Breast US Computer-aided Diagnosis Workstation: Performance with a Large Clinical Diagnostic Population

Drukker, Karen; Gruszauskas, Nicholas P.; Sennett, Charlene A.; Giger, Maryellen L.

doi:10.1148/radiol.2482071778

Cited by 52 publications

(41 citation statements)

References 16 publications

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“…These results are consistent with those found in the literature, in which the sensitivity of ultrasound CAD systems is reported to be high (between 88.9% and 100%) (11,13,14,16-21,23,25). A sensitivity rate of 100% has been reported, particularly in two studies (23,25). In these studies, the negative predictive value of CAD systems was also high, at 91.7% and 99%.…”

Section: Discussionmentioning

confidence: 92%

See 1 more Smart Citation

Evaluation of the Accuracy of a Computer-aided Diagnosis (CAD) System in Breast Ultrasound according to the Radiologist’s Experience

et al. 2012

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Section: Discussionmentioning

confidence: 92%

“…In addition, to our knowledge, these studies were conducted using noncommercial CAD systems, independently developed by research teams. However, the performance of CAD does not necessarily anticipate its influence on radiologists when used in clinical practice (23).…”

mentioning

confidence: 99%

Evaluation of the Accuracy of a Computer-aided Diagnosis (CAD) System in Breast Ultrasound according to the Radiologist’s Experience

et al. 2012

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“…CAD is being explored as a way of improving the specifi city of breast lesion classifi cation without sacrifi cing sensitivity (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37). A subset of CAD systems predicts biopsy outcomes with use of each radiologist's description of breast lesions instead of automated, computerextracted features (26)(27)(28).…”

Section: Methodsmentioning

confidence: 99%

Computer-aided Classification of Breast Masses: Performance and Interobserver Variability of Expert Radiologists versus Residents

Singh¹,

Maxwell²,

Baker³

et al. 2011

Radiology

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Purpose:To evaluate the interobserver variability in descriptions of breast masses by dedicated breast imagers and radiology residents and determine how any differences in lesion description affect the performance of a computer-aided diagnosis (CAD) computer classifi cation system. Materials and Methods:Institutional review board approval was obtained for this HIPAA-compliant study, and the requirement to obtain informed consent was waived. Images of 50 breast lesions were individually interpreted by seven dedicated breast imagers and 10 radiology residents, yielding 850 lesion interpretations. Lesions were described with use of 11 descriptors from the Breast Imaging Reporting and Data System, and interobserver variability was calculated with the Cohen k statistic. Those 11 features were selected, along with patient age, and merged together by a linear discriminant analysis (LDA) classifi cation model trained by using 1005 previously existing cases. Variability in the recommendations of the computer model for different observers was also calculated with the Cohen k statistic. Results:A signifi cant difference was observed for six lesion features, and radiology residents had greater interobserver variability in their selection of fi ve of the six features than did dedicated breast imagers. The LDA model accurately classifi ed lesions for both sets of observers (area under the receiver operating characteristic curve = 0.94 for residents and 0.96 for dedicated imagers). Sensitivity was maintained at 100% for residents and improved from 98% to 100% for dedicated breast imagers. For residents, the computer model could potentially improve the specifi city from 20% to 40% ( P , .01) and the k value from 0.09 to 0.53 ( P , .001). For dedicated breast imagers, the computer model could increase the specifi city from 34% to 43% ( P = .16) and the k value from 0.21 to 0.61 ( P , .001). Conclusion:Among fi ndings showing a signifi cant difference, there was greater interobserver variability in lesion descriptions among residents; however, an LDA model using data from either dedicated breast imagers or residents yielded a consistently high performance in the differentiation of benign from malignant breast lesions, demonstrating potential for improving specifi city and decreasing interobserver variability in biopsy recommendations.q RSNA, 2010 1

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“…To date, research on CAD in US-based diagnosis has been carried out mostly on breast lesions [14][15][16][17][18][19] and has been limited in the fields of gastroenterology and hepatology. In this article, we introduce CAD aimed at differential diagnosis of FLLs by use of contrast-enhanced US (CEUS), in addition to reviewing CAD on US in liver research.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis for contrast-enhanced ultrasound in the liver

Sugimoto

Shiraishi²,

Moriyasu³

et al. 2010

WJR

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Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. The basic concept of CAD is to provide computer output as a second opinion to assist radiologists' image interpretations by improving the accuracy and consistency of radiologic diagnosis and also by reducing the image-reading time. To date, research on CAD in ultrasound (US)-based diagnosis has been carried out mostly for breast lesions and has been limited in the fields of gastroenterology and hepatology, with most studies being conducted using B-mode US images. Two CAD schemes with contrast-enhanced US (CEUS) that are used in classifying focal liver lesions (FLLs) as liver metastasis, hemangioma, or three histologically differentiated types of hepatocellular carcinoma (HCC) are introduced in this article: one is based on physicians' subjective pattern classifications (subjective analysis) and the other is a computerized scheme for classification of FLLs (quantitative analysis). Classification accuracies for FLLs for each CAD scheme were 84.8% and 88.5% for metastasis, 93.3% and 93.8% for hemangioma, and 98.6% and 86.9% for all HCCs, respectively. In addition, the classification accuracies for histologic differentiation of HCCs were 65.2% and 79.2% for well-differentiated HCCs, 41.7% and 50.0% for moderately differentiated HCCs, and 80.0% and 77.8% for poorly differentiated HCCs, respectively. There are a number of issues concerning the clinical application of CAD for CEUS, however, it is likely that CAD for CEUS of the liver will make great progress in the future.

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Breast US Computer-aided Diagnosis Workstation: Performance with a Large Clinical Diagnostic Population

Cited by 52 publications

References 16 publications

Evaluation of the Accuracy of a Computer-aided Diagnosis (CAD) System in Breast Ultrasound according to the Radiologist’s Experience

Evaluation of the Accuracy of a Computer-aided Diagnosis (CAD) System in Breast Ultrasound according to the Radiologist’s Experience

Computer-aided Classification of Breast Masses: Performance and Interobserver Variability of Expert Radiologists versus Residents

Computer-aided diagnosis for contrast-enhanced ultrasound in the liver

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