Single reading with computer-aided detection could be an alternative to double reading and could improve the rate of detection of cancer from screening mammograms read by a single reader. (ClinicalTrials.gov number, NCT00450359.)

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Purpose: A method and computer tool to estimate percentage magnetic resonance (MR) imaging (MRI) breast density using three-dimensional T 1 -weighted MRI is introduced, and compared with mammographic percentage density [X-ray mammography (XRM)]. Materials and Methods: Ethical approval and informed consent were obtained. A method to assess MRI breast density as percentage volume occupied by watercontaining tissue on three-dimensional T 1 -weighted MR images is described and applied in a pilot study to 138 subjects who were imaged by both MRI and XRM during the Magnetic Resonance Imaging in Breast Screening study. For comparison, percentage mammographic density was measured from matching XRMs as a ratio of dense to total projection areas scored visually using a 21-point score and measured by applying a twodimensional interactive program (CUMULUS). The MRI and XRM percent methods were compared, including assessment of left-right and interreader consistency. Results: Percent MRI density correlated strongly (r = 0.78; P < 0.0001) with percent mammographic density estimated using Cumulus. Comparison with visual assessment also showed a strong correlation. The mammographic methods overestimate density compared with MRI volumetric assessment by a factor approaching 2. Discussion: MRI provides direct three-dimensional measurement of the proportion of water-based tissue in the breast. It correlates well with visual and computerized percent mammographic density measurements. This method may have direct application in women having breast cancer screening by breast MRI and may aid in determination of risk. (Cancer Epidemiol Biomarkers Prev 2008;17(9):2268 -74)

Objective To determine the cost to the NHS and the impact on anxiety of a one stop clinic for assessing women with suspected breast cancer. Study design Randomised controlled trial. Participants Women aged 35 or over referred with a breast lump. Study setting Teaching hospital, north west England. Interventions Women were randomly allocated to attend a one stop clinic or a dedicated breast clinic. Outcome measures Reduction in mean anxiety from baseline at 24 hours after the first visit and at 3 weeks and 3 months after diagnosis; mean cost per patient. Results 670 women were randomised. Compared with women who attended the dedicated clinic, patients attending the one stop clinic were less anxious 24 hours after the visit (adjusted mean change in state anxiety − 5.7 (95% confidence interval − 8.4 to − 3.0)) but not at 3 weeks or 3 months after diagnosis. The additional cost to the NHS of a one stop attendance was £32 per woman; this was largely explained by greater cytopathological and radiological staff costs. Conclusion One stop clinics may not be justified in terms of a reduction in short term anxiety.

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.