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Biomedical subjects

Caroline R M Boggis

Publications and source records attributed to Caroline R M Boggis.

2 recordsLinked to original sources

Single reading with computer-aided detection and double reading of screening mammograms in the United Kingdom National Breast Screening Program.

PURPOSE: To retrospectively determine if the use of a computer-aided detection (CAD) system can improve the performance of single reading of screening mammograms to match that of double reading in the United Kingdom. MATERIALS AND METHODS: Local research ethics committee approval was obtained; informed consent was not required. This study included a sample of 10 267 mammograms obtained in women aged 50 years or older who underwent routine screening at one of two breast screening centers in 1996. Mammograms that were double read in 1996 were randomly allocated to be re-read by eight different radiologists using CAD. The cancer detection and recall rates from double reading and single reading with CAD were compared. Statistical significance and confidence intervals were calculated with the McNemar test to account for the matched nature of the data. RESULTS: Single reading with CAD led to a cancer detection rate that was significantly (P = .02) higher than that achieved with double reading: 6.5% more cancers were detected by means of single reading with CAD than by means of double reading. However, the recall rate was higher for single reading with CAD than for double reading (8.6% vs 6.5%, respectively; P < .001). This was equivalent to relative increases of 15% and 32% in the cancer detection and recall rates, respectively. CONCLUSION: Single reading with CAD leads to an improved cancer detection rate and an increased recall rate.

Breast Neoplasms↗

Linear structures in mammographic images: detection and classification.

We describe methods for detecting linear structures in mammograms, and for classifying them into anatomical types (vessels, spicules, ducts, etc). Several different detection methods are compared, using realistic synthetic images and receiver operating characteristic (ROC) analysis. There are significant differences (p < 0.001) between the methods, with the best giving an Az value for pixel-level detection of 0.943. We also investigate methods for classifying the detected linear structures into anatomical types, using their cross-sectional profiles, with particular emphasis on recognising the "spicules" and "ducts" associated with some of the more subtle abnormalities. Automatic classification results are compared with expert annotations using ROC analysis, demonstrating useful discrimination between anatomical classes (Az = 0.746). Some of this discrimination relies on simple attributes such as profile width and contrast, but important information is also carried by the shape of the profile (Az = 0.653). The methods presented have potentially wide application in improving the specificity of abnormality detection by exploiting additional anatomical information.

Algorithms↗