Nonpalpable breast lesions: accuracy of prebiopsy mammographic diagnosis

Characterization of mammographic masses based on level set segmentation with new image features and patient information

Computer-aided diagnosis (CAD) for characterization of mammographic masses as malignant or benign has the potential to assist radiologists in reducing the biopsy rate without increasing false negatives. The purpose of this study was to develop an automated method for mammographic mass segmentation and explore new image based features in combination with patient information in order to improve the performance of mass characterization. The authors' previous CAD system, which used the active contour segmentation, and morphological, textural, and spiculation features, has achieved promising results in mass characterization. The new CAD system is based on the level set method and includes two new types of image features related to the presence of microcalcifications with the mass and abruptness of the mass margin, and patient age. A linear discriminant analysis (LDA) classifier with stepwise feature selection was used to merge the extracted features into a classification score. The classification accuracy was evaluated using the area under the receiver operating characteristic curve. The authors' primary data set consisted of 427 biopsy-proven masses (200 malignant and 227 benign) in 909 regions of interest (ROIs) (451 malignant and 458 benign) from multiple mammographic views. Leave-one-case-out resampling was used for training and testing. The new CAD system based on the level set segmentation and the new mammographic feature space achieved a view-based Az value of 0.83 +/- 0.01. The improvement compared to the previous CAD system was statistically significant (p = 0.02). When patient age was included in the new CAD system, view-based and case-based Az values were 0.85 +/- 0.01 and 0.87 +/- 0.02, respectively. The study also demonstrated the consistency of the newly developed CAD system by evaluating the statistics of the weights of the LDA classifiers in leave-one-case-out classification. Finally, an independent test on the publicly available digital database for screening mammography with 132 benign and 197 malignant ROIs containing masses achieved a view-based Az value of 0.84 +/- 0.02.

Computer-Aided Detection and Diagnosis of Breast Cancer

The use of computer-aided detection (CAD) with film or digital mammography is now widely regarded as the standard of practice in mammography and has been shown to increase the rate of breast cancer detection. There are inherent limitations in 2D mammography, and new technologies involving 2D and 3D imaging with X-rays, ultrasound, and MRI are in use or under investigation. CAD can aid in the reduction of oversight error for these modalities and has the potential to assist the physician in unifying the interpretation across alternative modalities. We believe the result will be improved sensitivity and specificity due to both improved detection and diagnosis.

Use of border information in the classification of mammographic masses

We are developing a new method to characterize the margin of a mammographic mass lesion to improve the classification of benign and malignant masses. Towards this goal, we designed features that measure the degree of sharpness and microlobulation of mass margins. We calculated these features in a border region of the mass defined as a thin band along the mass contour. The importance of these features in the classification of benign and malignant masses was studied in relation to existing features used for mammographic mass detection. Features were divided into three groups, each representing a different mass segment: the interior region of a mass, the border and the outer area. The interior and the outer area of a mass were characterized using contrast and spiculation measures. Classification was done in two steps. First, features representing each of the three mass segments were merged into a neural network classifier resulting in a single regional classification score for each segment. Secondly, a classifier combined the three single scores into a final output to discriminate between benign and malignant lesions. We compared the classification performance of each regional classifier and the combined classifier on a data set of 1076 biopsy proved masses (590 malignant and 486 benign) from 481 women included in the Digital Database for Screening Mammography. Receiver operating characteristic (ROC) analysis was used to evaluate the accuracy of the classifiers. The area under the ROC curve (A(z)) was 0.69 for the interior mass segment, 0.76 for the border segment and 0.75 for the outer mass segment. The performance of the combined classifier was 0.81 for image-based and 0.83 for case-based evaluation. These results show that the combination of information from different mass segments is an effective approach for computer-aided characterization of mammographic masses. An advantage of this approach is that it allows the assessment of the contribution of regions rather than individual features. Results suggest that the border and the outer areas contained the most valuable information for discrimination between benign and malignant masses.

Simulation of breast cancer screening: quality assessment of two protocols

Healthcare professionals must make breast cancer screening decisions without the help of clear answers in current medical knowledge. This study used computer simulation to evaluate two screening protocols. The American Cancer Society (ACS) protocol comprising self-breast examination, professional breast examination and annual mammography was evaluated versus annual mammography alone. The effective frequency of mammography and the cost in the ACS protocol doubles the cost of mammography alone. Breast self-examination and clinical breast examination contributes to increased cost without any added health effects. These study results could be applied by healthcare professionals to assist their decision making for breast cancer screening.

Classification of mammographic masses using generalized dynamic fuzzy neural networks

In this article, computer-aided classification of mammographic masses using generalized dynamic fuzzy neural networks (GDFNN) is presented. The texture parameters, derived from first-order gradient distribution and gray-level co-occurrence matrices, were computed from the regions of interest. A total of 343 images containing 180 benign masses and 163 malignant masses from the Digital Database for Screening Mammography were analyzed. A fast approach of automatically generating fuzzy rules from training samples was implemented to classify tumors. This work is novel in that it alleviates the problem of requiring a designer to examine all the input-output relationships of a training database in order to obtain the most appropriate structure for the classifier in a conventional computer-aided diagnosis. In this approach, not only the connection weights can be adjusted, but also the structure can be self-adaptive during the learning process. By virtue of the automatic generation of the classifier by the GDFNN learning algorithm, the area under the receiver-operating characteristic curve, Az, attains 0.868 +/- 0.020, which corresponds to a true-positive fraction of 95.0% at a false positive fraction of 52.8%. The corresponding accuracy is 70.0%, the positive predictive value is 62.0%, and the negative predictive value is 91.4%.

Computerized characterization of breast masses on three-dimensional ultrasound volumes

We are developing computer vision techniques for the characterization of breast masses as malignant or benign on radiologic examinations. In this study, we investigated the computerized characterization of breast masses on three-dimensional (3-D) ultrasound (US) volumetric images. We developed 2-D and 3-D active contour models for automated segmentation of the mass volumes. The effect of the initialization method of the active contour on the robustness of the iterative segmentation method was studied by varying the contour used for its initialization. For a given segmentation, texture and morphological features were automatically extracted from the segmented masses and their margins. Stepwise discriminant analysis with the leave-one-out method was used to select effective features for the classification task and to combine these features into a malignancy score. The classification accuracy was evaluated using the area Az under the receiver operating characteristic (ROC) curve, as well as the partial area index Az(0.9), defined as the relative area under the ROC curve above a sensitivity threshold of 0.9. For the purpose of comparison with the computer classifier, four experienced breast radiologists provided malignancy ratings for the 3-D US masses. Our dataset consisted of 3-D US volumes of 102 biopsied masses (46 benign, 56 malignant). The classifiers based on 2-D and 3-D segmentation methods achieved test Az values of 0.87+/-0.03 and 0.92+/-0.03, respectively. The difference in the Az values of the two computer classifiers did not achieve statistical significance. The Az values of the four radiologists ranged between 0.84 and 0.92. The difference between the computer's Az value and that of any of the four radiologists did not achieve statistical significance either. However, the computer's Az(0.9) value was significantly higher than that of three of the four radiologists. Our results indicate that an automated and effective computer classifier can be designed for differentiating malignant and benign breast masses on 3-D US volumes. The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists.

A computer model for the study of breast cancer

A computer model was designed as a relational database to assess breast cancer screening in a cohort of women where the growth and development of breast cancer originates with the first malignant cell. The concepts of thresholds for growth, axillary spread, and distant sites are integrated. With tumor diagnosis, staging was performed that includes clinical and sub-clinical states. The model was parameterized to have staging characteristics similar to data published by the Surveillance, Epidemiology, and End-Results (SEER) Program. Validation was accomplished by comparing simulated staging results with non-SEER sources, and simulated survival with independent clinical survival data.

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.

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 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.

The cost-effectiveness of fine-needle aspiration cytology and 14-gauge core needle biopsy compared with open surgical biopsy in the diagnosis of breast carcinoma

BACKGROUND

Judicious utilization of fine-needle aspiration cytology (FNAC) and 14-gauge core needle biopsy (CB) theoretically should result in greater accuracy in breast carcinoma diagnosis and fewer unnecessary open surgical biopsies (OSBs), thus lowering health care costs.

METHODS

In 1995 in Rochester, New York, the ratio of open surgical breast biopsies per each verified breast carcinoma (OSB/Ca) in a freestanding breast clinic (EWBC) was compared with the OSB/Ca ratio of all physicians in the remainder of the city. The EWBC differs from all other diagnostic facilities in Rochester in that it routinely performs FNAC and CB.

RESULTS

The EWBC recommended 462 OSBs resulting in 310 verified carcinomas, for a OSB/Ca ratio of 1.5. The physicians in the remainder of the city recommended 2036 OSBs resulting in 513 verified carcinomas, for a OSB/Ca ratio of 4.0. If the EWBC OSB/Ca ratio had been identical to the remainder of the city, the number of extra OSBs recommended by the clinic would have been 778, resulting in an additional cost of $1,712,082. When the added cost of the 2594 FNACs ($256,285) and 403 CBs ($252,278) performed by the clinic was subtracted from the $1,712,082, the freestanding breast clinic cost savings was $1,203,519. The lymph node metastasis rate of 19% for the breast carcinomas diagnosed in clinic patients was identical to that of the women with breast carcinoma in the remainder of the city.

CONCLUSIONS

Utilization of FNAC and CB allows radiologists to lower their OSB/Ca ratio without sacrificing early detection. In this study, these less expensive procedures result in lowered medical costs for the health care system.

Screening mammography behavior after a false positive mammogram

This pilot study describes women's interpretations of the experience of a false positive mammogram followed by a negative biopsy and the impact of this experience on subsequent participation in screening mammography. A 25-min, open-ended telephone interview was administered in 1992 to 30 women over age 39 who had negative biopsies in 1987 preceded by abnormal mammograms. Almost twice as many women reported getting regular mammograms after the biopsy (60%) as did before 1987 (33%). Most received their next mammogram after the biopsy within the recommended interval (73%), and those getting regular mammograms prior to the biopsy experience were more likely than those who did not have a prior habit of undergoing mammography to continue to get them afterwards. These preliminary findings suggest that a negative breast biopsy after a positive mammogram does not reduce a patient's likelihood of undergoing screening in the future. In fact, it may serve as an impetus for increased compliance with screening recommendations.

Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis

A new rubber band straightening transform (RBST) is introduced for characterization of mammographic masses as malignant or benign. The RBST transforms a band of pixels surrounding a segmented mass onto the Cartesian plane (the RBST image). The border of a mammographic mass appears approximately as a horizontal line, and possible speculations resemble vertical lines in the RBST image. In this study, the effectiveness of a set of directional textures extracted from the images before the RBST. A database of 168 mammograms containing biopsy-proven malignant and benign breast masses was digitized at a pixel size of 100 microns x 100 microns. Regions of interest (ROIs) containing the biopsied mass were extracted from each mammogram by an experienced radiologist. A clustering algorithm was employed for automated segmentation of each ROI into a mass object and background tissue. Texture features extracted from spatial gray-level dependence matrices and run-length statistics matrices were evaluated for three different regions and representations: (i) the entire ROI; (ii) a band of pixels surrounding the segmented mass object in the ROI; and (iii) the RBST image. Linear discriminant analysis was used for classification, and receiver operating characteristic (ROC) analysis was used to evaluate the classification accuracy. Using the ROC curves as the performance measure, features extracted from the RBST images were found to be significantly more effective than those extracted from the original images. Features extracted from the RBST images yielded an area (Az) of 0.94 under the ROC curve for classification of mammographic masses as malignant and benign.

The role of (99m)Tc methoxyisobutylisonitrile in imaging breast cancer

Multiple imaging modalities have been used in evaluating patients suspected of breast cancer. Mammography and ultrasound are the dominant modalities now used by most mammographers. Mammography has been shown to be inaccurate in patients with dense breast or in breasts where significant architectural distortion has occurred. Alternative modalities independent of breast density are currently being evaluated. These include ultrasonography, magnetic resonance imaging, and nuclear medicine techniques. Recent reports indicate methoxyisobutylisonitrile (MIBI) to be promising in the evaluation of patients with difficult to evaluate mammograms. The current sensitivity and specificity figures for MIBI scintigraphy of the breast are dependent on a number of factors. A recent multicenter trial involving 673 patients sponsored by the DuPont-Merck Radiopharmaceutical Corporation indicated an overall sensitivity of 85% with a specificity of 81%. Nonpalpable lesions showed a sensitivity of 55% to 72%. The acceptance of MIBI breast imaging will be dependent on specific applications in which MIBI can be shown to be superior to alternative modalities. A promising subgroup of patients includes patients with dense breasts, breasts with architectural distortions, or extensive scaring from prior biopsies, especially if these patients are considered to be in a high risk category.

Open versus stereotactic breast biopsy

BACKGROUND

Stereotactic breast biopsy has been developed as a less invasive means of performing biopsy for mammographic abnormalities.

METHODS

From July 1994 through June 1995, 103 women with mammographic abnormalities requiring biopsy were prospectively evaluated.

RESULTS

Fifty-one women had open biopsy, and 52 women had stereotactic biopsy. The average age in both groups was 60 years. Pathology revealed malignancy in 12% of stereotactic biopsies and 13% of open biopsies. Complications occurred in 6% of the open biopsies and 4% of the stereotactic biopsies and were limited to hematomas or seromas. The average cost was $2400 for open biopsy and $650 for stereotactic biopsy (P < 0.01). One hundred and one patients returned for a follow-up mammogram within 6 months, and 1 patient in each group required a second biopsy, which revealed benign pathology. A Patient Satisfaction Survey revealed no significant differences in patient satisfaction between the two types of procedures.

CONCLUSION

There were no differences between open and stereotactic biopsies in regards to diagnostic accuracy, complications, or patient satisfaction. A significant difference was noted in charges during the time frame of our study.

Technetium-99m tetrofosmin scintigraphy in the diagnosis of breast cancer

The routine mammogram remains the procedure of choice for screening asymptomatic women for breast cancer. However, it accounts for an elevated percentage of misdiagnosed breast nodules particularly in younger women with dense breast tissue or fibrocystic disease. New radiopharmaceuticals including labelled monoclonal antibodies, thalium-201 and technetium-99m sestamibi are under investigation as possible solutions to this issue. 99mTc-tetrofosmin (Myoview), like 99mTc-sestamibi, is a lipophilic, cationic myocardial imaging agent. Mitochondrial membrane potential appears to play a significant role in the uptake and/or retention of 99mTc-tetrofosmin. Twenty-seven female patients with suspected breast cancer were included in the study. Twenty mCi of 99mTc-tetrofosmin were administered intravenously. Imaging was begun 10 min post-injection. In twenty-three patients, breast cancer was detected using 99mTc-tetrofosmin scintigraphy. Two carcinomas were missed. In two patients, both breast scintigraphy and intraoperative biopsies were negative. Our preliminary results suggest that this product may have a role in the differentiation of benign from malignant lesions, and were encouraging in terms of its potential usefulness as a marker of malignancy.

Scintimammography with technetium-99m methoxyisobutylisonitrile: comparison with mammography and magnetic resonance imaging

The aim of the study was to compare the diagnostic accuracy of scintimammography with technetium-99m methoxyisobutylisonitrile (MIBI; SMM) in the detection of primary breast cancer with that of mammography (MM) and magnetic resonance imaging (MRI). Fifty-six patients with suspected lesions detected by palpation or MM were included in the study. Within the 4 weeks preceding excisional biopsy, MM and MRI were performed in all patients. Between 5 and 10 min after the injection of 740 MBq 99mTc-MIBI, SMM in the prone position was performed. In the total group of 56 patients, 43 lesions were palpable, while 13 were non-palpable but were detected by MM. Breast cancer was confirmed by histopathology in 27 of the patients (22 palpable and 5 non-palpable carcinomas). The tumour size ranged from 6 to 80 mm in diameter. For non-palpable lesions, the sensitivity of SMM, MM and MRI was 60%, 60% and 100%, respectively, while the specificity was 75%, 25% and 50%, respectively. For palpable breast lesions, all methods showed high sensitivity (SMM 91%, MM 95%, MRI 91%) but SMM demonstrated significantly higher specificity (SMM 62%, MM 10%, MRI 15%). In two mammographically negative tumours (dense tissue), SMM showed a positive result. In comparison to MRI, one additional carcinoma could be diagnosed by SMM. It may be concluded that for palpable breast lesions, the diagnostic accuracy of SMM is superior to that of MM and MRI. Through the complementary use of SMM it is possible to increase the sensitivity for the detection of breast cancer and multicentric disease. In patients in whom the status of a palpable breast mass remains unclear, SMM may help to reduce the amount of unnecessary biopsies.

The positive predictive value of mammographic signs: a review of 425 non-palpable breast lesions

The sensitivity of mammography in cancer detection needs to be high but is also important to achieve a high diagnostic specificity to avoid the morbidity associated with unnecessary surgical biopsy. We have reviewed the mammographic features of non-palpable breast lesions to identify factors which may improve the specificity of mammographic interpretation and reduce the number of open surgical biopsies for benign lesions. Four hundred and twenty-five surgical biopsies of non-palpable breast lesions were performed following image-guided localization between January 1987 and April 1994. The mammographic features of these lesions were reviewed and correlated with their histology. Two hundred and twenty-five of the excised lesions were malignant and 200 were benign giving a benign to malignant ratio of 0.88:1 and a positive predictive value (PPV) for malignancy of 53%. Pre-operative fine needle aspiration cytology was performed in 359 cases (84%). The PPV for the various mammographic abnormalities following the assessment process ranged from 4% for well defined masses to 94% for spiculate masses. The PPV for all microcalcifications was 45%. For impalpable lesions it is our policy to recommend surgical excision of all spiculate masses, parenchymal deformities and high risk microcalcifications. Ill defined masses are managed according to fine needle aspiration cytology (FNAC) and/or core biopsy results. Masses which are entirely well-defined are regarded as benign and are not recalled for assessment unless they are new or enlarging. Needle core biopsy has been added to our preoperative assessment of mammographically indeterminate microcalcifications with the aim of reducing the number of benign surgical biopsies.

The use of thallium-201 in the preoperative detection of breast cancer: an adjunct to mammography and ultrasonography

Thallium-201 breast scans were performed preoperatively in 72 female patients with breast abnormalities detected by mammography and/or ultrasonography (7.5-13 MHz), in order to differentiate benign from malignant breast disease. Informed consent was obtained from each patient. Scintigraphy consisted of anterior and oblique planar images of the affected breast and axilla at 10 min and 3 h following the injection of 201Tl chloride (110 MBq). All 201Tl scans were interpreted without prior knowledge of surgery data. Pathological features of breast malignancies, such as tumour size, axillary lymph node metastases, tumour grading, lymphatic vascular channel invasion and receptor status, were analysed for their association with 201Tl uptake by tumour cells. A total of 76 breast lesions were assessed in the study. On final histological diagnosis, there were 56 malignant tumours, 14 benign nodules (9 fibroadenomas, two cases of adenosis, two cases of focal fibrosis and one case of epitheliosis) and six atypical lesions (atypical ductal or lobular hyperplasia). Thallium scintigraphy was shown to have high accuracy (92%) in detecting breast cancer, better than mammography (74%) and ultrasonography (84%). Almost all (51/56) breast cancers showed greater 201Tl activity than surrounding normal breast tissue while there was no significant increase in 201Tl activity above background in all but one (19/20) case of non-malignant disease. 201Tl activity within breast tumours, calculated as tumour/background (T/B) ratio, ranged between 1.2 and 2.5 with a mean value of 1.45. In our experience the concentration of thallium in the breast cancer seems to be primarily dependent on vascularity and tumour size rather than tumour grading, lymphatic/vascular invasion or receptor status. 201Tl scan sensitivity was 97% for malignant lesions larger than 1.5 cm (n = 35) and 80% for lesions of 1.5 cm or less (n = 21); however, five of the eight breast cancers smaller than 1.0 cm were also detectable by 201Tl scintigraphy, compared with five out of seven by mammography. Thallium scintigraphy would not be useful in evaluating the axilla for lymph node metastases (sensitivity 27%, specificity 77%).

Prospective evaluation of a composite scoring system for mammographically detected cytologically assessed impalpable breast abnormalities

The aim of all breast screening programmes is a reduction in deaths from the disease. It is hoped that this can be achieved with minimal intervention in the patient cohort who do not have cancer. In this study we have assessed the efficacy of a combination of mammographic and cytological scoring systems in the evaluation of 208 women with screen-detected non-palpable breast lesions. All mammograms were scored 1-5 and those with a score of 3 or more required needle localization. A cytology score of 1 to 5 was generated for each patient according to a similar system and an aggregate score for each patient was achieved by the addition of the mammographic and cytology score. Ninety-three of the 208 patients had malignancy--the positive predictive value for mammography alone in this series was 45%. All 58 patients who had an aggregate score of 8 or greater had cancer. The 60 patients who had an aggregate score of 4 had benign disease on excision biopsy. We suggest that a 'wait and repeat mammogram' approach is appropriate in patients with grade 3 mammography and benign (grade 1) cytology. Patients with an intermediate aggregate score of 5-7 should have an excision biopsy and those with an aggregate score of 8 could have definitive surgery rather than excision biopsy. Application of this aggregate scoring technique would enhance the delivery of more appropriate surgery to a majority of patients with screen-detected breast abnormalities.

Dynamic MR mammography: a technique for potentially reducing the biopsy rate for benign breast disease

Thirty-five women with a total of 39 breast lesions detected at mammography and/or physical examination and who were scheduled for surgical biopsy were studied with dynamic magnetic resonance (MR) mammography. Fat-suppressed, spoiled GRASS (gradient-recalled acquisition in the steady state) sequences were performed before and after administration of gadopentetate dimeglumine. T1-weighted spin-echo and fat-suppressed T2-weighted fast spin-echo images were also obtained. By measuring percentage signal intensity change in lesions on the spoiled GRASS images after contrast agent injection, the authors achieved a sensitivity of 100% and a specificity of 83.3% in differentiating benign from malignant lesions (24 benign, 15 malignant), with biopsy results as the standard. This dynamic MR mammography technique deserves further evaluation.

Preoperative localization of breast lesions: tailored techniques and potential pitfalls

Preoperative needle localization procedures can be performed using a variety of devices and tailored approaches. Success in these endeavors can be optimized through communication and coordination among the radiologist, surgeon, and pathologist. Meticulous attention to performance and radiological-pathological correlation are required to ensure the best possible results.

Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions

To determine the likelihood of malignant disease for mammographically detected, nonpalpable breast lesions, we defined 11 morphologic categories and retrospectively reviewed the mammograms in 859 cases in which biopsy was performed after a wire localization procedure at our institution during 1989 and 1990. Within each category, the total number of lesions and the percentage of cases with a surgical pathologic diagnosis of malignant involvement were as follows: benign calcification, 25 (0% malignant); indeterminate calcification, 200 (22%); malignant calcification, 39 (92%); smooth mass, 84 (1%); irregular mass, 337 (40%); architectural distortion, 45 (47%); asymmetric breast tissue, 37 (3%, or 1 case of asymmetrically prominent ducts); smooth mass with calcification, 3 (0%); irregular mass with calcification, 68 (66%); architectural distortion with calcification, 14 (57%); and asymmetric breast tissue with calcification, 7 (29%). The overall rate of malignant involvement for the 859 cases was 34%. If follow-up examinations rather than biopsies had been done for the lesions categorized as benign calcification, smooth mass, smooth mass with calcification, and asymmetric breast tissue (excluding asymmetrically prominent ducts), the overall positive predictive value would have increased from 34 to 41%, and 148 biopsies would have been deferred (17% of all biopsies). If morphologic criteria are applied to the evaluation of mammographically detected, nonpalpable lesions, the rate of malignant disease at biopsy may reach 40%. This rate correlates with that in recent large series.