The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Potential Contribution of Computer-aided Detection to the Sensitivity of Screening Mammography
Abstract
PURPOSE: To determine the false-negative rate in screening mammography, the capability of computer-aided detection (CAD) to identify these missed lesions, and whether or not CAD increases the radiologists' recall rate. MATERIALS AND METHODS: All available screening mammograms that led to the detection of biopsy-proved cancer (n = 1,083) and the most recent corresponding prior mammograms (n = 427) were collected from 13 facilities. Panels of radiologists evaluated the retrospectively visible prior mammograms by means of blinded review. All mammograms were analyzed by a CAD system that marks features associated with cancer. The recall rates of 14 radiologists were prospectively measured before and after installation of the CAD system. RESULTS: At retrospective review, 67% (286 of 427) of screening mammography-detected breast cancers were visible on the prior mammograms. At independent, blinded review by panels of radiologists, 27% (115 of 427) were interpreted as warranting recall on the basis of a statistical evaluation index; and the CAD system correctly marked 77% (89 of 115) of these cases. The original attending radiologists' sensitivity was 79% (427 of [427 + 115]). There was no statistically significant increase in the radiologists' recall rate when comparing the values before (8.3%) with those after (7.6%) installation of the CAD system. CONCLUSION: The original attending radiologists had a false-negative rate of 21% (115 of [427 + 115]). CAD prompting could have potentially helped reduce this false-negative rate by 77% (89 of 115) without an increase in the recall rate.
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Cites
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Self-citationBenefit of independent double reading in a population-based mammography screening program.
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Cited by
Computer-aided diagnosis in medical imaging: historical review, current status and future potential
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American Cancer Society Guidelines for Breast Cancer Screening: Update 2003
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Detecting and classifying lesions in mammograms with Deep Learning
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A review of automatic mass detection and segmentation in mammographic images
Oliver, A., Freixenet, J., Martí, J., Pérez, E., Pont, J., Denton, E. R. E., & Zwiggelaar, R. (2010) · Medical Image Analysis · doi:10.1016/j.media.2009.12.005 · Cited by 398
Metadata
- Authors
- Warren Burhenne, L. J., Wood, S. A., D'Orsi, C. J., Feig, S. A., Kopans, D. B., O'Shaughnessy, K. F., Sickles, E. A., Tabar, L., Vyborny, C. J., & Castellino, R. A.
- Venue
- Radiology
- Published
- 2000-05
- Volume
- 215
- Issue
- 2
- Pages
- 554-562
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2078710233
- Cited by
- 554
- References
- 27
- FWCI
- 23.75
- Type
- journal article
- Language
- en
- Open access
- closed
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- Warren Burhenne, L. J., Wood, S. A., D'Orsi, C. J., Feig, S. A., Kopans, D. B., O'Shaughnessy, K. F., Sickles, E. A., Tabar, L., Vyborny, C. J., & Castellino, R. A. (2000). Potential Contribution of Computer-aided Detection to the Sensitivity of Screening Mammography. Radiology. https://doi.org/10.1148/radiology.215.2.r00ma15554
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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.
