Evaluation of the Effect of Computer-Aided Classification of Benign and Malignant Lesions on Reader Performance in Automated Three-dimensional Breast Ultrasound

Tan, Tao; Platel, Bram; Twellmann, Thorsten; Schie, Guido van; Mus, Roel; Grivegnee, André-Robert; Mann, Ritse M.; Karssemeijer, Nico

doi:10.1016/j.acra.2013.07.013

Cited by 27 publications

(19 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…US BI-RADS has substantially contributed to improving communication between physicians and radiologists, but observer variability remains a major limitation of US, which is 97.9 C4A = category 4A, low suspicion for malignancy, C4B = category 4B, moderate suspicious for malignancy, CAD = computer-aided diagnosis, NPV = negative predictive value, PPV = positive predictive value (4,6,9,11,13,15,16). In this study, we evaluated the di- (4,9,11,17).…”

Section: Discussionmentioning

confidence: 99%

Breast Ultrasound Computer-Aided Diagnosis: Analysis of Types of Errors

Jeong

Kang

Kim

et al. 2018

J Korean Soc Radiol

View full text Add to dashboard Cite

Purpose: The aim of this study was to evaluate the diagnostic performance of breast ultrasound (US) computer-aided diagnosis (CAD) to distinguish between benign and malignant lesions and analyze features of lesions interpreted with errors retrospectively. Materials and Methods: Three hundred and sixteen women with 375 breast lesions were enrolled. We assessed the accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Additionally, we evaluated the causes and patterns of the misinterpretation in the false positive and negative groups. Results: The accuracy, sensitivity, specificity, PPV, and NPV of breast US-CAD were 80.3%, 83.3%, 79.8%, 37.7%, and 97.0%, respectively. There were 8 false negative lesions that were oval in shape and in parallel orientation. There were 66 false positive lesions. The greatest number of errors entailed inappropriate demarcation due to heterogeneous echogenicity, etc. The second exhibited suspicious features with good demarcation and description but were confirmed as benign histologically. The third entailed a benign lesion with suspicious features, such as abscesses. The smallest portion with good demarcations and descriptions indicating benign status exhibited possible malignancy as a final conclusion. Conclusion: Breast US-CAD is expected to be helpful in avoiding unnecessary biopsies due to its high NPV. Therefore, operators need to know the characteristics of lesions prone to misinterpretation. Index terms

show abstract

Section: Discussionmentioning

confidence: 99%

Breast Ultrasound Computer-Aided Diagnosis: Analysis of Types of Errors

Jeong

Kang

Kim

et al. 2018

J Korean Soc Radiol

View full text Add to dashboard Cite

show abstract

“…Some studies 12,38 have shown that using CAD can improve the radiologist performance of distinguishing malignant from benign breast lesions in 3-D US images. The observer study 20 showed that the previous system performs as good as the best reader.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Incorporating texture features in a computer-aided breast lesion diagnosis system for automated three-dimensional breast ultrasound

et al. 2014

Self Cite

View full text Add to dashboard Cite

Abstract. We investigated the benefits of incorporating texture features into an existing computer-aided diagnosis (CAD) system for classifying benign and malignant lesions in automated three-dimensional breast ultrasound images. The existing system takes into account 11 different features, describing different lesion properties; however, it does not include texture features. In this work, we expand the system by including texture features based on local binary patterns, gray level co-occurrence matrices, and Gabor filters computed from each lesion to be diagnosed. To deal with the resulting large number of features, we proposed a combination of feature-oriented classifiers combining each group of texture features into a single likelihood, resulting in three additional features used for the final classification. The classification was performed using support vector machine classifiers, and the evaluation was done with 10-fold cross validation on a dataset containing 424 lesions (239 benign and 185 malignant lesions). We compared the classification performance of the CAD system with and without texture features. The area under the receiver operating characteristic curve increased from 0.90 to 0.91 after adding texture features (p < 0.001).

show abstract

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Evaluation of the Effect of Computer-Aided Classification of Benign and Malignant Lesions on Reader Performance in Automated Three-dimensional Breast Ultrasound

Cited by 27 publications

References 19 publications

Breast Ultrasound Computer-Aided Diagnosis: Analysis of Types of Errors

Breast Ultrasound Computer-Aided Diagnosis: Analysis of Types of Errors

Incorporating texture features in a computer-aided breast lesion diagnosis system for automated three-dimensional breast ultrasound

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

Contact Info

Product

Resources

About