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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.
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“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].”
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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
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Items 401–500 of 676
Meltzer, C., & Skaane, P. (2022) · Medical radiology · doi:10.1007/978-3-030-94918-1_3 · Cited by 6
The Case of Missed Cancers: Applying AI as a Radiologist’s Safety Net
Chorev, M., Shoshan, Y., Akselrod-Ballin, A., Spiro, A., Naor, S., Hazan, A., Barros, V., Weinstein, I., Herzel, E., Shalev, V., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59725-2_22 · Cited by 7
Condon, J. J. J., Oakden‐Rayner, L., Hall, K. A., Reintals, M., Holmes, A., Carneiro, G., & Palmer, L. J. (2021) · medRxiv · doi:10.1101/2021.05.28.21257892 · Cited by 7
Advancing Artificial Intelligence to Meet Breast Imaging Needs
Philpotts, L. E. (2022) · Radiology · doi:10.1148/radiol.213101 · Cited by 6
Toward More Equitable Breast Cancer Outcomes
Elmore, J. G., & Lee, C. I. (2024) · JAMA · doi:10.1001/jama.2024.6052 · Cited by 6
Amer, M., Gittins, R., Millana, A. M., Scheibein, F., Ferri, M., Tofighi, B., Sullivan, F., Handley, M., Ghosh, M., Baldacchino, A., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/58723 · Cited by 5
Shim, S., Hussain, L., Alsolami, E., & Alkinani, M. H. (2025) · IEEE Access · doi:10.1109/access.2025.3566353 · Cited by 5
Diab, A. R., & Lotter, W. E. (2025) · Radiology Artificial Intelligence · doi:10.1148/ryai.250369 · Cited by 8
Confidence in radiology AI: From black-box scores to trusted decisions
Karunanayake, N. (2025) · European Journal of Radiology Artificial Intelligence · doi:10.1016/j.ejrai.2025.100046 · Cited by 5
Self-supervised learning for breast cancer detection: A review
Figueiras, H., Domingues, J., Matela, N., & Garcia, N. C. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111245 · Cited by 5
A Novel Deep Learning Approach for Breast Cancer Detection on Screening Mammography
Leung, C. K.-S., & Nguyen, H. H. (2023) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe60311.2023.00052 · Cited by 5
Artificial Intelligence for Breast Cancer Screening: Trade-offs between Sensitivity and Specificity
Bahl, M., & Do, S. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.240184 · Cited by 5
Salloum, S. A. (2025) · JMIR Formative Research · doi:10.2196/71974 · Cited by 5
Danala, G., Maryada, S. K., Pham, H., Islam, W., Jones, M. A., & Zheng, B. (2022) · SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment · doi:10.1117/12.2611886 · Cited by 5
Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms
Sehgal, A., Sehgal, M., La, H. M., & Bebis, G. N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20716-7_21 · Cited by 5
Malmö Breast ImaginG database: objectives and development
Dahlblom, V., Dustler, M., Bolejko, A., Bakić, P. R., Granberg, H., Johnson, K., Förnvik, D., Lång, K., Tingberg, A., & Zackrisson, S. (2023) · Journal of medical imaging · doi:10.1117/1.jmi.10.6.061402 · Cited by 5
Arce, S., Vijay, A., Yim, E., Spiguel, L. R., & Hanna, M. (2023) · Cureus · doi:10.7759/cureus.38770 · Cited by 5
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Gilbert, F. J. (2023) · Nature Medicine · doi:10.1038/s41591-023-02441-1 · Cited by 5
Seigneurin, A., Exbrayat, C., Molinié, F., Croisier, L., Poncet, F., Berquet, K., Delafosse, P., & Colonna, M. (2020) · American Journal of Epidemiology · doi:10.1093/aje/kwaa218 · Cited by 5
Pavlovich, P. I., Бронов, О., Kapninskiy, A., Abovich, Y. A., & Rychagova, N. I. (2021) · Digital Diagnostics · doi:10.17816/dd83184 · Cited by 5
Dontchos, B. N., Hom, K. C., Lamb, L. R., Mercaldo, S., Eklund, M., Dang, P. A., & Lehman, C. D. (2022) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2022.04.001 · Cited by 5
Self-citationLee, S. E., Kim, G. R., Yoon, J. H., Han, K., Son, W. J., Shin, H. J., & Moon, H. J. (2022) · Acta Radiologica · doi:10.1177/02841851221140556 · Cited by 5
Primer for Image Informatics in Personalized Medicine
Chang, Y. H., Foley, P., Azimi, V., Borkar, R. N., & Lefman, J. (2016) · Procedia Engineering · doi:10.1016/j.proeng.2016.08.064 · Cited by 5
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de Santiago, I., & Polański, Ł. (2020) · Preprints.org · doi:10.20944/preprints202010.0356.v1 · Cited by 5
Chang, T.-Y., Wu, J., Liu, P.-Y., Liu, Y.-L., Luzhbin, D. A., & Lin, H.-C. (2022) · Electronics · doi:10.3390/electronics11111784 · Cited by 5
Artificial Intelligence‐Driven Decisions in Breast Cancer Diagnosis
Gangwal, A., & Gautam, R. K. (2023) · Wiley eBooks · doi:10.1002/9783527841165.ch8 · Cited by 5
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Wu, K., Wu, E., Theodorou, B., Liang, W., Mack, C. D., Glass, L. M., Sun, J., & Zou, J. (2023) · medRxiv · doi:10.1101/2023.08.26.23294629 · Cited by 5
Few-Shot Breast Cancer Diagnosis Using a Siamese Neural Network Framework and Triplet-Based Loss
Marasović, T., & Papić, V. (2025) · Algorithms · doi:10.3390/a18090567 · Cited by 5
Kashiwada, Y., Takaya, E., Hiroya, M., Matsuda, N., Yashima, T., Kobayashi, T., Tamiya, G., & Ueda, T. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3252 · Cited by 4
AI Use in Mammography for Diagnosing Metachronous Contralateral Breast Cancer
Adachi, M., Fujioka, T., Ishiba, T., Nara, M., Maruya, S., Hayashi, K., Kumaki, Y., Yamaga, E., Katsuta, L., Hao, D., et al. (2024) · Journal of Imaging · doi:10.3390/jimaging10090211 · Cited by 4
Enhancing Breast Cancer Detection Using A Transformer-Based Model
Sharma, S., & Singh, Y. (2024) · International Conference on Information Science and Communications Technologies (ICISCT) · doi:10.1109/icisct64202.2024.10957451 · Cited by 4
Yamaguchi, T., Koyama, Y., Inoue, K., Ban, K., Hirokaga, K., Kujiraoka, Y., Okanami, Y., Shinohara, N., Tsunoda, H. S., Uematsu, T., et al. (2025) · Breast Cancer · doi:10.1007/s12282-025-01741-3 · Cited by 4
Moger, T. A., Nardin, S. B., Holen, Å. S., Moshina, N., & Hofvind, S. S. H. (2025) · Journal of Medical Screening · doi:10.1177/09691413251372829 · Cited by 4
Yazarkan, Y., Sönmez, G., Sahin, T. K., Rizzo, A., & Güven, D. C. (2026) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2026.2648127 · Cited by 5
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Peng, Y., Chen, Q., Keenan, T. D. L., Chew, E. Y., & Lu, Z. (2021) · Springer eBooks · doi:10.1007/978-3-030-78601-4_8 · Cited by 4
Mawarni, M., Utaminingrum, F., & Mahmudy, W. F. (2022) · Journal of biomimetics, biomaterials and biomedical engineering · doi:10.4028/p-09g3n8 · Cited by 5
Ethical Considerations of Artificial Intelligence Applications in Healthcare
Gichoya, J. W., MELTZER, C. C., Newsome, J. M., Correa, R., Trivedi, H., Banerjee, I., Davis, M. A., & Celi, L. A. (2022) · Contemporary medical imaging · doi:10.1007/978-3-030-92087-6_52 · Cited by 5
Emerging Value-Based Radiology in the Era of Artificial Intelligence
Mun, S. K., Lo, S. B., Wong, K. H., Koh, D., & Prior, F. (2023) · Medical Research Archives · doi:10.18103/mra.v11i5.3915 · Cited by 6
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Noor, K. A., Norsuddin, N. M., Isa, I. N. C., & Karim, M. K. A. (2024) · Radiography · doi:10.1016/j.radi.2024.04.019 · Cited by 4
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Effectiveness of neural language models for word prediction of textual mammography reports
Marin, M. D., Mocanu, E., & Seifert, C. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283304 · Cited by 4
Applications of Artificial Intelligence in Mammography from a Development and Validation Perspective
Kim, K. H., & Lee, S. H. (2021) · Journal of the Korean Society of Radiology · doi:10.3348/jksr.2020.0205 · Cited by 5
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Shruthishree S.H, D. H. T. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i6.5686 · Cited by 4
Two-Stage Multi-Scale Mass Segmentation From Full Mammograms
Yan, Y., Conze, P.-H., Quellec, G., Lamard, M., Cochener, B., & Coatrieux, G. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433946 · Cited by 4
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Mohamed, S. K., Sakr, N. A., & Hikal, N. A. (2021) · International Journal of Advanced Science Computing and Engineering · doi:10.30630/ijasce.3.3.55 · Cited by 4
Sultan, S., Acharya, Y., Zayed, O., Elzomour, H., Parodi, J. C., Soliman, O. I. I., & Hynes, N. (2022) · Journal of Endovascular Therapy · doi:10.1177/15266028221102660 · Cited by 4
Brereton, M. F., Ambe, A. H., Lovell, D. R., Sitbon, L., Capel, T., Soro, A., Xu, Y., Moreira, C., Favre, B., & Bradley, A. P. (2023) · Australian Conference on Computer-Human Interaction (OzCHI) · doi:10.1145/3638380.3638435 · Cited by 4
Patterson, F., & Kunar, M. A. (2024) · Cognitive Research Principles and Implications · doi:10.1186/s41235-024-00576-4 · Cited by 4
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Marino, M. A., Avendaño, D., Sofia, C., Zapata, P., Portaluri, A., Orlando, A. A. M., Avalos-Montes, P. J., Blandino, A., Ascenti, G., & Cardona-Huerta, S. (2022) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405619666221128102209 · Cited by 3
Shen, J., Liu, Y., Liu, A., Gu, X., Zhou, J., Jiang, P., Mo, M., Zhang, L., Yang, C., Zhou, C., et al. (2025) · Breast Cancer Research · doi:10.1186/s13058-025-02128-0 · Cited by 3
Corines, M. J., Christianson, B., Comstock, C., Drotman, M. B., & Dodelzon, K. (2026) · British Journal of Radiology · doi:10.1093/bjr/tqag053 · Cited by 4
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Ghasemi, A., & aali, M. N. (2023) · Annals of Military and Health Sciences Research · doi:10.5812/amh-134440 · Cited by 4
Anderson, R. T., Hillemeier, M. M., CAMACHO, F. T., Harvey, J. A., Bonilla, G., Batten, G. P., Robinson, B., Safon, C. B., & Louis, C. (2023) · The Journal of Rural Health · doi:10.1111/jrh.12798 · Cited by 3
Wärnberg, F., Axelsson, O., Curiac, D., Hargreaves, P., Karakatsanis, A., Lekmeechai, S., & Hansen, M. (2025) · Investigative Radiology · doi:10.1097/rli.0000000000001164 · Cited by 3
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Dreizin, D. (2023) · Emergency Radiology · doi:10.1007/s10140-023-02135-8 · Cited by 2
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Badawy, E., Shalaby, F. S., El-nasr, S. I. S., Elyamany, A. M., & Hegazy, R. M. A. (2023) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-023-01136-4 · Cited by 3
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Dhou, S., Alhusari, K., & Alkhodari, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95462-4.00004-2 · Cited by 3
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Tekin, A., Toktay, B., Günay, A. C., Yazgan, H., İnan, N. G., & Kocadağlı, O. (2024) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub518.c2134 · Cited by 3
Yu, S., Young, C. Y., Chan, Y., Chan, Y., Tsoi, C., Choi, M. N., Chan, T., Leung, J. C. S., Chu, C. W., Hung, E. H. Y., et al. (2024) · Hong Kong Medical Journal · doi:10.12809/hkmj2310920 · Cited by 3
Zuckerman, S. P., Periaswamy, S., Shisler, J. L., Elahi, A., Edmonds, C. E., Hoffmeister, J. W., & Conant, E. F. (2025) · Radiology Artificial Intelligence · doi:10.1148/ryai.230597 · Cited by 3
Mathur, A. S., Pawar, S., Kamma, P. K. G., Obulareddy, V. T., Dash, K. S., Meto, A., & Mehta, V. (2025) · International Electronic Conference on Applied Sciences (ASEC) · doi:10.3390/engproc2025087080 · Cited by 3
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Neumuth, T. (2020) · Forum · doi:10.1007/s12312-019-00734-6 · Cited by 3
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Traino, C., Barca, P., Lamastra, R., Tucciariello, R. M., Sottocornola, C., Marini, C., Aringhieri, G., Caramella, D., & Fantacci, M. E. (2020) · European Radiology Experimental · doi:10.1186/s41747-020-00165-2 · Cited by 3
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Thomassin‐Naggara, I., Ceugnart, L., Tardivon, A. A., Verzaux, L., Balleyguier, C., Taourel, P., & Séradour, B. (2022) · Bulletin du Cancer · doi:10.1016/j.bulcan.2022.04.008 · Cited by 3
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Park, G. E., Shin, K., Mun, H. S., & Kang, B. J. (2025) · Tomography · doi:10.3390/tomography11070072 · Cited by 2
Behzad, S., Boucher, J. O., Alavijeh, N. S., Ravi, K., & Gholamrezanezhad, A. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.07.057 · Cited by 2
Ahmadzade, M., Rouientan, H., Abdi, N., Norouzi, M., Hakimi, M., Bahrambeigi, M., Pouya, E. K., Bahmanyar, F., Haghi, S., Abbasi, F., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-31065-3 · Cited by 4
Breast Cancer Detection Using Supervised Machine Learning Algorithm
Shashidhar, R., Arunakumari, B. N., S, N. F. H., Puneeth, S. B., Santhosh Kumar, R., & Roopa, M. S. (2020) · ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY · doi:10.33130/ajct.2020v06i03.006 · Cited by 2
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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
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