Soft-Copy Mammographic Readings with Different Computer-assisted Detection Cuing Environments: Preliminary Findings

Ganott, Marie A.; Britton, Cynthia A.; Hakim, Christiane M.; Hardesty, Lara A.; Chang, Thomas S.; Rockette, Howard E.; Gur, David

doi:10.1148/radiol.2213010308

Cited by 90 publications

(68 citation statements)

References 32 publications

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“…An unexpected finding is the association between use of CAD and degraded sensitivity of readers for comparatively difficult cases; particularly striking because this affected the highly discriminating readers. This finding is, however, consistent with our aforementioned empirical study (16,17) and the study by Zheng et al (18), which strongly suggested that readers using CAD were biased by incorrect computer outputs.…”

Section: Discussionsupporting

confidence: 93%

“…We also conducted two supplementary studies (16,17) with a set of cases selected to have large proportions of cancers incorrectly prompted by CAD. In these studies, radiologists proved significantly less sensitive with CAD than without, suggesting that incorrect CAD prompts may hinder readers under some circumstances, which is consistent with the findings of Zheng et al (18). Our further analyses of data from (6) suggested that CAD reduced radiologists' sensitivity in decisions about the more difficult cases -most of which were incorrectly prompted by the CAD tool -and increased sensitivity for comparatively easy cases (16).…”

Section: Cancer Screening and Previous Studies Of Cadsupporting

confidence: 88%

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How to Discriminate between Computer-Aided and Computer-Hindered Decisions

et al. 2013

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Background. Computer aids can affect decisions in complex ways, potentially even making them worse; common assessment methods may miss these effects. We developed a method for estimating the quality of decisions, as well as how computer aids affect it, and applied it to computer-aided detection (CAD) of cancer, reanalyzing data from a published study where 50 professionals (“readers”) interpreted 180 mammograms, both with and without computer support. Method. We used stepwise regression to estimate how CAD affected the probability of a reader making a correct screening decision on a patient with cancer (sensitivity), thereby taking into account the effects of the difficulty of the cancer (proportion of readers who missed it) and the reader’s discriminating ability (Youden’s determinant). Using regression estimates, we obtained thresholds for classifying a posteriori the cases (by difficulty) and the readers (by discriminating ability). Results. Use of CAD was associated with a 0.016 increase in sensitivity (95% confidence interval [CI], 0.003–0.028) for the 44 least discriminating radiologists for 45 relatively easy, mostly CAD-detected cancers. However, for the 6 most discriminating radiologists, with CAD, sensitivity decreased by 0.145 (95% CI, 0.034–0.257) for the 15 relatively difficult cancers. Conclusions. Our exploratory analysis method reveals unexpected effects. It indicates that, despite the original study detecting no significant average effect, CAD helped the less discriminating readers but hindered the more discriminating readers. Such differential effects, although subtle, may be clinically significant and important for improving both computer algorithms and protocols for their use. They should be assessed when evaluating CAD and similar warning systems.

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Section: Discussionsupporting

confidence: 93%

Section: Cancer Screening and Previous Studies Of Cadsupporting

confidence: 88%

How to Discriminate between Computer-Aided and Computer-Hindered Decisions

et al. 2013

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“…Zheng et al [31] have shown the impact of CAD sensitivity and false positive rate on radiologist performance with the four combinations of a CAD system with sensitivities of 90% or 50% and false positive rates of two or eight marks per four-image mammogram case (multiplying their per image false positive rates by 4). Their results showed that a CAD system with 90% sensitivity and two false positives per case improved radiologist performance, a CAD system with 90% sensitivity and eight false positives per case or a CAD system with 50% sensitivity and two false positives per case had no significant impact on radiologist performance, while a CAD system with 50% sensitivity and eight false positives per case was detrimental to radiologist performance.…”

Section: Discussionmentioning

confidence: 99%

False Positive Marks on Unsuspicious Screening Mammography with Computer-Aided Detection

Mahoney

Meganathan

2011

J Digit Imaging

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The contribution of computer-aided detection (CAD) systems as an interpretive aid in screening mammography can be hampered by a high rate of false positive detections. Specificity, false positive rate, and ease of dismissing false positive marks from two CAD systems are retrospectively evaluated. One hundred screening mammographic studies with a BI-RADS assessment code of 1 or 2 and at least 2-year normal mammographic followup were retrospectively reviewed using two CAD systems. Breast density, CAD marks, and radiologist"s ease of dismissing false positive marks were recorded. Specificities from the two CAD versions considering all marks were 23% and 15% (p value=0.07); mass marks, 35% and 17% (p value<0.01); and calcification marks 62% and 75% (p value = 0.01). The two CAD versions did not differ regarding mean and median marks per case for all marks (2.3, 2.0 and 2.3, 2.0, p value=0.65) or mass marks (1.6, 1.0 and 1.8, 2.0, p value=0.15), but differed for calcification marks (0.8, 0 and 0.5, 0, p value<0.01). Slightly higher specificity and fewer marks per case observed in dense breasts did not reach statistical significance. The reviewing radiologist classified most marks from both CAD systems (84% and 88%) as very easy/easy to dismiss. The two CAD versions had small differences in specificity and false positive marks. Differences, although not statistically significant, in specificities and false positive rates between dense and non-dense breasts warrant further research. Most false positive marks are easily dismissed and should not affect clinical performance.

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“…This can be explained by the difference in performance. "Highly performing" CAD was shown to improve radiologists' performance, while "poor performing" CAD negatively affected their performance [6]. This shows how important designing a highly performing CAD system is.…”

Section: Introductionmentioning

confidence: 95%

Abnormalities Detection in Digital Mammography Using Template Matching

Farrag¹

2016

ijacsa

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Abstract-Breast cancer affects 1 in 8 women in the United States. Early detection and diagnosis is key to recovery. ComputerAided Detection (CAD) of breast cancer helps decrease morbidity and mortality rates. In this study we apply Template Matching as a method for breast cancer detection to a novel data set comprised of mammograms annotated according to ground truth. Performance is evaluated in terms of Area Under the Receiver Operator Characteristic Curve (Area Under ROC) and Freeresponse ROC.

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Soft-Copy Mammographic Readings with Different Computer-assisted Detection Cuing Environments: Preliminary Findings

Abstract: CAD systems have the potential to significantly improve diagnostic performance in mammography. However, poorly performing schemes could adversely affect observer performance in both cued and noncued areas.

Cited by 90 publications

References 32 publications

How to Discriminate between Computer-Aided and Computer-Hindered Decisions

How to Discriminate between Computer-Aided and Computer-Hindered Decisions

False Positive Marks on Unsuspicious Screening Mammography with Computer-Aided Detection

Abnormalities Detection in Digital Mammography Using Template Matching

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