Dynamic functional and mechanical response of breast tissue to compression.

Physiological tissue dynamics following breast compression offer new contrast mechanisms for evaluating breast health and disease with near infrared spectroscopy. We monitored the total hemoglobin concentration and hemoglobin oxygen saturation in 28 healthy female volunteers subject to repeated fractional mammographic compression. The compression induces a reduction in blood flow, in turn causing a reduction in hemoglobin oxygen saturation. At the same time, a two phase tissue viscoelastic relaxation results in a reduction and redistribution of pressure within the tissue and correspondingly modulates the tissue total hemoglobin concentration and oxygen saturation. We observed a strong correlation between the relaxing pressure and changes in the total hemoglobin concentration bearing evidence of the involvement of different vascular compartments. Consequently, we have developed a model that enables us to disentangle these effects and obtain robust estimates of the tissue oxygen consumption and blood flow. We obtain estimates of 1.9+/-1.3 micromol/100 mL/min for OC and 2.8+/-1.7 mL/100 mL/min for blood flow, consistent with other published values.

Automated detection of microcalcification clusters for digital breast tomosynthesis using projection data only: a preliminary study.

Digital breast tomosynthesis (DBT) is a promising modality for breast imaging in which an anisotropic volume image of the breast is obtained. We present an algorithm for computerized detection of microcalcification clusters (MCCs) for DBT. This algorithm operates on the projection views only. Therefore it does not depend on reconstruction, and is computationally efficient. The algorithm was developed using a database of 30 image sets with microcalcifications, and a control group of 30 image sets without visible findings. The patient data were acquired on the first DBT prototype at Massachusetts General Hospital. Algorithm sensitivity was estimated to be 0.86 at 1.3 false positive clusters, which is below that of current MCC detection algorithms for full-field digital mammography. Because of the small number of patient cases, algorithm parameters were not optimized and one linear classifier was used. An actual limitation of our approach may be that the signal-to-noise ratio in the projection images is too low for microcalcification detection. Furthermore, the database consisted of predominantly small MCC. This may be related to the image quality obtained with this first prototype.

Computerized mass detection for digital breast tomosynthesis directly from the projection images.

Digital breast tomosynthesis (DBT) has recently emerged as a new and promising three-dimensional modality in breast imaging. In DBT, the breast volume is reconstructed from 11 projection images, taken at source angles equally spaced over an arc of 50 degrees. Reconstruction algorithms for this modality are not fully optimized yet. Because computerized lesion detection in the reconstructed breast volume will be affected by the reconstruction technique, we are developing a novel mass detection algorithm that operates instead on the set of raw projection images. Mass detection is done in three stages. First, lesion candidates are obtained for each projection image separately, using a mass detection algorithm that was initially developed for screen-film mammography. Second, the locations of a lesion candidate are backprojected into the breast volume. In this feature volume, voxel intensities are a combined measure of detection frequency (e.g., the number of projections in which a given lesion candidate was detected), and a measure of the angular range over which a given lesion was detected. Third, features are extracted after reprojecting the three-dimensional (3-D) locations of lesion candidates into projection images. Features are combined using linear discriminant analysis. The database used to test the algorithm consisted of 21 mass cases (13 malignant, 8 benign) and 15 cases without mass lesions. Based on this database, the algorithm yielded a sensitivity of 90% at 1.5 false positives per breast volume. Algorithm performance is positively biased because this dataset was used for development, training, and testing, and because the number of algorithm parameters was approximately the same as the number.of patient cases. Our results indicate that computerized mass detection in the sequence of projection images for DBT may be effective despite the higher noise level in those images.

Computerized detection of mass lesions in digital breast tomosynthesis images using two- and three dimensional radial gradient index segmentation.

Initial results for a computerized mass lesion detection scheme for digital breast tomosynthesis (DBT) images are presented. The algorithm uses a radial gradient index feature for the initial lesion detection and for segmentation of lesion candidates. A set of features is extracted for each segmented partition. Performance of two- and three dimensional features was compared. For gradient features, the additional dimension provided no improvement in classification performance. For shape features, classification using 3D features was improved compared to the 2D equivalent features. The preliminary overall performance was 76% sensitivity at 11 false positives per exam, estimated based on DBT image data of 21 masses. A larger database will allow for further development and improvement in our computer aided detection scheme.

Mammographic appearance of ductal carcinoma in situ does not reliably predict histologic subtype.

Histologic subtypes of ductal carcinoma in situ (DCIS) have been correlated with disease prognosis. There are conflicting reports on whether the grade of DCIS can be predicted by the morphology of calcifications seen on mammography. We undertook this study to determine whether the grade of DCIS can be reliably and accurately determined by mammography prior to excisional biopsy. Ninety consecutive cases of DCIS from 1993 to 1996 were identified, of which 75 cases had mammograms available for review. Any lesion with invasion was excluded. The mammogram showed only a mass in 10 of 75 cases, a mass and calcifications in 3 of 75 cases, and calcifications alone in 62 of 75 cases. Three board-certified radiologists with special expertise in mammography reviewed and categorized the mammographic findings as well, intermediate or poorly differentiated DCIS without knowledge of the histologic diagnosis. Histologic grading was performed without knowledge of the mammographic finding. Receiver operating curves (ROCs) were computed for each of the radiologists. For microcalcifications, the ROC comparisons of the radiologists' opinions of tumor grade and random chance were not significantly different. In those cases with available magnification views, the grade assessment did not change significantly. If only a mass was present on mammography, well-differentiated DCIS was the predominant histologic subtype. A histologic grade of DCIS cannot accurately be determined prospectively based on the mammographic appearance of microcalcifications. However, if only a mass is present, this is more likely to represent well-differentiated DCIS.

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.

Double reading.

Clearly, the cost of double reading varies with the approach used. The Massachusetts General Hospital method can only lead to an increase in recalls and the costs that these engender (anxiety for the women recalled, trauma from any biopsies obtained, and the actual monetary costs of additional imaging and interventions). It is of interest that one potential cost, the concern that women recalled may be reluctant to participate again in screening, does not seem to be the case. Women who are recalled appear to be more likely to participate in future screening. Double interpretation where there must be a consensus between the interpreting radiologists, and if this cannot be reached a third arbiter, is the most labor intensive, but can reduce the number of recalls in a double reading system. Computer systems have been developed to act as a second reader. The films must be digitized and then fed through the reader, but studies suggest that the computer can identify cancers that may be overlooked by a human reader. The challenge is to do this without too many false-positive calls. If the radiologist finds the false-positives are too numerous and distracting, then the system is not used. As digital mammographic systems proliferate, and computer algorithms become more sophisticated, the second human reader will likely be replaced by a computer-aided detection system and double reading will become the norm.

Potential contribution of computer-aided detection to the sensitivity of screening mammography.

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.

Breast cancer: computer simulation method for estimating optimal intervals for screening.

PURPOSE: To develop and evaluate a mathematic method that can be used to determine the optimal screening interval for detection of breast cancer prior to distant metastatic spread. MATERIALS AND METHODS: A computer simulation was developed with the use of biologically based data from the literature on the rates of tumor growth and spread, which can be used to calculate the course of breast cancer growth and metastasis. RESULTS: On the basis of the data available at this time, the results of the simulations suggested that a screening interval of 2 years would result in a 22% reduction in the rate of distant metastatic disease, an interval of 1 year would result in a 51% reduction, and an interval of 6 months would result in an 80% reduction. CONCLUSION: These findings suggest that more frequent screening could dramatically reduce the death rate from breast cancer.