Assessing the impact of CAD on the sensitivity and specificity of film readers

Taylor, Paul; Given-Wilson, Rosalind; Champness, J; Potts, Henry W W; Johnston, Kathy

doi:10.1016/j.crad.2004.04.017

Cited by 19 publications

(22 citation statements)

References 10 publications

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“…CAD tools have been designed to help readers by identifying and marking ("prompting") regions of interest (ROI) on a digitised mammogram, to prevent clinicians from overlooking them. However, evidence about the benefits of CAD is inconclusive (5)(6)(7)(8)(9)(10)(11)(12)(13)(14). Extensive literature surveys can be found in (7), (8) and (9).…”

Section: Cancer Screening and Previous Studies Of Cadmentioning

confidence: 99%

“…E.g. Taylor et al (6,8,11) assessed the impact of CAD prompts on the sensitivity and specificity of mammogram readers with and without CAD. We argue that these methodological approaches can be useful, but are ultimately insufficient and even potentially misleading for assessing the impact of using CAD and similar tools.…”

Section: Cancer Screening and Previous Studies Of Cadmentioning

confidence: 99%

“…Taylor et al (6,8,11) evaluated the R2 ImageChecker M1000 in a retrospective study which assessed the impact of CAD prompts on the sensitivity and specificity of film readers (30 radiologists, 5 breast clinicians and 15 radiographers), who read 180 films, all of which had a proven outcome. The set included 60 cancers (20 false negative interval cancers -i.e., cancers that had not been detected during screening using these mammograms but which were classified as false negatives when the mammograms were later reviewed by a panel of experts -and 40 screen detected cancers, i.e., detected during screening ).…”

Section: Data Sourcementioning

confidence: 99%

“…The CAD tool we considered in all our analyses is the R2 ImageChecker M1000, as evaluated by Taylor et al (6,8,11).…”

Section: Cancer Screening and Previous Studies Of Cadmentioning

confidence: 99%

“…The case set was selected in such a way that it contained a mixture of different types of cancerous signs (masses, micro-calcifications, spiculated lesions and asymmetries) and a combination of easy and difficult cases. The selection criteria (6,8,11) ensure that the case set covers a variety of categories of cases, although the set is not necessarily a representative sample of the population addressed by screening. Each reader read each case twice, once with CAD ("prompted condition") and once without CAD ("unprompted condition").…”

Section: Data Sourcementioning

confidence: 99%

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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: Cancer Screening and Previous Studies Of Cadmentioning

confidence: 99%

Section: Cancer Screening and Previous Studies Of Cadmentioning

confidence: 99%

Section: Data Sourcementioning

confidence: 99%

“…The CAD tool we considered in all our analyses is the R2 ImageChecker M1000, as evaluated by Taylor et al (6,8,11).…”

Section: Cancer Screening and Previous Studies Of Cadmentioning

confidence: 99%

Section: Data Sourcementioning

confidence: 99%

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

et al. 2013

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Using computer-aided detection in mammography as a decision support

et al. 2010

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Objective:To evaluate an interactive computer-aided detection (CAD) system for reading mammograms to improve decision making.Methods:A dedicated mammographic workstation has been developed in which readers can probe image locations for the presence of CAD information. If present, CAD findings are displayed with the computed malignancy rating. A reader study was conducted in which four screening radiologists and five non-radiologists participated to study the effect of this system on detection performance. The participants read 120 cases of which 40 cases had a malignant mass that was missed at the original screening. The readers read each mammogram both with and without CAD in separate sessions. Each reader reported localized findings and assigned a malignancy score per finding. Mean sensitivity was computed in an interval of false-positive fractions less than 10%.Results:Mean sensitivity was 25.1% in the sessions without CAD and 34.8% in the CAD-assisted sessions. The increase in detection performance was significant (p = 0.012). Average reading time was 84.7 ± 61.5 s/case in the unaided sessions and was not significantly higher when interactive CAD was used (85.9 ± 57.8 s/case).Conclusion:Interactive use of CAD in mammography may be more effective than traditional CAD for improving mass detection without affecting reading time.

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Role of Computer-Aided Detection in Very Small Screening Detected Invasive Breast Cancers

et al. 2012

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This study aims to assess computer-aided detection (CAD) performance with full-field digital mammography (FFDM) in very small (equal to or less than 1 cm) invasive breast cancers. Sixty-eight invasive breast cancers less than or equal to 1 cm were retrospectively studied. All cases were detected with FFDM in women aged 49-69 years from our breast cancer screening program. Radiological characteristics of lesions following BI-RADS descriptors were recorded and compared with CAD sensitivity. Age, size, BI-RADS classification, breast density type, histological type of the neoplasm, and role of the CAD were also assessed. Per-study specificity and mass false-positive rate were determined by using 100 normal consecutive studies. Thirty-seven (54.4 %) masses, 17 (25 %) calcifications, 6 (8.8 %) masses with calcifications, 7 (10.3 %) architectural distortions, and 1 asymmetry (1.5 %) were found. CAD showed an overall sensitivity of 86.7 % (masses, 86.5 %; calcifications, 100 %; masses with calcifications, 100 %; and architectural distortion, 57.14 %), CAD failed to detect 9 out of 68 cases: 5 of 37 masses, 3 of 7 architectural distortions, and 1 of 1 asymmetry. Fifteen out of 37 masses were hyperdense, and all of them were detected by CAD. No association was seen among mass morphology or margins and detectability. Per-study specificity and CAD false-positive rate was 26 % and 1.76 false marks per study. In conclusion, CAD shows a high sensitivity and a low specificity. Lesion size, histology, and breast density do not influence sensitivity. Mammographic features, mass density, and thickness of the spicules in architectural distortions do influence.

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Assessing the impact of CAD on the sensitivity and specificity of film readers

Cited by 19 publications

References 10 publications

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

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

Using computer-aided detection in mammography as a decision support

Role of Computer-Aided Detection in Very Small Screening Detected Invasive Breast Cancers

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