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

2017 ◽  
Vol 89 ◽  
pp. 54-59 ◽  
Author(s):  
J.C.M. van Zelst ◽  
T. Tan ◽  
B. Platel ◽  
M. de Jong ◽  
A. Steenbakkers ◽  
...  
Author(s):  
Iris Allajbeu ◽  
Sarah E Hickman ◽  
Nicholas Payne ◽  
Penelope Moyle ◽  
Kathryn Taylor ◽  
...  

Abstract Purpose of Review Automated breast ultrasound (ABUS) is a three-dimensional imaging technique, used as a supplemental screening tool in women with dense breasts. This review considers the technical aspects, pitfalls, and the use of ABUS in screening and clinical practice, together with new developments and future perspectives. Recent Findings ABUS has been approved in the USA and Europe as a screening tool for asymptomatic women with dense breasts in addition to mammography. Supplemental US screening has high sensitivity for cancer detection, especially early-stage invasive cancers, and reduces the frequency of interval cancers. ABUS has similar diagnostic performance to handheld ultrasound (HHUS) and is designed to overcome the drawbacks of operator dependence and poor reproducibility. Concerns with ABUS, like HHUS, include relatively high recall rates and lengthy reading time when compared to mammography. ABUS is a new technique with unique features; therefore, adequate training is required to improve detection and reduce false positives. Computer-aided detection may reduce reading times and improve cancer detection. Other potential applications of ABUS include local staging, treatment response evaluation, breast density assessment, and integration of radiomics. Summary ABUS provides an efficient, reproducible, and comprehensive supplemental imaging technique in breast screening. Developments with computer-aided detection may improve the sensitivity and specificity as well as radiologist confidence and reduce reading times, making this modality acceptable in large volume screening centers.


2010 ◽  
Vol 37 (5) ◽  
pp. 2063-2073 ◽  
Author(s):  
Ruey-Feng Chang ◽  
Kuang-Che Chang-Chien ◽  
Etsuo Takada ◽  
Chiun-Sheng Huang ◽  
Yi-Hong Chou ◽  
...  

2019 ◽  
Vol 61 (3) ◽  
pp. 312-320 ◽  
Author(s):  
Jan CM van Zelst ◽  
Tao Tan ◽  
Ritse M Mann ◽  
Nico Karssemeijer

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.


2019 ◽  
Vol 53 (3) ◽  
pp. 1919-1941 ◽  
Author(s):  
Ehsan Kozegar ◽  
Mohsen Soryani ◽  
Hamid Behnam ◽  
Masoumeh Salamati ◽  
Tao Tan

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