Computer-aided preoperative diagnosis of microcalcifications on mammograms

PURPOSE: To evaluate of a computer-aided method for differentiating malignant from benign clustered microcalcifications. MATERIAL AND METHODS: Our material was 350 suspicious microcalcifications on mammograms from 330 female patients who underwent breast biopsy (after hook wire localization and under mammographic guidance). The histologic findings were malignant in 140 cases (40%) and benign in 210 cases (60%). Those clusters were manually detected, computer-aided analyzed and quantitatively estimated. Besides computer analysis, 3 physicians-observers (2 radiologists and 1 breast surgeon) evaluated the malignant or benign nature of the clustered microcalcifications. The performance of the artificial network, each observer and the three observers as a group was evaluated by receiver operating characteristics (ROC) curves. RESULTS: Comparison of the ROC curves revealed the following AUC values (area under the curve): computer - 0.950, physician 1 - 0.815, physician 2 - 0.830, physician 3 - 0.830, and physicians as a group - 0.825. The results, compared by the student t-test for paired data, showed a statistically significant difference between computer analysis and physicians' performance, independently and as a group. CONCLUSION: Our study showed that computer analysis achieved statistically significantly better performance than that of physicians in the classification of malignant and benign calcifications.     

Comparison of independent double readings and computer-aided diagnosis (CAD) for the diagnosis of breast calcifications

RATIONALE AND OBJECTIVES: The aim of the study is to compare independent double readings by radiologists and computer-aided diagnosis (CAD) in diagnostic interpretation of mammographic calcifications. MATERIALS AND METHODS: Ten radiologists independently interpreted 104 mammograms containing clustered microcalcifications. Forty-six of these were malignant and 58 were benign at biopsy. Radiologists read the images with and without a computer aid by using a counterbalanced study design. Sensitivity and specificity were calculated from observer biopsy recommendations, and receiver operating characteristic (ROC) curves were computed from their diagnostic confidence ratings. Unaided double-reading sensitivity and specificity values were derived post hoc by using three different objective rules and an additional rule of simulated-optimal double reading that assumed that consultations for resolving two radiologists' different independent diagnoses always produce the correct clinical recommendation. ROC curves of unaided double readings were obtained according to the literature. RESULTS: Single reading without computer aid yielded 74% sensitivity and 32% specificity, whereas CAD reading yielded 87% sensitivity and 42% specificity and appeared on a higher ROC curve (P < .0001). Three methods of formulating independent double readings generated sensitivities between 59% and 89%, specificities between 50% and 13%, and operating points that moved essentially along the average unaided single-reading ROC curve. ROC curves of unaided independent double readings showed small, statistically insignificant improvement over those of unaided single readings. Results of the simulated-optimal double reading were similar to CAD: 89% sensitivity and 50% specificity. CONCLUSION: Independent double readings of mammographic calcifications may not improve diagnostic performance. CAD reading improves diagnostic performance to an extent approaching the maximum possible performance.     

Can the size of microcalcifications predict malignancy of clusters at mammography?

RATIONALE AND OBJECTIVES: The purpose of this study was to determine whether the size of mammographically detected microcalcifications is predictive of malignancy. MATERIALS AND METHODS: Two hundred sixty mammograms showing clustered microcalcifications with proven diagnoses (160 malignant, 100 benign) were respectively reviewed by experienced mammographers. Lesions that were obviously benign in appearance were excluded from the study. A computer-aided diagnosis system digitized the lesions at 600 dpi, and the microcalcifications on the digital image were interactively defined by mammographers. Subsequently, three quantitative features that reflected the size of the microcalcifications-length, area, and brightness-were automatically extracted by the system. For each feature, the standard average of values obtained for individual calcifications within the cluster and the average with emphasis on extreme values (E) obtained in a single cluster were analyzed and matched with pathologic results. RESULTS: In the malignant group of cases, the mean values of the standard average length and area were significantly higher (P < .0001) than the mean values in the benign group. Distribution analysis demonstrated that an average length of more than 0.41 mm was associated with malignant lesions 77% of the time, while an average length of less than 0.41 mm was associated with benign lesions 71% of the time. The mean of the average length (E) and area (E) of microcalcifications within the cluster demonstrated an even higher discriminative power when compared with the standard average length and area. The average brightness, on the other hand, showed only a low discriminative power. CONCLUSION: Digital computerized analysis of mammographically detected calcifications demonstrated that the average length and area of the calcifications in benign clusters were significantly smaller than those in malignant clusters.     

Optimizing parameters for computer-aided diagnosis of microcalcifications at mammography

RATIONALE AND OBJECTIVES: The purpose of this study was to optimize selection of the mammographic features most useful in discriminating benign from malignant clustered microcalcifications. MATERIALS AND METHODS: The computer-aided diagnosis (CAD) system automatically extracted from digitized mammograms 13 quantitative features characterizing microcalcification clusters. Archival cases (n = 134; patient age range, 31-77 years; mean age, 56.8 years) with known histopathologic results (79 malignant, 55 benign) were selected. Three radiologists at three facilities independently analyzed the microcalcifications by using the CAD system. Stepwise discriminant analysis selected the features best discriminating benign from malignant microcalcifications. A classification scheme was constructed on the basis of these optimized features, and its performance was evaluated by using receiver operating characteristic (ROC) analysis. RESULTS: Six of the 13 variables extracted by the CAD system were selected by stepwise determinant analysis for generating the classification scheme, which yielded an ROC curve with an area (Az) of 0.98, specificity of 83.64%, positive predictive value of 89.53%, and accuracy of 91.79% for 98% sensitivity. When patient age was an additional variable, the scheme's performance improved, but this was not statistically significant (Az = 0.98). The ROC curve of the classifier (without age as an additional variable) yielded a high Az of 0.96 for patients younger than 50 years and an even higher (P < .02) Az of 0.99 for those 50 years or older. CONCLUSION: Stepwise discriminant analysis optimized performance of a classification scheme for microcalcifications by selecting six optimized features. Scheme performance was significantly (P < .02) higher for women 50 years or older, but the addition of patient age as a variable did not produce a statistically significant increase in performance.     

Differentiation of benign and malignant breast microcalcifications: mammography versus mammography-sonography combination

PURPOSE: The aim of this study was to determine the diagnostic contribution of high-frequency sonography in the diagnosis of isolated clustered microcalcifications detected by mammography. MATERIAL AND METHODS: 238 women (aged 3198) with isolated clustered microcalcifications were examined by mammography and subsequently by high frequency sonography (13 MHz) at the site of microcalcification. 170 underwent surgery. 94 were affected by cancer and 76 by benign pathology. The other 68 were considered to have benign microcalcifications after three years follow-up. The ROC statistical technique was employed to compare the diagnostic role of mammography alone versus the combination of mammography and sonography. The area under the ROC curves was calculated by the Wilcoxon method, without any hypothesis on the distribution of the statistical data. RESULTS: The microcalcifications were neoplastic in 39.5% of cases. The difference between the areas under the mammography ROC curve (area = 0.807, standard error = 0.03) and the mammography-sonography ROC curve (area = 0.819, standard error = 0.028) was not statistically significant (p > 0.05). CONCLUSIONS: The higher sensitivity of mammography-sonography combination demonstrates that it may be useful to perform sonography following mammography when mammography yields a diagnosis of non malignant pathology.     

Breast US in patients who had microcalcifications with low concern of malignancy on screening mammography

PURPOSE: To review ultrasound (US) findings in patients who have suspicious microcalcifications with low concern of malignancy (BI-RADS category 4A) on screening mammography and to evaluate helpful findings in differentiating benign and malignant lesions. MATERIALS AND METHODS: Between August 2005 and July 2006, 192 patients showed microcalcifications only, without mass or associated density, on screening mammography. Among them, we selected 82 patients who had microcalcifications with low concern of malignancy (category 4A) that were pathologically confirmed by surgical excision after wire localization (n=23) or biopsy (n=59). Breast US was performed in 37/82 cases and we analyzed the US findings for the calcification areas in these patients, evaluating the findings with benign or malignant pathological results. We correlated US findings with mammographic calcifications using mammography-guided 2D-localization for the calcifications before US examination. RESULTS: There were 12 malignant lesions (32.4%) including 3 invasive ductal carcinomas (IDC), one microinvasive ductal carcinoma (MIDC), 8 ductal carcinoma in situ (DCIS) and 25 benign lesions (67.6%) including 2 atypical ductal hyperplasias (ADH). IDC showed calcifications within heterogeneous hypoechoic parenchyma or calcifications within complex hypoechoic masses of taller-than-wide shape on US. One MIDC showed calcifications within heterogeneous hypoechoic parenchyma and six DCIS showed negative findings, or calcifications with a small nodule, or only calcifications on US. The most common positive US finding in benign lesions was cysts with calcifications. In 24/37 cases (64.8%) with negative US findings, 18 (75%) were benign lesions and 6 (25%) were DCIS. CONCLUSION: In patients with category 4A microcalcifications without associated findings on screening mammography, negative US findings had a high rate of benign results (18/24, 75%). Visible calcifications within heterogeneous hypoechoic parenchyma or mass on US increased the probability of malignancy.     

Mammographic and histologic correlations of microcalcifications

The majority of microcalcifications found on mammograms are associated with benign disease; however, some types accompany malignant disease. By correlating histologic with mammographic findings, the radiologist may gain an understanding of the morphologic characteristics and distribution of microcalcifications. We present radiologic and histologic images from a series of cases of nonpalpable, clustered microcalcifications. Such microcalcifications can be divided into two basic histologic groups: lobular and ductal. Although rounded, similarly shaped lobular calcifications can be differentiated mammographically from ductal calcifications with their irregular margins and varying size and shape, both types can be associated with benign and malignant processes. Biopsy is usually needed to confirm the diagnosis when clustered microcalcifications are found at mammography.     

Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses

RATIONALE AND OBJECTIVE: To compare logistic regression and artificial neural network for computer-aided diagnosis on breast sonograms. MATERIALS AND METHODS: Ultrasound images of 24 malignant and 30 benign masses were analyzed quantitatively for margin sharpness, margin echogenicity, and angular variation in margin. These features and age of patients were used with two pattern classifiers, logistic regression, and an artificial neural network to differentiate between malignant and benign masses. The performance of two methods was compared by receiver operating characteristic (ROC) analysis. RESULTS: The area under the ROC curve Az (+/-SD) of the logistic regression analysis was 0.853 +/- 0.059 with 95% confidence limit (0.760-0.950). The area under the ROC curve of the artificial neural network analysis was 0.856 +/- 0.058 with 95% confidence limit (0.734-0.936). Although both the logistic regression and the artificial neural network had the same area under the ROC curve, the shapes of two curves were different. At 95% sensitivity, the artificial neural network had 76.5% specificity, whereas logistic regression had 64.7% specificity. CONCLUSION: There was no difference in performance between logistic regression and the artificial neural network as measured by the area under the ROC curve. However, at a fixed 95% sensitivity, the artificial neural network had higher (12%) specificity compared with logistic regression value.     

Critical review of 215 cases of non-palpable mammary lesions]

PURPOSE: The natural history of human breast cancer shows that lesion size correlates directly with nodal metastases and distant spread. Nodal metastases are found in only 6% of cases in the preclinical stage of the tumor and therefore imaging must detect a breast cancer before it becomes palpable. We reviewed 215 nonpalpable breast lesions studied in the last 10 years to assess observers performance and ultimately improve the interpretation of suspicious mammograms, evaluating "cost" in terms of the ratio between benign and malignant lesions (B/M). MATERIAL AND METHODS: From 1988 to October 1998, two hundred and fifteen women with nonpalpable breast lesions suspected at mammography were examined. The lesions were removed after stereotaxic or US location and a radiograph of the surgical specimen was always performed. Mammographic patterns were interpreted retrospectively by two blinded radiologist experienced in breast imaging and specialized in locating nonpalpable breast lesions. Mammographic patterns were classified as poorly/highly suspicious calcifications, regular/irregular masses, spiculated masses, masses with calcifications and parenchymal distortions. Radiographic findings were compared with surgical results and the data used to calculate the B/M, positive predictive value (PPV) for malignancy and the trend of operator's performance. RESULTS: Modern techniques permit to detect a very high number of in situ breast carcinomas. Nineteen of 22 lesions (86%) were detected by mammography as highly suspicious calcifications, 2/22 as spiculated masses and 1/22 as a mass with calcifications. No in situ carcinoma was detected as an irregular mass. All regular masses were proven to be benign at histology. B/M analysis showed a decreasing trend (from 1.94 in the first 3 years to .57 in 1994-96, to .83 in 1997-98) and an overall value of .90. The PPV for malignancy was 83.33% for spiculated masses, 65.5% for highly suspicious calcifications, 63.63% for irregular masses, 47.05% for masses with more or less dysmorphic calcifications, 32.65% for poorly suspicious calcifications, 8.33% for parenchymal distortions and 0% for regular masses. DISCUSSION AND CONCLUSIONS: All spiculated masses and highly suspicious calcifications and microcalcifications should be removed. Biopsy is recommended in parenchymal distortions, despite its low predictive value for malignancy, because these lesions are uncommon and the cost of biopsy is therefore acceptable. Needle aspiration or long-term monitoring can be reconsidered for irregular masses and poorly suspicious microcalcifications. Finally, relative to possible different interpretations of mammographic patterns by center and operator's experience, we suggest that the PPV for every single pattern be continually reassessed based on personal case records rather than on literature data. This holds true especially for microcalcifications.     

Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms

RATIONALE AND OBJECTIVES: To evaluate whether a computer-aided diagnosis (CADx) technique can accurately classify breast calcifications in full-field digital mammograms (FFDMs) as malignant or benign. The computer technique was developed previously on screen-film mammograms (SFMs) in which individual calcifications were identified manually. The present study evaluated the computer technique independently on a new database of FFDM images with automatic detection of the individual calcifications. MATERIALS AND METHODS: We analyzed 49 consecutive FFDM cases (19 cancers) that showed suspicious calcifications. Four mammography radiologists read soft-copy mammograms retrospectively and electronically indicated the region of calcifications in each image. The computer then automatically detected the individual calcifications within the indicated region and analyzed eight features of calcification morphology and distribution to arrive at an estimated likelihood of malignancy. The radiologists entered Breast Imaging Report and Data System assessments before and after seeing the computer results. Performance was analyzed using receiver operating characteristic analysis. RESULTS: Despite variability in radiologist-indicated regions of calcifications, the computer achieved consistently high performance taking input from the four radiologists (receiver operating characteristic curve area, A(z): 0.80, 0.80, 0.78, and 0.77; differences not statistically significant). Previous results showed that the computer technique achieved an A(z) value of 0.80 on SFMs, which improved radiologists' performance significantly. CONCLUSIONS: The computer technique appears to maintain consistently high performance in classifying calcifications in FFDMs as malignant or benign without requiring substantial modification from its initial development on SFMs. The computer performance appears to be robust with respect to variations in radiologists' input.     

Computer-assisted analysis of mammographic clustered calcifications

A total of 127 biopsy-proven clustered mammographic calcifications were diagnosed and evaluated using multiple parameters. The results were subjected to computer analysis. The number of calcifications/cm2 was the most important parameter. Less than 10 calcifications/cm2 resulted in an 82% chance of being benign while 10 or more calcifications/cm2 had a 44% chance of being malignant. The average distance between the calcifications in the cluster was also significant, with a 92% chance of being benign if greater than 1 mm and a 52% chance of being malignant if less than 1 mm. The remaining parameters did not yield statistically significant results, but there were trends which suggest that less than 10 clustered calcifications that are regular in size and shape and orderly in appearance tend to be benign while disorderly clusters tend to be malignant. In order to decrease the number of false positive biopsies and improve the cost-effectiveness of screening mammography, radiologists should consider 6-month follow-up mammography as a reasonable alternative for clustered calcifications that appear benign.     

DIMA enlargement mammography in microcalcifications: a prospective study with ROC analysis

The purpose of this study was to investigate whether the four-fold magnification mammography (direct magnification, DIMA) technique would perform better than conventional 1.5-fold magnification mammography in the differentiation of breast microcalcifications into benign and malignant. Fifty patients with non-palpable microcalcifications detected by mammography were examined immediately prior to surgical biopsy using both a conventional (1.5-fold) and the DIMA (fourfold) magnification mammography techniques. The microcalcifications were classified by five experienced radiologists using morphological criteria. A receiver operating characteristics curve (ROC) analysis of the sensitivity and specificity of both techniques in assessing malignancy was then carried out. The DIMA mammography technique was slightly but non-significantly superior to the conventional method in detecting malignancy (p > 0.05). Coarse granular and pleomorphic calcifications were detected more frequently with the DIMA technique. Coarse calcifications were significantly more frequently associated with histologically benign findings, whereas fine granular calcifications were significantly more likely to be malignant lesions. Assessment of malignancy associated with microcalcifications using morphological criteria is not significantly improved by mammography techniques with higher magnification.     

Lobular carcinoma in situ: mammographic-pathologic correlation of results of needle-directed biopsy

The mammographic and histologic findings were reviewed in 41 consecutive cases of isolated lobular carcinoma in situ (LCIS) unassociated with any malignant diagnosis. Thirty-one needle-directed breast biopsies were performed to evaluate clustered microcalcifications. In 24 of the 31 cases, the calcifications were found in areas of benign breast disease, with LCIS representing a separate process. In the few cases in which microcalcifications were seen in association with LCIS, a greater number of similar calcifications were present in adjacent benign disease. Soft-tissue abnormalities necessitating the performance of a biopsy represented benign foci, except in one patient with LCIS in and adjacent to a fibroadenoma. The authors conclude that LCIS has no characteristic mammographic features. LCIS is detected as an incidental finding at breast biopsy, with the mammographic abnormality predominantly reflecting a benign process.     

Radiologist detection of microcalcifications with and without computer-aided detection: a comparative study

AIM: To compare the sensitivity and specificity of microcalcification detection by radiologists alone and assisted by a computer-aided detection (CAD) system. MATERIALS AND METHODS: Films of 106 patients were masked, randomized, digitized and analysed by the CAD-system. Five readers interpreted the original mammograms and were blinded to demographics, medical history and earlier films. Forty-two mammograms with malignant microcalcifications, 40 with benign microcalcifications and 24 normal mammograms were included. Results were recorded on a standardized image interpretation form. The mammograms with suspicious areas flagged by the CAD-system were displayed on mini-monitors and immediately re-reviewed. The interpretation was again recorded on a new copy of the standard form and classified according to six groups. RESULTS: Forty-one out of 42 (98%) malignant microcalcifications and 32 of 40 (80%) benign microcalcifications were flagged by the CAD-system. There was an average of 1.2 markers per image. The sensitivity for malignant microcalcifications detection by mammographers without and with the CAD-system ranged from 81% to 98% and from 88% to 98%, respectively. The mean difference without and with CAD-system was 2.2% (range 0-7%). CONCLUSION: No statistically significant changes in sensitivity were found when experienced mammographers were assisted by the CAD-system, with no significant compromise in specificity.     

Computerized evaluation of mammographic lesions: what diagnostic role does the shape of the individual microcalcifications play compared with the geometry of the cluster?

OBJECTIVE: The objective of this study was to compare the diagnostic role of features reflecting the geometry of clusters with features reflecting the shape of the individual microcalcification in a mammographic computer-aided diagnosis system. MATERIALS AND METHODS: Three hundred twenty-four cases of clustered microcalcifications with biopsy-proven results were digitized at 42-microm resolution and analyzed on a computerized system. The shape factor and number of neighbors were computed for each microcalcification, and the eccentricity of the cluster was computed as well. The shape factor is related to the individual microcalcification; the average number of neighbors and the cluster eccentricity reflect the cluster geometry. Stepwise discriminant analysis was used to evaluate the contribution of the extracted features in predicting malignancy. The performance of a classifier based on the features selected by stepwise discriminant analysis was evaluated by receiver operating characteristic (ROC) analysis. RESULTS: To obtain the best discrimination model, we used stepwise discriminant analysis to select the average number of neighbors and the shape of the individual microcalcification, but excluded cluster eccentricity. A classification scheme assigned the average number of neighbors a weighting factor, which was 1.49 times greater than that assigned to the shape factor of the individual microcalcification. A scheme based only on these two features yielded an ROC curve with an area under the curve (A(z)) of 0.87, indicating a positive predictive value of 61% for 98% sensitivity. CONCLUSION: Computerized analysis permitted calculations reflecting the shape of individual microcalcification and the geometry of clusters of microcalcifications. For the computerized classification scheme studied, the cluster geometry was more effective in differentiating benign from malignant clusters than was the shape of individual microcalcification.     

Clustering of breast microcalcifications: revisited

AIM: To verify the diagnostic value of the traditional definition of 'clustering' of microcalcifications (more than five in the area of 1 cm(2)or 1 cm(3)) on mammography in the differential diagnosis of benign and malignant breast disease.METHODS AND MATERIALS: Three radiologists without knowledge of the final pathology retrospectively counted the number of microcalcifications per 0.25 cm(2) (0.5 x 0.5 cm) unit area on mammography in 57 pathologically proven non-palpable lesions including 26 cancers and 31 benign diseases. Pleomorphism of the microcalcifications, associated architectural distortion or mass or increased density and distribution of microcalcifications were also evaluated.RESULTS: The mean numbers of microcalcifications per 0.25 cm(2) were 16.4 in malignant and 16.7 in benign diseases (no statistically significant difference between the two groups). Pleomorphism of the microcalcifications, associated architectural distortion or mass or increased density were, however, important determining parameters. Clustering was more frequently observed in benign diseases. CONCLUSION: In this study, the mean number of microcalcifications per unit area is much larger than the traditional definition of 'clustering' and clustering itself is not effective in the differential diagnosis of benign and malignant breast lesions. Imaging features other than numbers of calcification per unit area are more important in assessing the significance of mammographic clustered microcalcifications.     

Differences between computer-aided diagnosis of breast masses and that of calcifications

PURPOSE: To compare the performance of a computer-aided diagnosis (CAD) system for diagnosis of previously detected lesions, based on radiologist-extracted findings on masses and calcifications. MATERIALS AND METHODS: A feed-forward, back-propagation artificial neural network (BP-ANN) was trained in a round-robin (leave-one-out) manner to predict biopsy outcome from mammographic findings (according to the Breast Imaging Reporting and Data System) and patient age. The BP-ANN was trained by using a large (>1,000 cases) heterogeneous data set containing masses and microcalcifications. The performances of the BP-ANN on masses and microcalcifications were compared with use of receiver operating characteristic analysis and a z test for uncorrelated samples. RESULTS: The BP-ANN performed significantly better on masses than microcalcifications in terms of both the area under the receiver operating characteristic curve and the partial receiver operating characteristic area index. A similar difference in performance was observed with a second model (linear discriminant analysis) and also with a second data set from a similar institution. CONCLUSION: Masses and calcifications should be considered separately when evaluating CAD systems for breast cancer diagnosis.     

Rapidly destructive osteoarthritis of the hip: MR imaging findings

OBJECTIVE: The purpose of our study was to use an artificial neural network to differentiate benign from malignant pulmonary nodules on high-resolution CT findings and to evaluate the effect of artificial neural network output on the performance of radiologists using receiver operating characteristic analysis. MATERIALS AND METHODS: We selected 155 cases with pulmonary nodules less than 3 cm (99 malignant nodules and 56 benign nodules). An artificial neural network was used to distinguish benign from malignant nodules on the basis of seven clinical parameters and 16 radiologic findings that were extracted by attending radiologists using subjective rating scales. In the observer test, 12 radiologists (four attending radiologists, four radiology fellows, and four radiology residents) were presented with high-resolution CT images, first without and then with the artificial neural network output. Observer performance was evaluated by means of receiver operating characteristic analysis using a continuous rating scale. RESULTS: The artificial neural network showed a high performance in differentiating benign from malignant pulmonary nodules (A(z) = 0.951). The average A(z) value for all radiologists increased by a statistically significant level, from 0.831 to 0.959, with the use of the artificial neural network output. CONCLUSION: Our computerized scheme using the artificial neural network can improve the diagnostic accuracy of radiologists who are differentiating benign from malignant pulmonary nodules on high-resolution CT.     

Sedimented calcium in benign breast cysts: the full spectrum of mammographic presentations

We retrospectively reviewed the mammograms of 318 patients that showed sedimented calcifications within benign breast cysts to describe the natural history and full spectrum of the mammographic appearances. Sedimented calcifications are seen in approximately 4% of symptomatic women undergoing mammography. Their recognition is important to avoid unnecessary workup, follow-up, or biopsy. Key to recognition is the difference in their radiographic features on lateral and craniocaudal views. The classic appearance is that of milk of calcium, seen as linear, curvilinear, or teacup-shaped particles on horizontal-beam lateral views and as ill-defined smudges on vertical-beam craniocaudal views. The most common presentation is multiple, bilateral, scattered and occasionally clustered calcifications within microcysts. Other presentations include milk of calcium within microcysts in a unilateral, clustered distribution; milk of calcium within macrocysts; sandlike calcifications (discrete particles rather than smudges on craniocaudal view) within cysts of various sizes; and rarely, milk of calcium within the lipid cysts of either fat necrosis or galactoceles. None of our cases has proved to be malignant. However, adjacent malignancies are a potential pitfall. We encountered eight patients with carcinoma presenting as clustered microcalcifications in a breast also containing typical sedimented calcifications. In each of these cases, the malignant calcifications could be distinguished by their mammographic appearance. The recognition of sedimented calcifications present in about 4% of symptomatic women undergoing mammography is important because these characteristic calcifications are an indication of benignity. Malignant-appearing microcalcifications found in the vicinity of sedimented calcifications can be distinguished and require biopsy.