Aromatase inhibitor-induced modulation of breast density: clinical and genetic effects.

BACKGROUND: Change in breast density may predict outcome of women receiving adjuvant hormone therapy for breast cancer. We performed a prospective clinical trial to evaluate the impact of inherited variants in genes involved in oestrogen metabolism and signalling on change in mammographic percent density (MPD) with aromatase inhibitor (AI) therapy. METHODS: Postmenopausal women with breast cancer who were initiating adjuvant AI therapy were enrolled onto a multicentre, randomised clinical trial of exemestane vs letrozole, designed to identify associations between AI-induced change in MPD and single-nucleotide polymorphisms in candidate genes. Subjects underwent unilateral craniocaudal mammography before and following 24 months of treatment. RESULTS: Of the 503 enrolled subjects, 259 had both paired mammograms at baseline and following 24 months of treatment and evaluable DNA. We observed a statistically significant decrease in mean MPD from 17.1 to 15.1% (P<0.001), more pronounced in women with baseline MPD ≥20%. No AI-specific difference in change in MPD was identified. No significant associations between change in MPD and inherited genetic variants were observed. CONCLUSION: Subjects with higher baseline MPD had a greater average decrease in MPD with AI therapy. There does not appear to be a substantial effect of inherited variants in biologically selected candidate genes.

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.

Computerized image analysis: estimation of breast density on mammograms.

An automated image analysis tool is being developed for the estimation of mammographic breast density. This tool may be useful for risk estimation or for monitoring breast density change in prevention or intervention programs. In this preliminary study, a data set of 4-view mammograms from 65 patients was used to evaluate our approach. Breast density analysis was performed on the digitized mammograms in three stages. First, the breast region was segmented from the surrounding background by an automated breast boundary-tracking algorithm. Second, an adaptive dynamic range compression technique was applied to the breast image to reduce the range of the gray level distribution in the low frequency background and to enhance the differences in the characteristic features of the gray level histogram for breasts of different densities. Third, rule-based classification was used to classify the breast images into four classes according to the characteristic features of their gray level histogram. For each image, a gray level threshold was automatically determined to segment the dense tissue from the breast region. The area of segmented dense tissue as a percentage of the breast area was then estimated. To evaluate the performance of the algorithm, the computer segmentation results were compared to manual segmentation with interactive thresholding by five radiologists. A "true" percent dense area for each mammogram was obtained by averaging the manually segmented areas of the radiologists. We found that the histograms of 6% (8 CC and 8 MLO views) of the breast regions were misclassified by the computer, resulting in poor segmentation of the dense region. For the images with correct classification, the correlation between the computer-estimated percent dense area and the "truth" was 0.94 and 0.91, respectively, for CC and MLO views, with a mean bias of less than 2%. The mean biases of the five radiologists' visual estimates for the same images ranged from 0.1% to 11%. The results demonstrate the feasibility of estimating mammographic breast density using computer vision techniques and its potential to improve the accuracy and reproducibility of breast density estimation in comparison with the subjective visual assessment by radiologists.

Automated registration of breast lesions in temporal pairs of mammograms for interval change analysis--local affine transformation for improved localization.

Analysis of interval change is important for mammographic interpretation. The aim of this study is to evaluate the use of an automated registration technique for computer-aided interval change analysis in mammography. Previously we developed a regional registration technique for identifying masses on temporal pairs of mammograms. In the current study, we improved lesion registration by including a local alignment step. Initially, the lesion position on the prior mammogram was estimated based on the breast geometry. An initial fan-shaped search region was then defined on the prior mammogram. In the second stage, the location of the fan-shaped region on the prior mammogram was refined by warping, based on an affine transformation and simplex optimization in a local region. In the third stage, a search for the best match between the lesion template from the current mammogram and a structure on the prior mammogram was carried out within the search region. This technique was evaluated on 124 temporal pairs of mammograms containing biopsyproven masses. Eighty-seven percent of the estimated lesion locations resulted in an area overlap of at least 50% with the true lesion locations and an average distance of 2.4 +/- 2.1 mm between their centroids. The average distance between the estimated and the true centroid of the lesions on the prior mammogram over all 124 temporal pairs was 4.2 +/- 5.7 mm. The registration accuracy was improved in comparison with our previous study that used a data set of 74 temporal pairs of mammograms. This improvement in accuracy resulted from the improved geometry estimation and the local affine transformation.

Digital mammography: observer performance study of the effects of pixel size on the characterization of malignant and benign microcalcifications.

RATIONALE AND OBJECTIVES: The authors performed this study to evaluate the effects of pixel size on the characterization of mammographic microcalcifications by radiologists. MATERIALS AND METHODS: Two-view mammograms of 112 microcalcification clusters were digitized with a laser scanner at a pixel size of 35 microm. Images with pixel sizes of 70, 105, and 140 microm were derived from the 35-microm-pixel size images by averaging neighboring pixels. The malignancy or benignity of the microcalcifications had been determined with findings at biopsy or 2-year follow-up. Region-of-interest images containing the microcalcifications were printed with a laser imager. Seven radiologists participated in a receiver operating characteristic (ROC) study to estimate the likelihood of malignancy. The classification accuracy was quantified with the area under the ROC curve (Az). The statistical significance of the differences in the Az values for different pixel sizes was estimated with the Dorfman-Berbaum-Metz method and the Student paired t test. The variance components were analyzed with a bootstrap method. RESULTS: The higher-resolution images did not result in better classification; the average Az with a pixel size of 35 microm was lower than that with pixel sizes of 70 and 105 microm. The differences in Az between different pixel sizes did not achieve statistical significance. CONCLUSION: Pixel sizes in the range studied do not have a strong effect on radiologists' accuracy in the characterization of microcalcifications. The low specificity of the image features of microcalcifications and the large interobserver and intraobserver variabilities may have prevented small advantages in image resolution from being observed.

Discrimination of sonographically detected breast masses using frequency shift color Doppler imaging in combination with age and gray scale criteria.

Frequency shift color Doppler imaging was assessed in conjunction with patient age and gray scale (GS) features for discriminating benign from malignant breast masses. Thirty-eight women with sonographically detected masses scheduled for biopsy were evaluated using a 6- to 13-MHz scan head, and the masses were delineated in ultrasonographic image volumes. Vascularity in and around each mass was quantified using speed-weighted pixel density (SWD). Gray scale features were ranked visually on a linear scale. Combinations of indices were compared with histologic findings (18 benign and 20 malignant). Receiver operating characteristic analysis ranked performance in decreasing order from the SWD-Age-GS index, to SWD-GS, SWD-Age, Age-GS, GS criteria, SWD, and Age. At 100% sensitivity, SWD-Age-GS, SWD-GS, and SWD-Age discriminated benign from malignant masses with specificities of 94%, 89%, and 72%, respectively. These results indicate significant improvement in ultrasonographic discrimination of sonographically detected breast masses by combining the vascularity measure SWD with age and GS criteria.

Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization.

Mass segmentation is used as the first step in many computer-aided diagnosis (CAD) systems for classification of breast masses as malignant or benign. The goal of this paper was to study the accuracy of an automated mass segmentation method developed in our laboratory, and to investigate the effect of the segmentation stage on the overall classification accuracy. The automated segmentation method was quantitatively compared with manual segmentation by two expert radiologists (R1 and R2) using three similarity or distance measures on a data set of 100 masses. The area overlap measures between R1 and R2, the computer and R1, and the computer and R2 were 0.76 +/- 0.13, 0.74 +/- 0.11, and 0.74 +/- 0.13, respectively. The interobserver difference in these measures between the two radiologists was compared with the corresponding differences between the computer and the radiologists. Using three similarity measures and data from two radiologists, a total of six statistical tests were performed. The difference between the computer and the radiologist segmentation was significantly larger than the interobserver variability in only one test. Two sets of texture, morphological, and spiculation features, one based on the computer segmentation, and the other based on radiologist segmentation, were extracted from a data set of 249 films from 102 patients. A classifier based on stepwise feature selection and linear discriminant analysis was trained and tested using the two feature sets. The leave-one-case-out method was used for data sampling. For case-based classification, the area Az under the receiver operating characteristic (ROC) curve was 0.89 and 0.88 for the feature sets based on the radiologist segmentation and computer segmentation, respectively. The difference between the two ROC curves was not statistically significant.

Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses.

A new classification scheme was developed to classify mammographic masses as malignant and benign by using interval change information. The masses on both the current and the prior mammograms were automatically segmented using an active contour method. From each mass, 20 run length statistics (RLS) texture features, 3 speculation features, and 12 morphological features were extracted. Additionally, 20 difference RLS features were obtained by subtracting the prior RLS features from the corresponding current RLS features. The feature space consisted of the current RLS features, the difference RLS features, the current and prior speculation features, and the current and prior mass sizes. Stepwise feature selection and linear discriminant analysis classification were used to select and merge the most useful features. A leave-one-case-out resampling scheme was used to train and test the classifier using 140 temporal image pairs (85 malignant, 55 benign) obtained from 57 biopsy-proven masses (33 malignant, 24 benign) in 56 patients. An average of 10 features were selected from the 56 training subsets: 4 difference RLS features, 4 RLS features, and 1 speculation feature from the current image, and 1 speculation feature from the prior, were most often chosen. The classifier achieved an average training Az of 0.92 and a test Az of 0.88. For comparison, a classifier was trained and tested using features extracted from the 120 current single images. This classifier achieved an average training Az of 0.90 and a test Az of 0.82. The information on the prior image significantly (p = 0.015) improved the accuracy for classification of the masses.

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.

Breast reconstruction with TRAM flaps: normal and abnormal appearances at CT.

In the transverse rectus abdominis musculocutaneous (TRAM) flap procedure, a portion of the abdominal wall is transposed to the chest as a pedicle or free flap. Patients who have undergone this procedure often subsequently undergo computed tomography (CT) for assessment of metastatic disease or unrelated pathologic conditions. CT scans obtained in patients who had undergone the TRAM flap procedure were reviewed to facilitate recognition of both the normal and abnormal postoperative CT appearances of the TRAM flap. In 28 reconstructed breasts in 21 patients, three general appearances were identified: type 1 (homogeneous fat attenuation) (n = 4), type 2 (fat attenuation with a thin, curvilinear soft-tissue band parallel to the skin surface) (n = 19), and type 3 (thick soft-tissue band parallel to the skin surface) (n = 5). A mass that arose in a type 2 breast 21 months after surgery represented recurrent cancer. A markedly thickened soft-tissue band in another patient represented a dry eschar with inflammation and fat necrosis. The rectus abdominis muscle was partially absent in eight cases and completely absent in 20 cases. Recognition of the normal postoperative appearance of the body wall helps avoid confusion with disease states and allows identification of abnormal conditions such as inflammation, infection, and recurrent breast cancer.

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.

Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms.

As an ongoing effort to develop a computer aid for detection of masses on mammograms, we recently designed an object-based region-growing technique to improve mass segmentation. This segmentation method utilizes the density-weighted contrast enhancement (DWCE) filter as a pre-processing step. The DWCE filter adaptively enhances the contrast between the breast structures and the background. Object-based region growing was then applied to each of the identified structures. The region-growing technique uses gray-scale and gradient information to adjust the initial object borders and to reduce merging between adjacent or overlapping structures. Each object is then classified as a breast mass or normal tissue based on extracted morphological and texture features. In this study we evaluated the sensitivity of this combined segmentation scheme and its ability to reduce false positive (FP) detections on a data set of 253 digitized mammograms, each of which contained a biopsy-proven breast mass. It was found that the segmentation scheme detected 98% of the 253 biopsy-proven breast masses in our data set. After final FP reduction, the detection resulted in 4.2 FP per image at a 90% true positive (TP) fraction and 2.0 FPs per image at an 80% TP fraction. The combined DWCE and object-based region growing technique increased the initial detection sensitivity, reduced merging between neighboring structures, and reduced the number of FP detections in our automated breast mass detection scheme.

Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study.

PURPOSE: To evaluate the effects of computer-aided diagnosis (CAD) on radiologists' classification of malignant and benign masses seen on mammograms. MATERIALS AND METHODS: The authors previously developed an automated computer program for estimation of the relative malignancy rating of masses. In the present study, the authors conducted observer performance experiments with receiver operating characteristic (ROC) methodology to evaluate the effects of computer estimates on radiologists' confidence ratings. Six radiologists assessed biopsy-proved masses with and without CAD. Two experiments, one with a single view and the other with two views, were conducted. The classification accuracy was quantified by using the area under the ROC curve, Az. RESULTS: For the reading of 238 images, the Az value for the computer classifier was 0.92. The radiologists' Az values ranged from 0.79 to 0.92 without CAD and improved to 0.87-0.96 with CAD. For the reading of a subset of 76 paired views, the radiologists' Az values ranged from 0.88 to 0.95 without CAD and improved to 0.93-0.97 with CAD. Improvements in the reading of the two sets of images were statistically significant (P = .022 and .007, respectively). An improved positive predictive value as a function of the false-negative fraction was predicted from the improved ROC curves. CONCLUSION: CAD may be useful for assisting radiologists in classification of masses and thereby potentially help reduce unnecessary biopsies.

Extent of lumpectomy for breast cancer after diagnosis by stereotactic core versus wire localization biopsy.

BACKGROUND: Stereotactic core biopsy of mammographically defined breast abnormalities is an alternative to wire localization biopsy. The purpose of this study was to evaluate the extent of lumpectomy in patients diagnosed by stereotactic core versus wire localization biopsy. METHODS: A total of 67 consecutive patients diagnosed with invasive cancers or ductal carcinoma in situ (DCIS) were retrospectively reviewed. Thirty-four were diagnosed by core biopsy and the remaining 33 by wire localization biopsy. RESULTS: Approximately 65% of patients subsequently had breast-conserving surgical therapy. Seventy-nine percent of patients undergoing wire localization biopsies had positive surgical margins. Achievement of negative surgical margins for lumpectomies performed after wire localization or stereotactic core biopsies was 100% and 89%, respectively, which was not significantly different. However, the total volume of breast tissue removed for breast conservation in patients undergoing lumpectomy after wire localization versus core biopsies was 183 cm3 and 104 cm3, respectively, which was significantly different (P = .003). CONCLUSIONS: Diagnosis by stereotactic core biopsies resulted in less tissue removal to achieve margin-negative lumpectomies for breast conservation. Stereotactic core biopsy is the method of choice for biopsying nonpalpable, suspicious breast lesions.

Linear motion correction in three dimensions applied to dynamic gadolinium enhanced breast imaging.

Quantitative analysis of dynamic gadolinium-DTPA (diethylenetriamine pentaacetic acid) enhanced magnetic resonance imaging (MRI) is emerging as a highly sensitive tool for detecting malignant breast tissue. Three-dimensional rapid imaging techniques, such as keyhole MRI, yield high temporal sampling rates to accurately track contrast enhancement and washout in lesions over the course of multiple volume acquisitions. Patient motion during the dynamic acquisitions is a limiting factor that degrades the image quality, particularly of subsequent subtraction images used to identify and quantitatively evaluate regions suggestive of malignancy. Keyhole imaging is particularly sensitive to motion since datasets acquired over an extended period are combined in k-space. In this study, motion is modeled as set of translations in each of the three orthogonal dimensions. The specific objective of the study is to develop and implement an algorithm to correct the consequent phase shifts in k-space data prior to offline keyhole reconstruction three-dimensional (3D) volume breast MR acquisitions.

Relationship between mammographic screening intervals and size and histology of ductal carcinoma in situ.

OBJECTIVE: The objective of this study was to determine how the length of time between mammographic screenings is related to the size, grade, and histology of mammographically detected ductal carcinoma in situ (DCIS). MATERIALS AND METHODS: We retrospectively reviewed 166 consecutive mammograms of women evaluated for DCIS with (n = 24) and without (n = 142) microinvasion. The size of the DCIS was determined by the maximum diameter as measured on the mammogram. After pathologic analysis, DCIS was classified by histologic architecture, nuclear grade, presence of microinvasion, and presence of multifocality. Four screening intervals were defined: annual (6-17 months), biennial (18-29 months), triennial (> or = 30 months), and first time. Patients were grouped according to screening intervals. The average age of all groups was 55 years. RESULTS: The annual group (mean size of DCIS, 1.69 cm) had significantly smaller DCIS than did the biennial (mean size, 2.27 cm), triennial (mean size, 3.49 cm), or first time groups (mean size, 3.29 cm) (p = .003). Comedo histology was more frequently observed in patients screened biennially (73.7%) than in those screened annually (46.8%) (p = .05). High-grade nuclear histology was more commonly seen in the biennial (76.3%) than in the annual (48.1%) screening group (p = .008). We found no significant correlation between screening interval and the incidence of microinvasion and multifocality. CONCLUSION: Small, low-grade noncomedo DCIS was more common in the annual mammographic screening group than in the biennial screening group. A direct relationship was found between DCIS size and length of screening interval: DCIS detected at annual screening was smaller than that found at biennial screening, which in turn was smaller than DCIS revealed at triennial screening. This study provides inferential support for annual screening mammography for DCIS detection and management.

Mammographic appearance of recurrent breast carcinoma in six patients with TRAM flap breast reconstructions.

PURPOSE: To determine the mammographic appearance of locally recurrent cancer in patients with breast reconstructions with transverse rectus abdominis musculocutaneous (TRAM) flaps after mastectomy for primary breast cancer. MATERIALS AND METHODS: The mammograms and records of women treated for breast cancer with mastectomy and TRAM flap reconstruction who developed local recurrences from 1995 to 1997 were reviewed retrospectively. Eight cancers were identified in six women. Five women had palpable abnormalities, and the sixth had cancer detected at screening mammography at another institution. Mean age at recurrence was 48 years. RESULTS: All eight cancers were visible on mammograms: four masses, two pleomorphic microcalcifications, and two masses with calcifications. Four of the eight were in the upper central portion of the breast. Before reconstruction, the original histologic diagnosis for all cases had been multifocal ductal carcinoma in situ. All recurrences were invasive cancer. Median time from the original diagnosis of breast cancer to diagnosis of recurrence was 42 months. Two of four patients who subsequently underwent axillary node dissection had metastatic disease in the lymph nodes. The single patient who underwent mammographic screening (elsewhere) had negative axillary lymph nodes. CONCLUSION: The mammographic appearance of recurrent carcinoma in TRAM flap reconstructions is similar to that of primary breast cancer.

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces.

We are developing computerized feature extraction and classification methods to analyze malignant and benign microcalcifications on digitized mammograms. Morphological features that described the size, contrast, and shape of microcalcifications and their variations within a cluster were designed to characterize microcalcifications segmented from the mammographic background. Texture features were derived from the spatial gray-level dependence (SGLD) matrices constructed at multiple distances and directions from tissue regions containing microcalcifications. A genetic algorithm (GA) based feature selection technique was used to select the best feature subset from the multi-dimensional feature spaces. The GA-based method was compared to the commonly used feature selection method based on the stepwise linear discriminant analysis (LDA) procedure. Linear discriminant classifiers using the selected features as input predictor variables were formulated for the classification task. The discriminant scores output from the classifiers were analyzed by receiver operating characteristic (ROC) methodology and the classification accuracy was quantified by the area, Az, under the ROC curve. We analyzed a data set of 145 mammographic microcalcification clusters in this study. It was found that the feature subsets selected by the GA-based method are comparable to or slightly better than those selected by the stepwise LDA method. The texture features (Az = 0.84) were more effective than morphological features (Az = 0.79) in distinguishing malignant and benign microcalcifications. The highest classification accuracy (Az = 0.89) was obtained in the combined texture and morphological feature space. The improvement was statistically significant in comparison to classification in either the morphological (p = 0.002) or the texture (p = 0.04) feature space alone. The classifier using the best feature subset from the combined feature space and an appropriate decision threshold could correctly identify 35% of the benign clusters without missing a malignant cluster. When the average discriminant score from all views of the same cluster was used for classification, the Az value increased to 0.93 and the classifier could identify 50% of the benign clusters at 100% sensitivity for malignancy. Alternatively, if the minimum discriminant score from all views of the same cluster was used, the Az value would be 0.90 and a specificity of 32% would be obtained at 100% sensitivity. The results of this study indicate the potential of using combined morphological and texture features for computer-aided classification of microcalcifications.

Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis.

A genetic algorithm (GA) based feature selection method was developed for the design of high-sensitivity classifiers, which were tailored to yield high sensitivity with high specificity. The fitness function of the GA was based on the receiver operating characteristic (ROC) partial area index, which is defined as the average specificity above a given sensitivity threshold. The designed GA evolved towards the selection of feature combinations which yielded high specificity in the high-sensitivity region of the ROC curve, regardless of the performance at low sensitivity. This is a desirable quality of a classifier used for breast lesion characterization, since the focus in breast lesion characterization is to diagnose correctly as many benign lesions as possible without missing malignancies. The high-sensitivity classifier, formulated as the Fisher's linear discriminant using GA-selected feature variables, was employed to classify 255 biopsy-proven mammographic masses as malignant or benign. The mammograms were digitized at a pixel size of 0.1 mm x 0.1 mm, and regions of interest (ROIs) containing the biopsied masses were extracted by an experienced radiologist. A recently developed image transformation technique, referred to as the rubber-band straightening transform, was applied to the ROIs. Texture features extracted from the spatial grey-level dependence and run-length statistics matrices of the transformed ROIs were used to distinguish malignant and benign masses. The classification accuracy of the high-sensitivity classifier was compared with that of linear discriminant analysis with stepwise feature selection (LDAsfs). With proper GA training, the ROC partial area of the high-sensitivity classifier above a true-positive fraction of 0.95 was significantly larger than that of LDAsfs, although the latter provided a higher total area (Az) under the ROC curve. By setting an appropriate decision threshold, the high-sensitivity classifier and LDAsfs correctly identified 61% and 34% of the benign masses respectively without missing any malignant masses. Our results show that the choice of the feature selection technique is important in computer-aided diagnosis, and that the GA may be a useful tool for designing classifiers for lesion characterization.