Adverse effects of increased body weight on quantitative measures of mammographic image quality

OBJECTIVE: The purpose of our study was to show that compressed breast thickness on mammograms in overweight and obese women exceeds the thickness in normal-weight women and that increased thickness results in image degradation. SUBJECTS AND METHODS: Three hundred consecutive routine mammograms were reviewed. Patients were categorized according to body mass index. Compression thickness, compressive force, kilovoltage, and milliampere-seconds were recorded. Geometric unsharpness and contrast degradation were calculated for each body mass index category. RESULTS: Body mass index categories were lean (3%), normal (36%), overweight (36%), and obese (25%). Body mass index was directly correlated with compressed thickness. In the mediolateral oblique view, the mean thickness of the obese category exceeded normal thickness by 18 mm (p < 0.01), corresponding to a 32% increase in geometric unsharpness. Mean obese thickness exceeded lean thickness by 33 mm (p < 0.01), corresponding to a 79% increase in unsharpness. Similar trends were observed for the craniocaudal view. In the mediolateral oblique projection, there was an increase of 1.0 kVp (p < 0.01) for obese compared with normal and 1.7 kVp (p < 0.01) between lean and obese, corresponding, respectively, to a 16% and a 25% decrease in image contrast because of scatter and kilovoltage changes. Milliampere-seconds increased by 47% on the mediolateral oblique images in the obese category compared with normal body mass index. CONCLUSION: An increased body mass index was associated with greater compressed breast thickness, resulting in increased geometric unsharpness, decreased image contrast, and greater potential for motion unsharpness.     

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.     

Neoadjuvant docetaxel and capecitabine and the use of thymidine phosphorylase as a predictive biomarker in breast cancer.

PURPOSE: Thymidine phosphorylase (TP) induction by docetaxel is a proposed mechanism for the observed preclinical synergy of docetaxel and capecitabine (DC). We evaluated whether TP protein expression is increased by docetaxel and correlates with pathologic complete response (pCR) in breast cancer patients. EXPERIMENTAL DESIGN: Women with stage II to III breast cancer were given four cycles of neoadjuvant docetaxel 36 mg/m(2) i.v. over 30 min on days 1, 8, and 15 and capecitabine 2,000 mg/d, in two divided doses, on days 5 to 21 of a 28-day cycle. Radiology-directed biopsies of the breast tumors were done at baseline and 5 days after the first dose of docetaxel to evaluate TP expression. Following DC therapy, patients had core breast biopsies, and if residual disease was present, received four cycles of standard dose-dense doxorubin and cyclophosphamide (AC). RESULTS: The pCR rate was 26.9% (95% confidence interval, 11.6-47.8). Up-regulation of TP expression was not observed by either quantitative immunofluorescence (QIF) or immunohistochemistry. Radiology-directed core biopsy after neoadjuvant chemotherapy accurately predicted pathologic response in 88% (95% confidence interval, 69.8-97.6) of the cases. Neither level of TP expression nor TP up-regulation correlated with pCR. Significant toxicity resulted in therapy discontinuation in 3 of 26 patients. CONCLUSIONS: DC chemotherapy exhibited a similar pCR rate compared with standard taxane regimens, with increased toxicity. TP expression was not up-regulated after docetaxel and did not correlate with therapeutic response. Core breast biopsy after neoadjuvant chemotherapy accurately predicted pathologic response.     

Invasive cancers detected after breast cancer screening yielded a negative result: relationship of mammographic density to tumor prognostic factors

PURPOSE: To evaluate common breast tumor prognostic characteristics, including estrogen receptor (ER) status, grade, size, and method of detection, in relationship to mammographic density. MATERIALS AND METHODS: The study involved 121 women who had negative results at both screening mammography and breast physical examination within 17 months before a diagnosis of breast cancer. Mammographic density was classified according to Breast Imaging Reporting and Data System patterns 1 through 4 (where 1 indicates a fatty breast and 4 indicates a dense breast). Axillary nodal status and tumor histologic ER status, histologic grade, size, stage, and method of detection (mammography alone, palpation alone, or both palpation and mammography) were analyzed by density category and tested for statistically significant differences across categories by using analysis of variance. RESULTS: Statistically significant differences (P <.05) by density category were found for the following variables: ER positivity (15 of 15 tumors in category 1 breasts, 32 of 41 tumors in category 2 breasts, 37 of 49 tumors in category 3 breasts, and eight of 16 tumors in category 4 breasts were ER positive), occurrence of grade 1 tumors (eight, 11, 19, and four tumors in category 1, category 2, category 3, and category 4 breasts, respectively, were grade 1), mean tumor size (11.3, 13.0, 14.7, and 19.7 mm for category 1, category 2, category 3, and category 4 breasts, respectively), detection with mammography alone (13, 31, 36, and four tumors in category 1, category 2, category 3, and category 4 breasts, respectively, were detected with mammography alone), and occurrence of stage I tumors (10, 25, 28, and five tumors in category 1, category 2, category 3, and category 4 breasts, respectively, were stage I). CONCLUSION: In women with negative results at clinical and mammographic screening within 17 months before breast tumor detection, subsequently diagnosed cancers tend to be ER negative, of higher grade, and larger in size in those with dense tissue patterns than in those with fat patterns.     

Combination of digital mammography with semi-automated 3D breast ultrasound

This paper describes work aimed at combining 3D ultrasound with full-field digital mammography via a semi-automatic prototype ultrasound scanning mechanism attached to the digital mammography system gantry. Initial efforts to obtain high x-ray and ultrasound image quality through a compression paddle are proving successful. Registration between the x-ray mammogram and ultrasound image volumes is quite promising when the breast is stably compressed. This prototype system takes advantage of many synergies between the co-registered digital mammography and pulse-echo ultrasound image data used for breast cancer detection and diagnosis. In addition, innovative combinations of advanced US and X-ray applications are being implemented and tested along with the basic modes. The basic and advanced applications are those that should provide relatively independent information about the breast tissues. Advanced applications include x-ray tomosynthesis, for 3D delineation of mammographic structures, and non-linear elasticity and 3D color flow imaging by ultrasound, for mechanical and physiological information unavailable from conventional, non-contrast x-ray and ultrasound imaging.     

Improvement of mammographic mass characterization using spiculation meausures and morphological features

We are developing new computer vision techniques for characterization of breast masses on mammograms. We had previously developed a characterization method based on texture features. The goal of the present work was to improve our characterization method by making use of morphological features. Toward this goal, we have developed a fully automated, three-stage segmentation method that includes clustering, active contour, and spiculation detection stages. After segmentation, morphological features describing the shape of the mass were extracted. Texture features were also extracted from a band of pixels surrounding the mass. Stepwise feature selection and linear discriminant analysis were employed in the morphological, texture, and combined feature spaces for classifier design. The classification accuracy was evaluated using the area Az under the receiver operating characteristic curve. A data set containing 249 films from 102 patients was used. When the leave-one-case-out method was applied to partition the data set into trainers and testers, the average test Az for the task of classifying the mass on a single mammographic view was 0.83 +/- 0.02, 0.84 +/- 0.02, and 0.87 +/- 0.02 in the morphological, texture, and combined feature spaces, respectively. The improvement obtained by supplementing texture features with morphological features in classification was statistically significant (p = 0.04). For classifying a mass as malignant or benign, we combined the leave-one-case-out discriminant scores from different views of a mass to obtain a summary score. In this task, the test Az value using the combined feature space was 0.91 +/- 0.02. Our results indicate that combining texture features with morphological features extracted from automatically segmented mass boundaries will be an effective approach for computer-aided characterization of mammographic masses.     

Design and evaluation of an external filter technique for exposure equalization in mammography.

We are developing an external filter method for equalizing x-ray exposure in the peripheral region of the breast. This method requires the use of only a limited number of custom-built filters for different breast shapes in a given view. This paper describes the design methodology for these external filters. The filter effectiveness was evaluated through a simulation study on 171 mediolateral and 196 craniocaudal view digitized mammograms and through imaging of a breast phantom. The degree of match between the simulated filter and the individual 3-D exposure profiles at the breast periphery was quantified. An analysis was performed to investigate the effect of filter misalignment. The simulation study indicates that the filter is effective in equalizing exposures for more than 80% of the breast images in our database. The tolerance in filter misalignment was estimated to be about +/- 2 mm for the CC view and +/- 1 mm for the MLO view at the image plane. Some misalignment artifacts were demonstrated with simulated filtered mammograms.