Computer aids and human second reading as interventions in screening mammography: Two systematic reviews to compare effects on cancer detection and recall rate

Taylor, Paul; Potts, Henry W W

doi:10.1016/j.ejca.2008.02.016

Cited by 137 publications

(100 citation statements)

References 50 publications

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“…Other studies have suggested limited effectiveness of CAD in mammography (31,32), however the finding that CAD can systematically improve or worsen readers' performance depending on their skill and the difficulty of cases is a novel finding.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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

et al. 2013

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“…This work has been described in detail elsewhere. 21 Ten studies were found comparing single reading with single reading with CAD, and 17 comparing single reading with double reading. Double reading with arbitration (films where the two initial readers disagree are reviewed by a third) shows a significant increase in detection rate (odds ratio: 1.08, 95% CI 1.02 -1.15) and a significant decrease in recall rate (odds ratio: 0.94, 95% CI 0.92 -0.96).…”

Section: Discussionmentioning

confidence: 99%

Modelling the Impact of Changes in Sensitivity on the Outcomes of the Uk Breast Screening Programme

Taylor

2010

J Med Screen

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Objectives To assess the impact on breast cancer mortality of improving the sensitivity of breast screening programmes. Methods A Markov model was populated with data obtained from published statistics describing the UK National Health Service Breast Screening Programme and the incidence and mortality of breast cancer in the UK. The model was used to study the impact of changes to the sensitivity of screening. The effects on cancer detection rates and breast cancers and total mortality was studied for a cohort of women followed from age 45 to age 89. Results Running the model from age 45 to 89, with sensitivity set at the baseline value of 75%, predicts the detection at screening of 44 cancers per thousand of population and the detection outside screening of 82 cancers per thousand of population. Running the model with values of sensitivity from 75% to 95% shows the proportion of cancers detected at screening increasing as screening improves, and deaths from breast cancer falling. The drop in breast cancer deaths is however modest. Increasing sensitivity from 75% to 85% reduces the number of breast cancer deaths from 28 to 27 per thousand. Conclusions Likely achievable improvements in the sensitivity of screening do not have a marked effect on breast cancer mortality.

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“…Many studies have validated CAD techniques in screening populations and found a sensitivity ranging from 73 [2] to 96% [3]. Moreover, the impact of a CAD system on the performance of mammogram observers was evaluated in several studies [2,[4][5][6][7][8][9][10][11][12][13][14], showing that there is insufficient evidence to claim that CAD improves cancer detection rates but that it does increase recall rates in screening programs for breast cancer [15].…”

Section: Introductionmentioning

confidence: 99%

Computer-aided detection in full-field digital mammography in a clinical population: performance of radiologist and technologists

Biggelaar

Kessels

Engelshoven

et al. 2009

Breast Cancer Res Treat

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The purpose of the study was to evaluate the impact of a computer-aided detection (CAD) system on the performance of mammogram readers in interpreting digital mammograms in a clinical population. Furthermore, the ability of a CAD system to detect breast cancer in digital mammography was studied in comparison to the performance of radiologists and technologists as mammogram readers. Digital mammograms of 1,048 consecutive patients were evaluated by a radiologist and three technologists. Abnormalities were recorded and an imaging conclusion was given as a BI-RADS score before and after CAD analysis. Pathology results during 12 months follow up were used as a reference standard for breast cancer. Fifty-one malignancies were found in 50 patients. Sensitivity and specificity were computed before and after CAD analysis and provided with 95% CIs. In order to assess the detection rate of malignancies by CAD and the observers, the pathological locations of these 51 breast cancers were matched with the locations of the CAD marks and the mammographic locations that were considered to be suspicious by the observers. For all observers, the sensitivity rates did not change after application of CAD. A mean sensitivity of 92% was found for all technologists and 84% for the radiologist. For two technologists, the specificity decreased (from 84 to 83% and from 77 to 75%). For the radiologist and one technologist, the application of CAD did not have any impact on the specificity rates (95 and 83%, respectively). CAD detected 78% of all malignancies. Five malignancies were indicated by CAD without being noticed as suspicious by the observers. In conclusion, the results show that systematic application of CAD in a clinical patient population failed to improve the overall sensitivity of mammogram interpretation by the readers and was associated with an increase in false-positive results. However, CAD marked five malignancies that were missed by the different readers.

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Computer aids and human second reading as interventions in screening mammography: Two systematic reviews to compare effects on cancer detection and recall rate

Abstract: Background

Cited by 137 publications

References 50 publications

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

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

Modelling the Impact of Changes in Sensitivity on the Outcomes of the Uk Breast Screening Programme

Computer-aided detection in full-field digital mammography in a clinical population: performance of radiologist and technologists

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