The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection
Open accessAbstract
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 RELEVANCE: Computer-aided detection does not improve diagnostic accuracy of mammography. These results suggest that insurers pay more for CAD with no established benefit to women.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Rejection bias refers to the conscious or unconscious desensitization to excessive alerts. Alert fatigue is a manifestation of this bias, as clinicians may ignore important alerts owing to an overwhelming number of false alarms [81, 82].”
From Fairness of artificial intelligence in healthcare: review and recommendations (2024) · cites this paper as [81]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Statistical Evaluation of Medical Tests for Classification and Prediction
PEPE, M. S. (2003) · doi:10.1093/oso/9780198509844.001.0001 · Cited by 2,206
Breast imaging reporting and data system (BI-RADS)
Liberman, L., & Menell, J. H. (2002) · Radiologic Clinics of North America · doi:10.1016/s0033-8389(01)00017-3 · Cited by 2,159
The Statistical Evaluation of Medical Tests for Classification and Prediction
PEPE, M. S. (2005) · Technometrics · doi:10.1198/tech.2005.s278 · Cited by 1,907
A hierarchical regression approach to meta‐analysis of diagnostic test accuracy evaluations
Rutter, C. M., & Gatsonis, C. (2001) · Statistics in Medicine · doi:10.1002/sim.942 · Cited by 1,510
Breast Imaging Reporting and Data System
Berg, W. A., Campassi, C., LANGENBERG, P. W., & Sexton, M. J. (2000) · American Journal of Roentgenology · doi:10.2214/ajr.174.6.1741769 · Cited by 1,268
Freer, T. W., & Ulissey, M. J. (2001) · Radiology · doi:10.1148/radiol.2203001282 · Cited by 748
Influence of Computer-Aided Detection on Performance of Screening Mammography
Fenton, J. J., Taplin, S. H., Carney, P. A., Abraham, L., Sickles, E. A., D’Orsi, C. J., Berns, E. A., Cutter, G., Hendrick, R. E., Barlow, W. E., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa066099 · Cited by 619
Improving breast cancer diagnosis with computer-aided diagnosis
Jiang, Y., Nishikawa, R. M., Schmidt, R. A., Metz, C. E., Giger, M. L., & Doi, K. (1999) · Academic Radiology · doi:10.1016/s1076-6332(99)80058-0 · Cited by 310
Gur, D., Sumkin, J. H., Rockette, H. E., Ganott, M. A., Hakim, C. M., Hardesty, L. A., Poller, W. R., Shah, R., & Wallace, L. P. (2004) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djh067 · Cited by 306
Single Reading with Computer-Aided Detection for Screening Mammography
Gilbert, F. J., Astley, S., Gillan, M. G., Agbaje, O. F., Wallis, M., James, J. J., Boggis, C. R. M., & Duffy, S. W. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0803545 · Cited by 296
Flagged first, then by citations
Cited by
Sorted by citations
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803
International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287
Key challenges for delivering clinical impact with artificial intelligence
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817
Deep Learning in Medical Imaging: General Overview
Lee, J., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017) · Korean Journal of Radiology · doi:10.3348/kjr.2017.18.4.570 · Cited by 1,328
Metadata
- Authors
- Lehman, C. D., Wellman, R. D., Buist, D. S. M., Kerlikowske, K., Tosteson, A. N. A., & Miglioretti, D. L.
- Affiliations
- Massachusetts General Hospital (US)
- Kaiser Permanente Washington Health Research Institute (US)
- University of California, San Francisco (US)
- Dartmouth College (US)
- Corresponding author
- Lehman, Constance D.
- ORCID
- Venue
- JAMA Internal Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2015-11-01
- Volume
- 175
- Issue
- 11
- Pages
- 1828
- ISSN
- 2168-6106 (ISSN-L)
- PMC
- PMC4836172
- OpenAlex
- W1917647701
- Cited by
- 678
- References
- 44
- Citations by year
- 2026: 38 · 2025: 76 · 2024: 81 · 2023: 92 · 2022: 95 · 2021: 86 · 2020: 72 · 2019: 59 · 2018: 35 · 2017: 27 · 2016: 14 · 2015: 1
- FWCI
- 20.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Cancer Institute (HHSN261201100031C, U54CA163303, P30CA023108, P01CA154292)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Digital Radiography and Breast Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
- Keywords
- computer-aided detection, digital mammography, breast cancer screening, diagnostic accuracy, mammography sensitivity, screen-detected breast cancer, interval breast cancer, Breast Cancer Surveillance Consortium, community practice, radiologist interpretation, breast cancer detection rate, mammography quality assurance
- MeSH terms
- Adult, Aged, Aged, 80 and over, Breast Neoplasms, Female, Humans, Mammography, Mass Screening, Middle Aged, Radiographic Image Interpretation, Computer-Assisted, Registries, Sensitivity and Specificity, United States, Reproducibility of Results, Logistic Models, Early Detection of Cancer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
