Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection

Zelst, Jan van; Tan, Tao; Platel, Bram; Jong, Matthijn D.F. de; Steenbakkers, Arjan; Mourits, M.; Grivegnee, André-Robert; Borelli, Cristina; Karssemeijer, Nico; Mann, Ritse M.

doi:10.1016/j.ejrad.2017.01.021

Cited by 47 publications

(42 citation statements)

References 30 publications

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“…The ABUS system dedicated software "computed aided detection (CAD) software" improves the rates of detection of breast masses by radiologist as proved by van zelst et al [9]. They found better sensitivity for all readers by about 5.2-10.6% by CAD software [9].…”

Section: Discussionmentioning

confidence: 96%

Automated breast ultrasound system (ABUS): can it replace mammography as a screening tool?

Elkhalek

Bassiouny

Hamid

2019

Egypt J Radiol Nucl Med

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Background: Mammography is the most accepted, accurate, and effective modality in screening of breast cancer, yet its sensitivity is affected by the density of the breast tissue. Alternative methods for screening are the sonography and MRI but both had their limitations. A new option named ABUS (automated breast ultrasound system) is now proposed to overcome the breast US limitation as it is time-consuming and operator-dependent and to overcome the costly time-consuming MRI. The objectives of the study are to evaluate the accuracy of ABUS in the detection of different breast lesions as a substitution for mammography. This prospective study included 25 women outreached for digital mammography or handheld ultrasound examination at the period between January 2017 and February 2018. Women have no specific age group. Results: Significant improvement in the detection of breast lesions by ABUS use with mammogram especially in dense breasts (ACR class C and D) Conclusion: ABUS is a promising competitor to mammogram in screening of breast lesions

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Section: Discussionmentioning

confidence: 96%

Automated breast ultrasound system (ABUS): can it replace mammography as a screening tool?

Elkhalek

Bassiouny

Hamid

2019

Egypt J Radiol Nucl Med

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“…The efficiency and effectiveness of these systems shall be investigated to answer the question that whether these systems can improve the performance of radiologists and meanwhile reduce the reading time which is essential for screening (Rella 2018). For example, van Zelst et al (2017) showed that with an existing CADe system, the average area-under-the-curve (AUC) of the readers was significantly improved. Later in another reader study (van Zelst et al 2018), it was found that CAD software for ABUS can decrease the screening time for dense breasts.…”

Section: Discussionmentioning

confidence: 99%

Computer aided detection in automated 3-D breast ultrasound images: a survey

et al. 2019

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Nowadays, breast cancer is the leading cause of cancer death for women all over the world. Since the reason of breast cancer is unknown, early detection of the disease plays an important role in cancer control, saving lives and reducing costs. Among different modalities, automated 3-D breast ultrasound (3-D ABUS) is a new and effective imaging modality which has attracted a lot of interest as an adjunct to mammography for women with dense breasts. However, reading ABUS images is time consuming for radiologists and subtle abnormalities may be overlooked. Hence, computer aided detection (CADe) systems can be utilized as a second interpreter to assist radiologists to increase their screening speed and sensitivity. In this paper, a general architecture representing different CADe systems for ABUS images is introduced and the approaches for implementation of each block are categorized and reviewed. In addition, the limitations of these systems are discussed and their performance in terms of sensitivity and number of false positives per volume are compared.

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“…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). Recently developed CAD software for ABUS may improve screening efficiency, aid radiologists in detecting subtle cancers, and might improve specificity (19,20,23).…”

Section: Discussionmentioning

confidence: 99%

“…The conventional implementation of CAD is by marking regions suspicious for cancer in an image and such software has also been developed for ABUS (8,(16)(17)(18). CAD may help to improve sensitivity, specificity, and/or efficiency of radiologists reading ABUS when implemented as a conventional aid (19,20).…”

Section: Introductionmentioning

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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Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection

Cited by 47 publications

References 30 publications

Automated breast ultrasound system (ABUS): can it replace mammography as a screening tool?

Automated breast ultrasound system (ABUS): can it replace mammography as a screening tool?

Computer aided detection in automated 3-D breast ultrasound images: a survey

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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