Impact of Computer-Aided Detection Systems on Radiologist Accuracy With Digital Mammography

Cole, Elodia B.; Zhang, Zheng; Marques, Helga S.; Hendrick, R. Edward; Yaffe, Martin J.; Pisano, Etta D.

doi:10.2214/ajr.12.10187

Cited by 63 publications

(36 citation statements)

References 22 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A meta-analysis in 2008 of 10 studies of CAD applied to screening mammography concluded that CAD significantly increased recall rate with no significant improvement in cancer detection rates compared to readings without CAD (37). The largest recent reader study of digital mammography obtained during the Digital Mammography Imaging Screening Trial (DMIST) found no impact of CAD on radiologist interpretations of mammograms (5). In that report, the authors concluded that radiologists overall were not influenced by CAD markings and CAD had no impact, either beneficial or detrimental, on mammography interpretations.…”

Section: Discussionmentioning

confidence: 99%

“…Data were pooled from five mammography registries that participate in the Breast Cancer Surveillance Consortium (BCSC) (20) funded by the National Cancer Institute: (1) San Francisco Mammography Registry; (2) New Mexico Mammography Advocacy Project; (3) Vermont Breast Cancer Surveillance System; (4) New Hampshire Mammography Network; and (5) Carolina Mammography Registry. Each mammography registry links women to a state tumor registry or regional Surveillance Epidemiology and End Results program that collects population-based cancer data.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection

et al. 2015

View full text Add to dashboard Cite

for the Breast Cancer Surveillance Consortium IMPORTANCE After the US Food and Drug Administration (FDA) approved computer-aided detection (CAD) for mammography in 1998, and the Centers for Medicare and Medicaid Services (CMS) provided increased payment in 2002, CAD technology disseminated rapidly. Despite sparse evidence that CAD improves accuracy of mammographic interpretations and costs over $400 million a year, CAD is currently used for most screening mammograms in the United States. OBJECTIVE To measure performance of digital screening mammography with and without CAD in US community practice. DESIGN, SETTING, AND PARTICIPANTS We compared the accuracy of digital screening mammography interpreted with (n = 495 818) vs without (n = 129 807) CAD from 2003 through 2009 in 323 973 women. Mammograms were interpreted by 271 radiologists from 66 facilities in the Breast Cancer Surveillance Consortium. Linkage with tumor registries identified 3159 breast cancers in 323 973 women within 1 year of the screening.MAIN OUTCOMES AND MEASURES Mammography performance (sensitivity, specificity, and screen-detected and interval cancers per 1000 women) was modeled using logistic regression with radiologist-specific random effects to account for correlation among examinations interpreted by the same radiologist, adjusting for patient age, race/ethnicity, time since prior mammogram, examination year, and registry. Conditional logistic regression was used to compare performance among 107 radiologists who interpreted mammograms both with and without CAD.RESULTS Screening performance was not improved with CAD on any metric assessed. Mammography sensitivity was 85.3% (95% CI, 83.6%-86.9%) with and 87.3% (95% CI, 84.5%-89.7%) without CAD. Specificity was 91.6% (95% CI, 91.0%-92.2%) with and 91.4% (95% CI, 90.6%-92.0%) without CAD. There was no difference in cancer detection rate (4.1 in 1000 women screened with and without CAD). Computer-aided detection did not improve intraradiologist performance. Sensitivity was significantly decreased for mammograms interpreted with vs without CAD in the subset of radiologists who interpreted both with and without CAD (odds ratio, 0.53; 95% CI, 0.29-0.97). CONCLUSIONS AND RELEVANCEComputer-aided detection does not improve diagnostic accuracy of mammography. These results suggest that insurers pay more for CAD with no established benefit to women.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The normal goal of a CAD system is to direct the radiologist's attention to specific, suspicious locations. Although these systems perform at a level comparable to that of an expert radiologist, they have not been hugely successful in clinical practice (35), in part because the positive predictive value of any given CAD mark is very low in a mammography screening situation where the prevalence of disease is low. As a result, radiologists tend to dismiss the correct CAD marks when they occur (36).…”

Section: Discussionmentioning

confidence: 99%

A half-second glimpse often lets radiologists identify breast cancer cases even when viewing the mammogram of the opposite breast

Evans

Haygood

Cooper

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

137

View full text Add to dashboard Cite

Humans are very adept at extracting the "gist" of a scene in a fraction of a second. We have found that radiologists can discriminate normal from abnormal mammograms at above-chance levels after a half-second viewing (d′ ∼ 1) but are at chance in localizing the abnormality. This pattern of results suggests that they are detecting a global signal of abnormality. What are the stimulus properties that might support this ability? We investigated the nature of the gist signal in four experiments by asking radiologists to make detection and localization responses about briefly presented mammograms in which the spatial frequency, symmetry, and/or size of the images was manipulated. We show that the signal is stronger in the higher spatial frequencies. Performance does not depend on detection of breaks in the normal symmetry of left and right breasts. Moreover, above-chance classification is possible using images from the normal breast of a patient with overt signs of cancer only in the other breast. Some signal is present in the portions of the parenchyma (breast tissue) that do not contain a lesion or that are in the contralateral breast. This signal does not appear to be a simple assessment of breast density but rather the detection of the abnormal gist may be based on a widely distributed image statistic, learned by experts. The finding that a global signal, related to disease, can be detected in parenchyma that does not contain a lesion has implications for improving breast cancer detection.gist processing | medical image perception | attention | mammography R apid extraction of scene "gist" (1-4) is a very useful aspect of routine visual perception that allows us to allocate our time and attention intelligently when confronted with new visual information (Can I find food here? Is there danger here?). The signals that we extract on our first glimpse of a scene are imperfect but not random. Experts often anecdotally report gistlike experiences with complex images in their domain of expertise. For instance, we have shown that radiologists can distinguish normal from abnormal mammograms at above-chance levels in as little as a quarter of a second, whereas nonexperts cannot (5). The gist of abnormality appears to be a global signal. Radiologists can detect it but cannot even crudely localize the abnormality under these conditions.Detecting the gist of breast cancer might be more than a curiosity, if that signal could be used to improve performance in breast cancer screening. Screening mammography can reduce mortality through early diagnosis of disease (6). Breast cancer is the most prevalent cancer in women and is the second leading cause of cancer deaths in women (7). In North America, screening mammography has a false negative rate of 20 to 30% (8, 9) and a recall rate of about 10% (10). With a disease prevalence of about 0.3% (11), the vast majority of those recalled will not have cancer. Thus, there is significant room for improvement.It has been argued for many years that an initial, global processing step is an i...

show abstract

“…115 In a recent retrospective study, 300 examinations from DMIST 10 were re-read by 15 radiologists using two CAD systems. 116 No significant difference in sensitivity and specificity was found for the group as a whole. 116 CAD is currently being investigated for DBT and may potentially reduce reading times.…”

Section: Breast Density Assessmentmentioning

confidence: 87%

“…116 No significant difference in sensitivity and specificity was found for the group as a whole. 116 CAD is currently being investigated for DBT and may potentially reduce reading times. 117 …”

Section: Breast Density Assessmentmentioning

confidence: 87%

A comparison of digital mammography detectors and emerging technology

Diffey

2015

Radiography

View full text Add to dashboard Cite

Impact of Computer-Aided Detection Systems on Radiologist Accuracy With Digital Mammography

Cited by 63 publications

References 22 publications

Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection

Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection

A half-second glimpse often lets radiologists identify breast cancer cases even when viewing the mammogram of the opposite breast

A comparison of digital mammography detectors and emerging technology

Contact Info

Product

Resources

About