ArticleBreast cancer research and treatment2018
Screening mammography: benefit of double reading by breast density.
Article in Breast cancer research and treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 32 citations in OpenAlex.
- Review
- Pancreatic cancer detection with a non-contrast MR protocol: is it reliable?La Radiologia medica · 2023Article
- Breast density and risk of breast cancer.International journal of cancer · 2023Article
- The augmented radiologist: artificial intelligence in the practice of radiology.Pediatric radiology · 2022Review
- Repeat Breast Ultrasound Demonstrates Utility with Added Cancer Detection in Patients following Breast Imaging Second Opinion Recommendations.The breast journal · 2022Article
- Can artificial intelligence reduce the interval cancer rate in mammography screening?European radiology · 2021Article
- External validation of AI algorithms in breast radiology: the last healthcare security checkpoint?Quantitative imaging in medicine and surgery · 2021Article
- Breast Mammographic Density: Stromal Implications on Breast Cancer Detection and Therapy.Journal of clinical medicine · 2020Review
- Double reading in breast cancer screening: considerations for policy-making.The British journal of radiology · 2020Review
- Mammographic Density and Screening Sensitivity, Breast Cancer Incidence and Associated Risk Factors in Danish Breast Cancer Screening.Journal of clinical medicine · 2019Article
- Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier.Journal of imaging · 2019Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe currently recommended double reading of all screening mammography examinations is an economic burden for screening programs. The sensitivity of screening is higher for women with low breast density than for women with high density. One may therefore ask whether single reading could replace double reading at least for women with low density. We addressed this question using data from a screening program where the radiologists coded their readings independently.
methodsData include all screening mammography examinations in the Capital Region of Denmark from 1 November 2012 to 31 December 2013. Outcome of screening was assessed by linkage to the Danish Pathology Register. We calculated sensitivity, specificity, number of interval cancers, and false positive-tests per 1000 screened women by both single reader and consensus BI-RADS density code.
resultsIn total 54,808 women were included. The overall sensitivity of double reading was 72%, specificity was 97.6%, 3 women per 1000 screened experienced an interval cancer, and 24 a false-positive test. Across all BI-RADS density codes, single reading consistently decreased sensitivity as compared with consensus reading. The same was true for specificity, apart from results across BI-RADS density codes set by reader 2.
conclusionsSingle reading decreased sensitivity as compared with double reading across all BI-RADS density codes. This included results based on consensus BI-RADS density codes. This means that replacement of double with single reading would have negative consequences for the screened women, even if density could be assessed automatically calibrated to the usual consensus level.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.