologists’ performance in the detection of benign and malignant masses with 3D automated breast ultrasound (ABUS)

Cited by 54 publications

References 21 publications

Comparison of automated breast ultrasonography to handheld ultrasonography in detecting and diagnosing breast lesions

Comparison of automated breast ultrasonography to handheld ultrasonography in detecting and diagnosing breast lesions

Computer-aided detection system for masses in automated whole breast ultrasonography: development and evaluation of the effectiveness

Comparative Study of Automated Breast 3-D Ultrasound and Handheld B-Mode Ultrasound for Differentiation of Benign and Malignant Breast Masses

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