Compression forces used in the Norwegian Breast Cancer Screening Program

Objectives: Compression is used in mammography to reduce breast thickness, which is claimed to improve image quality and reduce radiation dose. In the Norwegian Breast Cancer Screening Program (NBCSP), the recommended range of compression force for full field digital mammography is 11-18 kg (108-177 Newton [N]). This is the first study to investigate the compression force used in the program. Methods: The study included information from 17,951 randomly selected women screened with FFDM at 14 breast centres in the NBCSP, January-March 2014. We investigated the applied compression force on left breast in craniocaudal (CC) and mediolateral oblique (MLO) view for breast centres, mammography machines within the breast centres and for the radiographers. Results: The mean compression force for all mammograms in the study was 116N and ranged from 91 to 147N between the breast centres. The variation in compression force was wider between the breast centres than between mammography machines (range 137-155N) and radiographers (95-143N) within one breast centre. Approximately 59% of the mammograms in the study complied with the recommended range of compression force. Conclusions: A wide variation in applied compression force was observed between the breast centres in the NBCSP. This variation indicates a need for evidence-based recommendations for compression force aimed at optimizing the image quality and individualising breast compression. Advances in knowledge: There was a wide variation in applied compression force between the breast centres in the NBCSP. The variation was wider between the breast centres than between mammography machines and radiographers within one breast centre

Comments on John D. Keen and James E. Keen, What is the point: will screening mammography save my life? BMC Medical Informatics and Decision Making, 2009

This paper by John D. Keen and James E. Keen addresses a thorny subject. The numerical findings and commentaries in their paper will be disturbing to some readers and seem to defy logic and well established viewpoints. It may well generate angry letters to the editor. However such numerical analysis and reporting including civil discussion should be welcomed and are the basis for informed decision making – something that is highly needed in this field

Computer-aided detection system for clustered microcalcifications: comparison of performance on full-field digital mammograms and digitized screen-film mammograms

We have developed a computer-aided detection (CAD) system to detect clustered microcalcifications automatically on full-field digital mammograms (FFDMs) and a CAD system for screen-film mammograms (SFMs). The two systems used the same computer vision algorithms but their false positive (FP) classifiers were trained separately with sample images of each modality. In this study, we compared the performance of the CAD systems for detection of clustered microcalcifications on pairs of FFDM and SFM obtained from the same patient. For case-based performance evaluation, the FFDM CAD system achieved detection sensitivities of 70%, 80% and 90% at an average FP cluster rate of 0.07, 0.16 and 0.63 per image, compared with an average FP cluster rate of 0.15, 0.38 and 2.02 per image for the SFM CAD system. The difference was statistically significant with the alternative free-response receiver operating characteristic (AFROC) analysis. When evaluated on data sets negative for microcalcification clusters, the average FP cluster rates of the FFDM CAD system were 0.04, 0.11 and 0.33 per image at detection sensitivity level of 70%, 80% and 90% compared with an average FP cluster rate of 0.08, 0.14 and 0.50 per image for the SFM CAD system. When evaluated for malignant cases only, the difference of the performance of the two CAD systems was not statistically significant with AFROC analysis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58099/2/pmb7_4_008.pd

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134952/1/mp8389.pd

The spatial distribution of radiodense breast tissue: a longitudinal study

Introduction Mammographic breast density is one of the strongest known markers of susceptibility to breast cancer. To date research into density has relied on a single measure ( for example, percent density (PD)) summarising the average level of density for the whole breast, with no consideration of how the radiodense tissue may be distributed. This study aims to investigate the spatial distribution of density within the breast using 493 mammographic images from a sample of 165 premenopausal women (similar to 3 medio-lateral oblique views per woman).Methods Each breast image was divided into 48 regions and the PD for the whole breast ( overall PD) and for each one of its regions ( regional PD) was estimated. The spatial autocorrelation ( Moran's I value) of regional PD for each image was calculated to investigate spatial clustering of density, whether the degree of clustering varied between a woman's two breasts and whether it was affected by age and other known density correlates.Results The median Moran's / value for 165 women was 0.31 (interquartile range: 0.26, 0.37), indicating a clustered pattern. High-density areas tended to cluster in the central regions of the breast, regardless of the level of overall PD, but with considerable between-woman variability in regional PD. The degree of clustering was similar between a woman's two breasts (mean within-woman difference in Moran's / values between left and right breasts = 0.00 (95% confidence interval (CI) = -0.01, 0.01); P = 0.76) and did not change with aging (mean within-woman difference in I values between screens taken on average 8 years apart = 0.01 (95% CI = -0.01, 0.02); P = 0.30). Neither parity nor age at first birth affected the level of spatial autocorrelation of density, but increasing body mass index (BMI) was associated with a decrease in the degree of spatial clustering.Conclusions This study is the first to demonstrate that the distribution of radiodense tissue within the breast is spatially autocorrelated, generally with the high-density areas clustering in the central regions of the breast. The degree of clustering was similar within a woman's two breasts and between women, and was little affected by age or reproductive factors although it declined with increasing BMI

Image Processing Algorithms for Digital Mammography: A Pictorial Essay

Digital mammography systems allow manipulation of fine differences in image contrast by means of image processing algorithms. Different display algorithms have advantages and disadvantages for the specific tasks required in breast imaging—diagnosis and screening. Manual intensity windowing can produce digital mammograms very similar to standard screen-film mammograms but is limited by its operator dependence. Histogram-based intensity windowing improves the conspicuity of the lesion edge, but there is loss of detail outside the dense parts of the image. Mixture-model intensity windowing enhances the visibility of lesion borders against the fatty background, but the mixed parenchymal densities abutting the lesion may be lost. Contrast-limited adaptive histogram equalization can also provide subtle edge information but might degrade performance in the screening setting by enhancing the visibility of nuisance information. Unsharp masking enhances the sharpness of the borders of mass lesions, but this algorithm may make even an indistinct mass appear more circumscribed. Peripheral equalization displays lesion details well and preserves the peripheral information in the surrounding breast, but there may be flattening of image contrast in the nonperipheral portions of the image. Trex processing allows visualization of both lesion detail and breast edge information but reduces image contrast

The Role of Magnetic Resonance Imaging in Diagnosis and Management of Breast Cancer

A review of the literature on the current applications of breast magnetic resonance imaging (MRI) indications, their rationale and their place in diagnosis and management of breast cancer was given. Contrast-enhanced breast MRI is developing as a valuable adjunct to mammography and sonography. Its high sensitivity for invasive breast cancer establishes its superiority in evaluation of multifocality/multicentricity, tumor response to neoadjuvant chemotherapy, detection of recurrence, and staging. Emerging applications include spectroscopy, usage of new contrast agents, and MRI-guided interventions, including noninvasive treatment of breast cancer. Its potential benefit in screening high-risk women has yet to be established with prospective studies, particularly with regard to false positive results