Number Of Microcalcifications Research Articles

Detection of microcalcifications using nonlinear beamforming techniques.

Abundant research demonstrates that early detection of cancer leads to improved patient prognoses. By detecting cancer earlier, when tumors are in their primary stages, treatment can be applied before metastases have occurred. The presence of microcalcifications (MCs) is indicative of malignancy in the breast, i.e., 30-50% of all nonpalpable breast cancers detected using mammograms are based on identifying the presence of MCs. Therefore, improving the ability to detect MCs with modern imaging technology remains an important goal. Specifically, improving the sensitivity of ultrasound imaging techniques to detect MCs in the breast will provide an important role for the early detection and diagnosis of breast cancer. In this work, a novel nonlinear beamforming technology for ultrasonic arrays is investigated for its ability to detect MCs. The beamforming technique, called null subtraction imaging (NSI), utilizes nulls in the beam pattern to create images using ultrasound. NSI provides improved lateral resolution, a reduction in side lobes, and an accentuation of bright singular targets. Therefore, it was hypothesized that the use of NSI would result in identification of more MCs in rat tumors having a speckle background. To test this hypothesis, rats with tumors were injected with Hydroxyapatite (HA) particles to mimic MCs. Ultrasound was used to scan the rat tumors and images were constructed using conventional delay and sum and using NSI beamforming. Three readers with experience in diagnostic ultrasound imaging examined the 1,344 images and scored the presence or absence of MCs. In all, 336 different tumor image slices were recorded and each reconstructed using NSI or conventional delay and sum with Hann apodization. In every image where one or MCs were detected in the Hann reconstructions, MCs were detected in the NSI images. In nine rat tumor images, one or more MCs were detected in the NSI images but not in the Hann images. Statistically, the results did support the hypothesis that NSI would increase the number of MCs detected in the rat tumors.

Value of Molybdenum Target X-Ray and High-Frequency Color Doppler Flow Imaging in Early Diagnosis of Breast Carcinoma: A Comparative Analysis.

Breast carcinoma (BC) threatens the physical and mental health of women worldwide, and early diagnosis is important for improving patient outcomes and ensuring successful treatment. This research mainly aims to compare and analyze the value of molybdenum target X-ray and high-frequency color Doppler flow imaging (CDFI) in the early diagnosis of BC. First, 102 patients with suspected early-stage BC (ESBC) admitted to Henan Provincial People's Hospital were examined by molybdenum target X-ray and CDFI. Based on the pathological findings, the diagnostic efficiency data of the two diagnostic modalities such as positive detection rate (PDR), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SEN), specificity (SPE), and accuracy (ACC), as well as imaging information like masses, microcalcifications (MCs), axillary lymph node (LN) metastases, and blood flow signal or vascular sign abnormalities were analyzed. CDFI contributed to higher PDR, PRV, NPV, SEN, and ACC than molybdenum target X-ray in ESBC diagnosis, but similar SPE. The combined diagnosis of molybdenum target X-ray plus CDFI contributed to even higher PDR, PRV, NPV, SEN, and ACC than molybdenum target X-ray alone and higher ACC than CDFI. Imaging inspection revealed that the number of cases of masses, axillary LN metastases, and abnormalities in blood flow signals or vascular signs detected by CDFI was significantly higher than that by molybdenum target X-ray, while the number of MCs was significantly lower. Molybdenum target X-ray plus CDFI is more effective in the diagnosis of ESBC and plays a complementary role in imaging examination, which can synergistically improve the diagnostic ACC of ESBC and is worthy of clinical promotion.

Clinical Utility of MicroPure US Imaging for Breast Microcalcifications.

PurposeTo evaluate the performance of MicroPure US imaging to detect and characterize microcalcifications.Materials and MethodsA total of 171 lesions with suspicious microcalcifications seen on mammography and B-mode US were included and simultaneously evaluated using MicroPure US imaging. The size of microcalcifications was divided into small (punctate, amorphous, fine pleomorphic, and fine linear) and large (coarse heterogeneous), and the extent was divided into narrow (grouped) and wide (others). MicroPure US imaging visibility was divided into four types based on the number of microcalcifications on the two images: B > M (more on B-mode), B = M (similar), B < M (more on MicroPure), and negative. Triple pairwise comparison was used to evaluate the imaging features according to the MicroPure US imaging visibility.ResultsAmong the 171 lesions examined, 157 lesions (91.8%) were detected by MicroPure US imaging. The proportion of Breast Imaging Reporting and Data System (BI-RADS) category 4A was significantly higher in the MicroPure positive group, and that of category 4B was significantly higher in the MicroPure negative group (p = 0.035). The other imaging features did not differ. Among the positive MicroPure subgroups, all features showed no significant difference.ConclusionMicroPure US imaging demonstrated 91.8% positivity in detecting microcalcifications on B-mode US. MicroPure US imaging visibility correlated with the BI-RADS category of microcalcifications.

Abstract NG15: Investigation of relationships between breast cancer risk factors and bilateral mammographic breast density asymmetry among women undergoing diagnostic image-guided breast biopsies

Introduction: The etiology of breast cancer remains an area of ongoing investigation. Improving our understanding of factors associated with breast cancer development will strengthen the utility of risk prediction strategies and improve risk stratification. Mammographic breast density remains one of the most influential breast cancer risk factors, with 4 to 6 fold elevated risk observed among women with the highest compared to the lowest levels. These associations have consistently been observed irrespective of the method (visual or automated), used to quantify breast density, showing the robustness of the associations. An understudied breast cancer risk factor is the bilateral asymmetry of mammographic features, with prior studies suggesting that women with higher levels of breast asymmetry may be at elevated breast cancer risk. Increasingly, studies are recognizing the potential of bilateral breast asymmetry defined by mammographic breast density in determining breast cancer risk. Recently, Eriksson and colleagues highlighted a strong influence of breast asymmetry within a model they developed to predict short-term breast cancer risk among women attending breast screening. They observed that the contribution to the model of asymmetry in mammographic breast density, microcalcifications, and masses between the breasts was as substantial as the total number of microcalcifications and masses found within a mammogram, indicating that differences between the breasts may be an important risk marker. To help further understand associations between breast cancer risk factors and asymmetry of breast density, particularly among women at elevated risk for breast cancer, we evaluated risk factor relationships with the bilateral asymmetry of volumetric measures of global and local breast density across the spectrum of premalignant and invasive breast cancer diagnoses. Methods: This study included 882 women enrolled as part of the National Cancer Institute9s Breast Radiology Evaluation and Study of Tissues (BREAST)-Stamp Project (2007-2010). The BREAST-Stamp Project is a cross-sectional molecular epidemiologic study that aims to understand how novel breast density measures are related to breast cancer etiology. Women were enrolled if they were referred for diagnostic image-guided breast biopsy following an abnormal mammogram at the University of Vermont Medical Center, and had not previously been diagnosed or treated for cancer, undergone breast surgery within one year or received chemoprevention. Risk factor data were collected at study enrolment via interview and self-administered questionnaires. Breast density measures were estimated using Single X-ray Absorptiometry (SXA), a technique in which an SXA breast density phantom was affixed to the compression paddle of the mammography machine during routine mammography so that it was included in the X-ray field. It served as a reference standard to estimate volumetric breast density. Breast density was assessed using pre-biopsy craniocaudal full-field digital mammograms of both the ipsilateral (affected) and contralateral (unaffected) breast. Firstly, global density from each laterality was determined as percent fibroglandular volume (%FGV). Secondly, localized volumetric density measures were estimated following identification of the biopsy site on the ipsilateral pre-biopsy mammogram by the study radiologist and identifying the corresponding site on the contralateral mammogram. The SXA estimated %FGV in a perilesional volume twice the size of, but excluding, the biopsy target, centered at the biopsy site. Previous estimates of reproducibility for the SXA test phantoms demonstrated a repeatability SD of 2%, with a ±2% accuracy for the entire thickness and density ranges. Breast density asymmetry was defined as an absolute bilateral difference in measures when subtracting the breast density measures of the contralateral breast from the ipsilateral measures. Spearman9s correlations (rho) examined associations between breast density measures of the left and right breasts. To determine relationships between breast cancer risk factors (defined as categorical variables) and measures of bilateral breast density asymmetry, analysis of covariance (ANCOVA) models (PROC GLM) were used. Analyses were conducted at the per woman level using SAS. Probability values of Citation Format: Maeve Mullooly, Shaoqi Fan, Ruth M. Pfeiffer, Brian Sprague, Pamela M. Vacek, Donald L. Weaver, John A. Shepherd, Amir Pasha Mahmoudzadeh, Jeff Wang, Serghei Malkov, Jason M. Johnson, Sally D. Herschorn, Gretchen L. Gierach. Investigation of relationships between breast cancer risk factors and bilateral mammographic breast density asymmetry among women undergoing diagnostic image-guided breast biopsies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr NG15.

Pancreatic Fibrosis and Chronic Pancreatitis: Mini-Review of Non-Histologic Diagnosis for Clinical Applications.

Pancreatic fibrosis is the dominant reversible pathological change and diagnostic factor in early chronic pancreatitis, defined by a mechanistic approach proposed in 2016. Main guidelines for chronic pancreatitis were published by the American Pancreas Association in 2014, the Japanese Society of Gastroenterology in 2015, and United European Gastroenterology in 2017. All three sets of guidelines mentioned that the staging of chronic pancreatitis is important but challenging. There are various image modalities for the non-histologic diagnosis of pancreatic fibrosis: (1) shear wave elastography, such as an acoustic radiation force impulse with a cut-off value of 1.4 m/s; (2) strain elastography using grades of strain; (3) endoscopic ultrasonography using the Rosemont criteria or endoscopic ultrasound criteria for early chronic pancreatitis proposed by the Japan Pancreas Society; (4) computed tomography using the Hounsfield scale or number of micro-calcifications; and (5) magnetic resonance imaging using the apparent diffusion coefficient and the T1w flash and T2w HASTE sequences. The clinical applications are to (1) evaluate pancreatic tumors and inflammatory disease; (2) monitor dyspepsia with early chronic pancreatitis; (3) monitor individuals with a high risk of pancreatic cancer; (4) analyze a fatty pancreas with fibrosis; (5) predict a fistula after pancreatic surgery; and (6) predict outcomes for chronic pancreatitis or pancreatic cancer. The selection of tools will be dependent on the clinical scenario. Conclusion: There are various modalities for the non-histologic diagnosis of pancreatic fibrosis. The selection of the optimal device will be dependent on the clinical scenario.

Tree-based modelling for the classification of mammographic benign and malignant micro-calcification clusters

Computer Aided Detection (CAD) systems are being developed to assist radiologists in diagnosis. For breast cancer the emphasis is shifting from detection to classification of abnormalities. The presented work concentrates on the benign versus malignant classification of micro-calcification clusters, which are a specific type of mammographic abnormality associated with the early development of breast cancer. After segmentation (automatic or manual), tree-based representations were used to distinguish between benign and malignant clusters, which takes into account clinical criteria such as the number of micro-calcifications in the clusters and their distribution and is based on the topology of the trees and the connectivity of the micro-calcifications. The idea of using tree structure based on the distance of individual calcifications for the classification of benign and malignant micro-calcification clusters is novel and closely related to clinical perception. Tree structures used in this study are distinct from decision trees classifiers being used in many machine learning approaches. Initial evaluation on the Digital Database for Screening Mammography (DDSM) data shows promising results, with an accuracy equal to 91 %, which is comparable to state of the art CAD systems and is in line with clinical perception of the morphology and appearance of benign and malignant micro-calcification clusters.

Abstract P2-06-06: A clinical model for assessment of the individual breast cancer risk

Abstract Background. Most mammography screening programs are not individualized. To efficiently screen for breast cancer the individual risk of the disease should be determined. We describe a model that estimate the 2-year risk of breast cancer and could be used at most mammography screening units without adding substantial cost. Methods. The study was based on the population based prospective Karma cohort including 70,877 participants. Mammograms were collected up to three years following baseline mammogram. A prediction protocol was developed using mammographic features (density, microcalcifications and masses), use of hormone replacement therapy (HRT), family history of breast cancer, menopausal status, age, and body mass index. Relative risks were calculated using conditional logistic regression. Absolute risks were calculated using the iCARE protocol. Results. Comparing women at highest and lowest mammographic density yielded a 5-fold higher risk of breast cancer for women at highest density. When adding microcalcifications and masses to the model, high-risk women had a nearly 9-fold higher risk of breast cancer compared to those at lowest risk. The difference in microcalcifications and masses between left and right breast was a better predictor of breast cancer than number of microcalcifications and masses in the breasts. When calculating the absolute 2-year risk of breast cancer we stratified women using the NICE guidelines for 10-year risk divided by 5 (Table 1). The mean absolute 2-year risk of breast cancer in the different risk categories was 0.12%, 0.33%, 0.83% and 1.95% for women at low, moderate, general, and high risk. In most countries with established mammography screening programs approximately 5 women in a 1000 are diagnosed with breast cancer at regular screening. We managed to identify a low risk group of approximately 10% of all women where 1 woman in a 1000 will be diagnosed with breast cancer, contrasting the 2% of all women at highest risk where 20 women out of a 1000 will be diagnosed with breast cancer within 2 years of last negative screen (Table 1). In the full model, taking HRT use, family history of breast cancer and menopausal status into consideration, area under the curve (AUC) reached 0.71. Conclusions. Our model includes three mammographic features that could easily be derived from clinically available software. By adding information on some few established risk factors it is possible to improve clinical care by identifying women in need of additional examination procedures. At the same time there is a substantial proportion of women that will have very little benefit from mammography screening due to their low risk of breast cancer. Table 1. Absolute 2-year risk of breast cancer in women stratified in to risk categories based on the NICE guidelinesAbsolute 2-year risk (risk group)Percent women at riskMean absolute 2-year risk2Stratified 2-year risk0-0.15 (low)10.30.121.0 (reference)0.15-&amp;lt;0.6 (general)64.80.332.750.6-&amp;lt;1.6 (moderate)22.90.826.83≥1.6 (high)2.01.9516.2 Citation Format: Eriksson M, Czene K, Pawitan Y, Leifland K, Darabi H, Hall P. A clinical model for assessment of the individual breast cancer risk [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P2-06-06.

A Micro CT Study in Patients with Breast Microcalcifications Using a Mathematical Algorithm to Assess 3D Structure.

PurposeThe aim of this study was to evaluate the relevance of the three-dimensional (3D) structure of breast microcalcifications (MC) as a predictor of malignancy using highly resolved micro-computed tomography (micro-CT) datasets of biopsy samples.Material and MethodsThe study included 28 women with suspicious MC in their mammogram undergoing vacuum-assisted biopsy. Directly after the intervention, the specimens were scanned in a micro-CT with an isometric spatial resolution of 9 μm. Datasets were analysed regarding the number, volume and morphology of suspicious non-monomorphic MC (fl—fine linear, fp—fine pleomorphic, ch—coarse heterogeneous) and the structure model index (SMI). Histological evaluation was performed according to the B-classification: normal tissue or benign (group A: B1, B2), unclear malignant potential or suspicious of malignancy (group B: B3, B4) and malignant lesions (group C: B5).ResultsIn all groups, suspicious non-monomorphic MC were found: group A exhibited fp MC in 38.5% of samples, no fl/ch; group B: fl 14.3%, fp 28.6%, ch 14.3%; group C always had at least one type of suspicious non-monomorphic MC (fl (57.1%) or fp (57.1%)) in each sample. The different histologic groups showed a similar mean SMI (benign: 2.97 ± 0.31, malignant: 3.02 ± 0.10, unclear: 2.90 ± 0.28). Between the three groups, no significant differences were found regarding number, volume or SMI value of MC.Conclusion3D structure based on the SMI of MC analysed with highest spatial resolution is not significantly associated with the B-classification of breast lesions. Thus, magnification views of MC may be omitted in the analysis of MC detected in mammograms.

Computational growth model of breast microcalcification clusters in simulated mammographic environments

BackgroundWhen screening for breast cancer, the radiological interpretation of mammograms is a difficult task, particularly when classifying precancerous growth such as microcalcifications (MCs). Biophysical modeling of benign vs. malignant growth of MCs in simulated mammographic backgrounds may improve characterization of these structures MethodsA mathematical model based on crystal growth rules for calcium oxide (benign) and hydroxyapatite (malignant) was used in conjunction with simulated mammographic backgrounds, which were generated by fractional Brownian motion of varying roughness and quantified by the Hurst exponent to mimic tissue of varying density. Simulated MC clusters were compared by fractal dimension, average circularity of individual MCs, average number of MCs per cluster, and average cluster area. ResultsBenign and malignant clusters were distinguishable by average circularity, average number of MCs per cluster, and average cluster area with p<0.01 across all Hurst exponent values considered. Clusters were distinguishable by fractal dimension with p<0.05 in low Hurst exponent environments. As the Hurst exponent increased (tissue density increased) benign and malignant MCs became indistinguishable by fractal dimension. ConclusionsThe fractal dimension of MCs changes with breast tissue density, which suggests tissue environment plays a role in regulating MC growth. Benign and malignant MCs are distinguishable in all types of tissue by shape, size, and area, which is consistent with findings in the literature. These results may help to better understand the effects of the tissue environment on tumor progression, and improve classification of MCs in mammograms via computer-aided diagnosis.

The interior structure of breast microcalcifications assessed with micro computed tomography

Morphology microcalcification descriptors help stratify the risk of breast malignancy. Micro CT is feasible for visualization of the fine-structure of tissues and may be suitible for high resolution microcalcification analysis. To analyze the interior structure of microcalcifications using a micro CT imaging system. Core needle breast biopsy specimens from 16 women with clustered microcalcifications were examined with micro CT. The samples measured between 0.8 to 1.2 mm in diameter. Micro CT scans with an isotropic voxel size of 8.4 µm were obtained to generate two- and three-dimensional images of the microcalcifications. The number of microcalcifications were counted on the magnified specimen radiogram and on micro CT. Attenuation values of microcalcifications were measured by drawing a region of interest (ROI) in the center of the microcalcifications. Two blinded observers assessed the morphology and the interior structure of microcalcifications. Five patients had benign and 11 patients had malignant breast lesions. On micro CT, microcalcifications of benign tissue showed a coarse lamellar or trabecular interior structure, whereas microcalcifications of breast cancer tissue showed a more uniform granular interior structure. There was no correlation between attenuation values of benign compared with malignant microcalcifications. On micro CT, an interior structure of microcalcifications is detectable. Benign and malignant microcalcifications display different patterns of interior structure.

Comparaison entre le zoom numérique et l’agrandissement géométrique dans l’étude des foyers de microcalcifications en mammographie numérique

With the advent of digital mammography with electronic zoom capabilities, we have sought to determine the need for geometric magnification for the evaluation of clusters of microcalcifications. Eighty-eight clusters of microcalcifications were reviewed by two experienced radiologists using electronic zoom (ZOOM) and geometric magnification (MAG). The following criteria were evaluated: image quality, shape and number of microcalcifications, size and shape of the clusters. The clusters were classified based on malignancy risk using the BI-RADS criteria. Histological results from macrobiopsy or surgery as well as 2 year follow-up were used as reference for statistical analysis. Sensitivity (100% for MAG and 90% for ZOOM), specificity (52% versus 39%), positive predictive value (51% versus 44%) and negative predictive value (100% versus 88%) were superior for geometric magnification compared to electronic zoom irrespective of the reviewer but without reaching statistical significance. However, image quality was significantly superior with geometric magnification (p<<0.05). In addition, reviewers were more confident in their interpretation of geometric magnification images. Geometric magnification remains necessary in routine clinical practice for the characterization of microcalcifications and BI-RADS classification.

SU‐FF‐I‐114: Effects of Exposure Level and Anatomical Background On Detection of Simulated Microcalcifications in Digital Mammography

Purpose: To assess the influence of quantum and anatomic noises on the detection of simulated microcalcifications (MCs) on digital mammograms. Materials and Methods: Images of an anthropomorphic breast phantom (RMI 169) overlapping with simulated MCs ranging from 160 to 224 micron in size placed at various locations were acquired with an aSi/aSe flat panel based digital mammography system (Selenia) operated with Mo‐Mo target/filter combination at 28 kVp. These mammograms were exposed with 10, 20, 40, 80, 160, and 325 mAs. All images were displayed randomly on a review workstation and reviewed by five readers independently. The readers were asked to count the number of visible MCs. The ratio of visible MCs was computed for each different combination of MC size, reader and mAs setting. The performance differences in the ratios averaged from all readers between mAs levels was assessed by performing Student t‐test to compute the p values. Results: It was found that in the dense tissue region the visibility reached 90% at 10, 33 and 37 mAs for 200–212, 160–180 and 180–200 micron MCs, respectively; and at 10 mAs for 180–200 and 200–212 micron MCs and at 15 mAs for 160–180 micron MCs in the fatty region. The lower visibility in the dense tissue region may be due to the fact that the photon flux was lower in the dense tissue regions therefore requiring higher mAs setting to achieve sufficient image SNRs for detection of MCs. Our results also show that in the dense tissue region, the visibility of 180–200 micron MCs stayed at 90% at 80 mAs. This may indicate that the anatomical noise was limiting the detectability of 180–200 micron MCs.This work was supported in part by grants CA104759 and CA124585 from NIH‐NCI, a grant EB00117 from NIH‐NIBIB, and a subcontract from NIST‐ATP.

Factores predictivos de malignidad en microcalcificaciones de mama sin lesiones asociadas

Screening mammography allows the early detection and treatment of nonpalpable carcinomas. Microcalcifications are highly important for the detection of many of these malignancies but are also present in benign breast disease. In the present study we analyzed the radiologic and clinical factors associated with malignancy. We retrospectively reviewed 133 patients who underwent open biopsy after a mammographic finding of microcalcifications without an associated lesion. Clinical and radiologic characteristics were correlated with histologic findings. A total of 28.6% of the lesions analyzed were malignant. Of these, 65.8% were ductal carcinoma in situ. Among invasive carcinomas, 23.1% presented lymph node metastasis. No clinical criterion was significant for malignancy. On univariate analysis six radiologic criteria were significant: morphologic type (Le Gal's classification), irregularity of size, irregularity of density, number of microcalcifications per cluster, diameter of the lesion, and the presence of more than one cluster. On multivariate analysis the factors with independent predictive value were: irregularity of density, > or = 10 microcalcifications per cluster, the presence of more than one cluster, and diameter of the cluster > or = 10 mm. Although none of the clinical factors analyzed was predictive for malignancy, the radiological characteristics of microcalcifications were determining factors in the indication for biopsy.

WE‐C‐I‐609‐01: Computer‐Aided Diagnosis: Computerized Classification of Malignant and Benign Microcalcifications On Full Field Digital Mammograms

Purpose: To develop a computer‐aided diagnosis (CAD) system for characterization of malignant and benign microcalcifications on mammograms acquired with a full field digital mammography (FFDM) system. Method and Materials: Our computerized classification system uses raw digital mammograms as input. The individual microcalcifications are first segmented by the system from the mammographic breast tissue background. Five morphological features describing the size, the density, and the shape of the individual microcalcifications are extracted. The mean and the variation of each of these features for the individual microcalcifications within a cluster are calculated. These cluster features in combination with the number of microcalcifications in a cluster form the morphological feature space. For texture feature extraction, a region of interest (ROI) containing the cluster of microcalcifications is identified on the mammogram. Background correction is applied to the ROI to reduce the intensity variation in the breast tissue areas. Texture features including the mean, entropy, contrast, and angular second moment are extracted from the gray level dependence difference statistics of the background‐corrected ROIs in four directions. A leave‐one‐case‐out resampling scheme is used to train and test the linear discriminant classifier. The most effective features from the combined morphological and texture feature space are identified using stepwise feature selection with simplex optimization in each training cycle. The performance of the classifier is evaluated by receiver operating characteristic (ROC) analysis. Results: In a preliminary study using digitized mammograms, the computer classifier obtained an area under the ROC curve, Az, of 0.82±0.03 for testing. The performance of the CAD system using a data set of 100 cases of biopsy‐proven microcalcifications on FFDMs will be presented. Conclusion: A trained CAD system can provide an estimate of the likelihood of malignancy of microcalcification on mammograms and thus may be used as a second opinion by radiologists for mammographic interpretation.

Breast microcalcifications: multivariate analysis of radiologic and clinical factors for carcinoma.

Screening mammography contributes to the improvement of breast carcinoma survival through early detection and treatment of non-palpable lesions. Microcalcifications are of fundamental importance in this process. The percentage of malignant lesions found in biopsies for microcalcifications varies from 10% to 40%. The purpose of this study was to evaluate the relationship between clinical and radiologic records and the presence of malignant breast diseases. To establish the basis for the study, 211 mammographic files showing clustered microcalcifications from 204 women were prospectively reviewed and clinical records were retrospectively drawn. Definitive pathologic analysis was available for all. The value for cancer of each criterion was investigated by univariate and multivariate analyses. A first analysis was performed on the entire population and a second one was performed with stratification on morphologic subgroups. There were 99 malignant lesions (47%). In the entire group, no clinical criterion was significant. In the univariate analysis, five radiologic variables were significant: morphologic type(p < 0.0001), number of calcifications per cluster(p < 0.0001), linear or triangular distribution(p < 0.0002), diameter of the area (p < 0.01),and number of clusters (p = 0.011). In the multivariate analysis, two criteria remained significant: morphologic type 4 (irregularly punctiform) or 5 (vermicular) microcalcifications(Le Gal's classification) (p < 0.0001) and diameter of the cluster larger than 25 mm (p = 0.032). In subgroups,in the multivariate analysis, the "age > 60 years" criterion was statistically significant in the group of regular punctiform microcalcifications (type 2); for irregularly punctiform microcalcifications (type 4), "number of microcalcifications > 20" was significant. The morphologic features of microcalcifications must be the first criterion evaluated. They permit identification of characteristically benign (annular calcifications) or malignant calcifications (vermicular calcifications). For the remainder of the calcification types, other criteria must be taken into account, and their value vary with (according to) the morphologic aspect. These findings have implications for the management of women with microcalcifications and could help breast specialists make treatment decisions.

Segmentation of suspicious clustered microcalcifications in mammograms

We have developed a multistage computer-aided diagnosis (CAD) scheme for the automated segmentation of suspicious microcalcification clusters in digital mammograms. The scheme consisted of three main processing steps. First, the breast region was segmented and its high-frequency content was enhanced using unsharp masking. In the second step, individual microcalcifications were segmented using local histogram analysis on overlapping subimages. For this step, eight histogram features were extracted for each subimage and were used as input to a fuzzy rule-based classifier that identified subimages containing microcalcifications and assigned the appropriate thresholds to segment any microcalcifications within them. The final step clustered the segmented microcalcifications and extracted the following features for each cluster: the number of microcalcifications, the average distance between microcalcifications, and the average number of times pixels in the cluster were segmented in the second step. Fuzzy logic rules incorporating the cluster features were designed to remove nonsuspicious clusters, defined as those with typically benign characteristics. A database of 98 images, with 48 images containing one or more microcalcification clusters, provided training and testing sets to optimize the parameters and evaluate the CAD scheme, respectively. The results showed a true positive rate of 93.2% and an average of 0.73 false positive clusters per image. A comparison of our results with other reported segmentation results on the same database showed comparable sensitivity and at the same time an improved false positive rate. The performance of the CAD scheme is encouraging for its use as an automatic tool for efficient and accurate diagnosis of breast cancer.

Mammographic findings after 14-gauge automated needle and 14-gauge directional, vacuum-assisted stereotactic breast biopsies.

To compare findings from first imaging follow-up mammography for breast lesions shown to be benign at stereotactic biopsy with 14-gauge automated needles or 14-gauge directional, vacuum-assisted probes. In 495 stereotactic breast biopsies, the mammographic appearance of the biopsy site or target lesion was evaluated at first imaging follow-up with a four-point scale (1 = progression of lesion or suspicious interval change [repeat biopsy], 2 = no clinically relevant change, 3 = interval decrease in size of lesion or number of microcalcifications, and 4 = no residual mammographic lesion). An automated needle was used in 363 biopsies and a directional, vacuum-assisted probe was used in 132 biopsies. Patient and lesion variables and time to first imaging follow-up were compared for the two techniques. No biopsy site or target lesion was assigned a score of 1 at first imaging follow-up with either technique. No lesion was referred for repeat biopsy because of the mammographic appearance at first imaging follow-up. First imaging follow-up was performed an average of 6.6 months for the directional, vacuum-assisted biopsies and 8.6 months for the automated needle biopsies (P < .0001). This difference reflected a difference in scheduling methods. Directional, vacuum-assisted and automated needle breast biopsies produced no distortion or suspicious interval change at the biopsy site at the first follow-up mammographic examination.

A new human breast cancer cell line, KPL-3C, secretes parathyroid hormone-related protein and produces tumours associated with microcalcifications in nude mice

Parathyroid hormone-related protein (PTHrP) is the main cause of humoral hypercalcaemia of malignancy (HHM). We recently established a new human breast cancer cell line, designated KPL-3C, from the malignant effusion of a breast cancer patient with HHM. Morphological, cytogenetic and immunohistochemical analyses indicated that the cell line is derived from human breast cancer. The KPL-3C cells stably secrete immunoreactive PTHrP measured by a two-site immunoradiometric assay, possess both oestrogen and progesterone receptors and are tumorigenic in female nude mice. The addition of phorbol-12-myristate-13-acetate to the medium significantly increased PTHrP secretion from the cells. In contrast, hydrocortisone, medroxyprogesterone acetate and 22-oxacalcitriol decreased PTHrP secretion in a dose-dependent manner. Unexpectedly, a number of microcalcifications were observed in the transplanted tumours. Radiographical examination indicated that the microcalcifications in the tumours are very similar to those commonly observed in human breast cancer. These findings suggest that this KPL-3C cell line may be useful for studying the regulatory mechanisms of PTHrP secretion and the mechanisms that lead to the deposition of microcalcifications in breast cancer.

Digital Optical Card

To assess a patient-oriented digital optical card (OC) for documentation and communication of images using the analysis of breast microcalcifications to illustrate its resolution power. Fifty film mammograms with histologically proved clustered microcalcifications were digitized using a 5 lp/mm CCD-scanner. A region of interest containing the cluster was selected for documentation on an OC as an overview OC-image and as a magnified OC-image (5 lp/mm). The shape (spherical/nonspherical) as well as the total number of microcalcifications were quantitatively analyzed by 2 radiologists. The detection rate for total number of overall and spherical microcalcifications using digital media was significantly reduced (p < 0.01) compared to analog mammography. There were no significant differences in the detection rate of nonspherical microcalcifications between film mammograms (100%) and magnified section OC-images (92.7%). The overview OC-image revealed 72% of those calcifications (p < 0.01). According to our results, this technology is not appropriate for diagnosis of breast microcalcifications, but may be a promising communication digital medium for transmitting an image/report unit to referring physicians.

A simulation model of clustered breast microcalcifications.

A simulation model of clustered microcalcifications superimposed on normal mammographic backgrounds has been developed and evaluated. A cluster is described according to its size, the number of microcalcifications it contains, and their density. A microcalcification is defined on the basis of its size, shape, location within the cluster, its contrast, and the distribution of the gray-level values between pixels that belong to it. Gray-level parameters are dependent on the surrounding tissue, which is also slightly modified. The main dependencies between the parameters are taken into account. All the parameters are randomly sampled using distribution laws determined from the statistical analysis of 408 real clusters containing a total of 8611 microcalcifications. Some statistical characteristics depend on the structural mammographic pattern on which the cluster is superimposed. The evaluation of the simulation model, performed by two radiologists on a test set of 100 real and simulated clusters, did not reveal any statistically significant difference between the simulated clusters and the real ones. This method provides clusters of microcalcifications with well-defined characteristics and can be used to evaluate the accuracy of computerized detection methods.