The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium
Open accessAbstract
Purpose To establish performance benchmarks for modern screening digital mammography and assess performance trends over time in U.S. community practice. Materials and Methods This HIPAA-compliant, institutional review board–approved study measured the performance of digital screening mammography interpreted by 359 radiologists across 95 facilities in six Breast Cancer Surveillance Consortium (BCSC) registries. The study included 1 682 504 digital screening mammograms performed between 2007 and 2013 in 792 808 women. Performance measures were calculated according to the American College of Radiology Breast Imaging Reporting and Data System, 5th edition, and were compared with published benchmarks by the BCSC, the National Mammography Database, and performance recommendations by expert opinion. Benchmarks were derived from the distribution of performance metrics across radiologists and were presented as 50th (median), 10th, 25th, 75th, and 90th percentiles, with graphic presentations using smoothed curves. Results Mean screening performance measures were as follows: abnormal interpretation rate (AIR), 11.6 (95% confidence interval [CI]: 11.5, 11.6); cancers detected per 1000 screens, or cancer detection rate (CDR), 5.1 (95% CI: 5.0, 5.2); sensitivity, 86.9% (95% CI: 86.3%, 87.6%); specificity, 88.9% (95% CI: 88.8%, 88.9%); false-negative rate per 1000 screens, 0.8 (95% CI: 0.7, 0.8); positive predictive value (PPV) 1, 4.4% (95% CI: 4.3%, 4.5%); PPV2, 25.6% (95% CI: 25.1%, 26.1%); PPV3, 28.6% (95% CI: 28.0%, 29.3%); cancers stage 0 or 1, 76.9%; minimal cancers, 57.7%; and node-negative invasive cancers, 79.4%. Recommended CDRs were achieved by 92.1% of radiologists in community practice, and 97.1% achieved recommended ranges for sensitivity. Only 59.0% of radiologists achieved recommended AIRs, and only 63.0% achieved recommended levels of specificity. Conclusion The majority of radiologists in the BCSC surpass cancer detection recommendations for screening mammography; however, AIRs continue to be higher than the recommended rate for almost half of radiologists interpreting screening mammograms. © RSNA, 2016 Online supplemental material is available for this article.
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.
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
Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening
Pisano, E. D., Gatsonis, C., Hendrick, E., Yaffe, M., Baum, J. K., Acharyya, S., Conant, E. F., Fajardo, L. L., Bassett, L., D'Orsi, C., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa052911 · Cited by 1,940
REDUCTION IN MORTALITY FROM BREAST CANCER AFTER MASS SCREENING WITH MAMMOGRAPHY
Tabár, L., Gad, A., Holmberg, L. H., Ljungquist, U., Fagerberg, C. J. G., Baldetorp, L., Gröntoft, O., Lundström, B., Månson, J. C., Eklund, G., et al. (1985) · The Lancet · doi:10.1016/s0140-6736(85)92204-4 · Cited by 1,781
Oeffinger, K. C., Fontham, E. T. H., Etzioni, R., Herzig, A., Michaelson, J. S., Shih, Y.-C. T., Walter, L. C., Church, T. R., Flowers, C. R., LaMonte, S. J., et al. (2015) · JAMA · doi:10.1001/jama.2015.12783 · Cited by 1,653
The benefits and harms of breast cancer screening: an independent review
(2012) · The Lancet · doi:10.1016/s0140-6736(12)61611-0 · Cited by 1,492
Skaane, P., Bandos, A. I., Gullien, R., Eben, E. B., Ekseth, U., Haakenaasen, U., Izadi, M., Jebsen, I. N., Jahr, G., Krager, M., et al. (2013) · Radiology · doi:10.1148/radiol.12121373 · Cited by 1,012
Breast Cancer Screening Using Tomosynthesis in Combination With Digital Mammography
Friedewald, S. M., Rafferty, E. A., Rose, S. L., Durand, M. A., Plecha, D. M., Greenberg, J. S., Hayes, M. K., Copit, D. S., Carlson, K. L., Cink, T. M., et al. (2014) · JAMA · doi:10.1001/jama.2014.6095 · Cited by 851
Ten- to Fourteen-Year Effect of Screening on Breast Cancer Mortality 2
(1982) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/69.2.349 · Cited by 840
Ciatto, S., Houssami, N., Bernardi, D., Caumo, F., Pellegrini, M., Brunelli, S., Tuttobene, P., Bricolo, P., Fantò, C., Valentini, M., et al. (2013) · The Lancet Oncology · doi:10.1016/s1470-2045(13)70134-7 · Cited by 838
Tice, J. A., Cummings, S. R., Smith-Bindman, R., Ichikawa, L., Barlow, W. E., & Kerlikowske, K. (2008) · Annals of Internal Medicine · doi:10.7326/0003-4819-148-5-200803040-00004 · Cited by 537
Self-citationDiagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening
Pisano, E. D., Gatsonis, C., Hendrick, E., Yaffe, M., Baum, J. K., Acharyya, S., Conant, E. F., Fajardo, L. L., Bassett, L., D???Orsi, C., et al. (2006) · Obstetrical & Gynecological Survey · doi:10.1097/01.ogx.0000201894.94616.71 · Cited by 482
Cited by
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
Liu, M. C., Oxnard, G. R., Klein, E. A., Swanton, C., Seiden, M. V., Liu, M. C., Oxnard, G. R., Klein, E. A., Smith, D., Richards, D., et al. (2020) · Annals of Oncology · doi:10.1016/j.annonc.2020.02.011 · Cited by 1,495
Deep Learning to Improve Breast Cancer Detection on Screening Mammography
Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., & Sieh, W. (2019) · Scientific Reports · doi:10.1038/s41598-019-48995-4 · Cited by 873
Establishment of Best Practices for Evidence for Prediction: A Review
Poldrack, R. A., Huckins, G., & Varoquaux, G. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.3671 · Cited by 738
Lotter, W., Diab, A. R., Haslam, B., Kim, J. G., Grisot, G., Wu, E., Wu, K., Onieva, J. O., Boyer, Y., Boxerman, J. L., et al. (2021) · Nature Medicine · doi:10.1038/s41591-020-01174-9 · Cited by 423
Metadata
- Authors
- Lehman, C. D., Arao, R. F., Sprague, B. L., Lee, J. M., Buist, D. S. M., Kerlikowske, K., Henderson, L. M., Onega, T., Tosteson, A. N. A., Rauscher, G. H., & Miglioretti, D. L.
- Venue
- Radiology
- Published
- 2017-04
- Volume
- 283
- Issue
- 1
- Pages
- 49-58
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2560617851
- Cited by
- 665
- References
- 25
- FWCI
- 30.19
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium. https://app.cochat.ai/papers/10.1148/radiol.2016161174
- The paper
- Lehman, C. D., Arao, R. F., Sprague, B. L., Lee, J. M., Buist, D. S. M., Kerlikowske, K., Henderson, L. M., Onega, T., Tosteson, A. N. A., Rauscher, G. H., & Miglioretti, D. L. (2017). National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium. Radiology. https://doi.org/10.1148/radiol.2016161174
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
