Microcalcification Clusters Research Articles

A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis

BackgroundScreening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features.ResultsFor each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance.ConclusionsThe best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters.

Computational reader design and statistical performance evaluation of an in-silico imaging clinical trial comparing digital breast tomosynthesis with full-field digital mammography.

A recent study reported on an in-silico imaging trial that evaluated the performance of digital breast tomosynthesis (DBT) as a replacement for full-field digital mammography (FFDM) for breast cancer screening. In this in-silico trial, the whole imaging chain was simulated, including the breast phantom generation, the x-ray transport process, and computational readers for image interpretation. We focus on the design and performance characteristics of the computational reader in the above-mentioned trial. Location-known lesion (spiculated mass and clustered microcalcifications) detection tasks were used to evaluate the imaging system performance. The computational readers were designed based on the mechanism of a channelized Hotelling observer (CHO), and the reader models were selected to trend human performance. Parameters were tuned to ensure stable lesion detectability. A convolutional CHO that can adapt a round channel function to irregular lesion shapes was compared with the original CHO and was found to be suitable for detecting clustered microcalcifications but was less optimal in detecting spiculated masses. A three-dimensional CHO that operated on the multiple slices was compared with a two-dimensional (2-D) CHO that operated on three versions of 2-D slabs converted from the multiple slices and was found to be optimal in detecting lesions in DBT. Multireader multicase reader output analysis was used to analyze the performance difference between FFDM and DBT for various breast and lesion types. The results showed that DBT was more beneficial in detecting masses than detecting clustered microcalcifications compared with FFDM, consistent with the finding in a clinical imaging trial. Statistical uncertainty smaller than 0.01 standard error for the estimated performance differences was achieved with a dataset containing approximately 3000 breast phantoms. The computational reader design methodology presented provides evidence that model observers can be useful in-silico tools for supporting the performance comparison of breast imaging systems.

Reduction of false positives in the screening CAD tool for microcalcification detection

Breast cancer is one of the leading causes of cancer deaths among women worldwide. Early diagnosis of breast cancer can help in reducing the mortality rate. The major challenge in the early diagnosis of breast cancer is the fewer number of radiologists available per million population in developing countries. The total number of radiologists is less than 30 in many third world countries. Since majority of the screening mammograms are normal or do not show any cancer signs, there is need of a screening computer-aided diagnosis (CAD) tool that can detect normal mammograms correctly and thereby reduce the burden on radiologists. Thus, a screening CAD is developed that is able to detect microcalcification clusters in mammogram with 100% sensitivity on the subset of DDSM, INbreast and PGIMER-IITKGP databases at lower false positives as compared with state of the art methods. The synthetic minority over-sampling technique and the majority class under-sampling based on data distribution are used to improve the classifiers performance by reducing the false positives. An approach based on principal component analysis is proposed to further reduce the false positives by removing the vascular calcifications that are not of any clinical significance and may increase the false positives.

Диагностика заболеваний молочной железы в программе вспомогательных репродуктивных технологий

Objective. To study the state of the breasts in women who undergo assisted reproductive technologies treatment, using modern methods of radiation imaging. Patients and methods. Pre- and prospective examination and analysis of the data of radiation imaging of the breasts of 220 women, having tubal factor infertility in combination with benign mammary dysplasia, who have successful or unsuccessful experience of participation in assisted reproductive technology programmes. The average age of patients was 42.5 years. Assessment points were: breast tissue density according to the findings of ultrasound, mammography and tomosynthesis, presence of clustered microcalcifications, presence of benign dysplastic processes and fibroadenomas of the breasts. Results. In the group of 35–39-year old patients, the most common pathological processes were fibrocystic mastopathy and fibroadenomas. The main diagnostic methods were ultrasound visualization and US-controlled core-needle vacuum-assisted biopsy. In the group of 40–44-year old women, the predominant pathological process was microcalcification clusters alongside high mammographic density. The main diagnostic methods were digital x-ray tomosynthesis and X-ray-guided core-needle vacuum-assisted biopsy. In the group of 45–49-year old women, cases of breast cancer were diagnosed. 12 women in this group had breast implants. Conclusion. Hormonal medication used in assisted reproductive technologies under the protocols of in-vitro fertilization and intracytoplasmatic sperm injection influence the state of the breasts, increasing a risk of developing diffuse benign mammary dysplasias and, quite possible, a risk of developing breast cancer later in life. Key words: breast diseases, assisted reproductive technologies, tomosynthesis, mammotome, clustered microcalcifications

Analysis and correlation of imaging findings with the anatomopathological study of bi-rads 4a lesions

Introduction: The Breast Imaging Reporting and Data System (BI-RADS®) was developed to standardize reports based on imaging findings, classifying them into six categories. Its fourth edition proposed the subdivision of category 4 into three subcategories according to malignancy suspicion. Category 4A shows likelihood of malignancy between 2% and 10%, and diagnosis by biopsy is recommended. Frequent histological findings in the literature for 4A lesions include fibrocystic breast changes, fibroadenoma, columnar cell lesions with atypia, stromal sclerosis, inflammatory disorders, and proliferative epithelial lesions. Objectives: To show the most relevant radiological and histological findings for the BI-RADS® 4A subcategory, corroborating its likelihood ratio of malignancy. Method: This is a cross-sectional study based on the review of medical records of patients submitted to the anatomopathological study of BI-RADS 4A lesions in public and private health services from Curitiba, Paraná, Southern Brazil, between March and September 2019. The findings were subsequently correlated with histopathological results. Results: A total of 727 core needle breast biopsies were performed – 78.6% guided by ultrasound and 21.4% by stereotaxy. Approximately 35.8% of ultrasound-guided procedures (group X) and 55.4% of stereotaxic biopsies (group Y) were classified as BI-RADS 4A. Among the main imaging findings in group X, solid nodules, solid cystic lesions, and solid heterogeneous areas stood out. Group Y presented clusters of heterogeneous, punctate, amorphous microcalcifications, and findings that did not fit the BI-RADS classification. Benign changes predominated among the histopathological findings in both groups. The malignancy rate according to guidelines of the European classification for anatomopathological results of breast lesions4 remained around 2% in group X and 8.7% in group Y. Conclusion: Based on the results obtained, we concluded that the malignancy rates of biopsies from patients classified as BI-RADS 4A were within the acceptable values established by the literature. However, they varied considerably according to the biopsy method chosen, presenting higher values in patients submitted to stereotaxy.

Is there a safety-net effect with computer-aided detection?

Computer-aided detection (CAD) systems are used to aid readers interpreting screening mammograms. An expert reader searches the image initially unaided and then once again with the aid of CAD, which prompts automatically detected suspicious regions. This could lead to a "safety-net" effect, where the initial unaided search of the image is adversely affected by the fact that it is preliminary to an additional search with CAD and may, therefore, be less thorough. To investigate the existence of such an effect, we created a visual search experiment for nonexpert observers mirroring breast screening with CAD. Each observer searched 100 images for microcalcification clusters within synthetic images in both prompted (CAD) and unprompted (no-CAD) conditions. Fifty-two participants were recruited for the study, 48 of whom had their eye movements tracked in real-time; the other 4 participants could not be accurately calibrated, so only behavioral data were collected. In the CAD condition, before prompts were displayed, image coverage was significantly lower than coverage in the no-CAD condition ( , ). Observer sensitivity was significantly greater for targets marked by CAD than the same targets in the no-CAD condition ( , ). For targets not marked by CAD, there was no significant difference in observer sensitivity in the CAD condition compared with the same targets in the no-CAD condition ( , ). These results suggest that the initial search may be influenced by the subsequent availability of CAD; if so, cross-sectional CAD efficacy studies should account for the effect when estimating benefit.

Ensemble Discrete Wavelet Transform and Gray-Level Co-Occurrence Matrix for Microcalcification Cluster Classification in Digital Mammography

The presence of clusters of microcalcifications is a primary sign of breast cancer. Their identification is still difficult today for radiologists, and the wrong evaluations involve unnecessary biopsies. In this paper, an automatic tool for characterizing and discriminating clusters of microcalcifications into benign/malignant in digital mammograms is proposed. A set of 104 digital mammograms including microcalcification clusters was randomly extracted from a public available database and manually labeled by our radiologists, obtaining 96 abnormal ROIs. For each so-identified ROI, a multi-scale image decomposition based on the Haar wavelet transform was performed. On the decomposition, a textural features extraction step was carried out both on each sub-image and on the corresponding gray-level co-occurrence matrix. Then, a random forest classifier was employed for classifying microcalcification clusters into benign and malignant. The study found that the most discriminant features extracted from the ROIs decomposition by Haar transform were variance and relative smoothness, whereas as regards the textural features calculated on the GLCMs corresponding to the Haar-decomposed ROI, it emerged that the relationship between the pixels of the sub-image in the diagonal direction had high discriminating power for the classification of microcalcification clusters into benign and malignant. The proposed method was evaluated in cross-validation and performed highly in the prediction of the benign/malignant ROIs, with a mean AUC value of 97.39 ± 0.01 % .

Quantitative assessment of microcalcification cluster image quality in digital breast tomosynthesis, 2-dimensional and synthetic mammography.

Quantitative assessment of microcalcification (MC) cluster image quality is presented, in terms of cluster signal-difference-to-noise ratio (SDNR) intercomparison among digital breast tomosynthesis (DBT) and 2-dimensional (2D) and synthetic-2-dimensional (s2D) mammography. A phantom that provides realistic appearance of MC clusters located in uniform and nonuniform background was imaged in 2D and DBT, considering various scattering conditions. MC cluster SDNR differentiation is investigated with respect to MC particle size (uniform background) and surrounding parenchyma density (nonuniform background). An accurate MC cluster segmentation method was used to delineate individual MC particles and estimate MC cluster SDNR. Analysis of the uniform part of the phantom indicated higher performance of DBT and 2D over s2D for the smallest cluster size (106-177 μm), no difference among mammographic modes for the largest MC cluster (224-354 μm), and enhanced role of 2D for decreasing cluster size and increasing scattering. Analysis of the nonuniform part of the phantom indicated DBT performed better than 2D and s2D in case of dense parenchyma pattern, while 2D and s2D did not differ across parenchyma density patterns and scattering conditions. The presented MC cluster SDNR analysis was capable of revealing subtle differences among mammographic modes and suggests a methodology for clinical image quality assessment. Graphical abstract.

Application of Fractals to Detect Breast Cancer

Breast cancer is one of the major causes of death among women. Small clusters of micro calcifications appearing as collection of white spots on mammograms show an early warning of breast cancer. Image segmentation is an important element of Digital Image processing that subdivides the image into discrete regions/objects, each identified by the property of homogeneity of pixels. X-ray mammography is used as diagnostic tool for diagnosis of breast cancer. Edge detection of micro calcification clusters in mammogram images is the main issue of early detection of breast cancer. Fractals are of rough or fragmented geometric shape that can be subdivided in parts, each of which is a reduced similar of the whole. Fractal dimension and Hurst exponent are used to locate the micro calcifications in the mammogram. The concept of fractal is associated with geometrical objects satisfying criteria such as self-similarity and fractal dimensionality. Present method of edge detection is superior compared to conventional Sobel method, in which detection of edges of the regions or objects in an image takes place by the Fudge factor. The paper presents the application of fractals for early detection of breast cancer.

Objective assessment of task performance: a comparison of two FFDM detectors using an anthropomorphic breast phantom.

Current digital mammography systems primarily employ one of two types of detectors: indirect conversion, typically using a cesium-iodine scintillator integrated with an amorphous silicon photodiode matrix, or direct conversion, using a photoconductive layer of amorphous selenium (a-Se) combined with thin-film transistor array. The goal of this study was to evaluate a methodology for objectively assessing image quality to compare human observer task performance in detecting microcalcification clusters and extended mass-like lesions achieved with different detector types. The proposed assessment methodology uses a novel anthropomorphic breast phantom fabricated with ink-jet printing. In addition to human observer detection performance, standard linear metrics such as modulation transfer function, noise power spectrum, and detective quantum efficiency (DQE) were also measured to assess image quality. An Analogic Anrad AXS-2430 a-Se detector used in a commercial FFDM/DBT system and a Teledyne Dalsa Xineos-2329 with CMOS pixel readout were evaluated and compared. The DQE of each detector was similar over a range of exposures. Similar task performance in detecting microcalcifications and masses was observed between the two detectors over a range of clinically applicable dose levels, with some perplexing differences in the detection of microcalcifications at the lowest dose measurement. The evaluation approach presented seems promising as a new technique for objective assessment of breast imaging technology.

Association of Microcalcification Clusters with Short-term Invasive Breast Cancer Risk and Breast Cancer Risk Factors

Using for-presentation and for-processing digital mammograms, the presence of microcalcifications has been shown to be associated with short-term risk of breast cancer. In a previous article we developed an algorithm for microcalcification cluster detection from for-presentation digital mammograms. Here, we focus on digitised mammograms and use a three-step algorithm. In total, 253 incident invasive breast cancer cases (with a negative mammogram between three months and two years before diagnosis, from which we measured microcalcifications) and 728 controls (also with prior mammograms) were included in a short-term risk study. After adjusting for potential confounding variables, we found evidence of an association between the number of microcalcification clusters and short-term (within 3–24 months) invasive breast cancer risk (per cluster OR = 1.30, 95% CI = (1.11, 1.53)). Using the 728 postmenopausal healthy controls, we also examined association of microcalcification clusters with reproductive factors and other established breast cancer risk factors. Age was positively associated with the presence of microcalcification clusters (p = 4 × 10−04). Of ten other risk factors that we studied, life time breastfeeding duration had the strongest evidence of association with the presence of microcalcifications (positively associated, unadjusted p = 0.001). Developing algorithms, such as ours, which can be applied on both digitised and digital mammograms (in particular for presentation images), is important because large epidemiological studies, for deriving markers of (clinical) risk prediction of breast cancer and prognosis, can be based on images from these different formats.

Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier.

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy (%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A value equal to .

Multiscale connected chain topological modelling for microcalcification classification

Computer-aided diagnosis (CAD) systems can be employed to help classify mammographic microcalcification clusters. In this paper, a novel method for the classification of the microcalcification clusters based on topology/connectivity has been introduced. The proposed method is distinct from existing techniques which concentrate on morphology and texture of microcalcifications and surrounding tissue. The proposed approach used multiscale morphological relationship of connectivity between microcalcifications where connected chains between nearest microcalcifications were generated at each scale. Subsequently, graph connectivity features at each scale were extracted to estimate the topological connectivity structure of microcalcification clusters for benign versus malignant classification. The proposed approach was evaluated using publicly available digitized datasets: MIAS and DDSM, in addition to the digital OPTIMAM dataset. The classification of features using KNN obtained a classification accuracy of 86.47±1.30%, 90.0±0.00%, 82.5±2.63%, 76.75±0.66% for the DDSM, MIAS-manual, MIAS-auto and OPTIMAM datasets respectively. The study showed that topological/connectivity modelling using a multiscale approach was appropriate for microcalcification cluster analysis and classification; topological connectivity and distribution can be linked to clinical understanding of microcalcification spatial distribution.

A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms.

Breast cancer is the most common cancer diagnosed in women worldwide. Up to 50% of non-palpable breast cancers are detected solely through microcalcification clusters in mammograms. This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. Several texture, shape, intensity, and histogram of oriented gradients (HOG)-based features are used to distinguish microcalcifications from other brighter mammogram regions. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. The algorithm is able to achieve 100% sensitivity with 2.59, 1.78, and 0.68 average false positives per image on Digital Database for Screening Mammography (scanned film), INbreast (direct radiography) database, and PGIMER-IITKGP mammogram (direct radiography) database, respectively. Thus, it might be used as a second reader as well as a screening tool to reduce the burden on radiologists.

A four-alternative forced choice (4AFC) methodology for evaluating microcalcification detection in clinical full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) systems using an inkjet-printed anthropomorphic phantom.

The advent of three-dimensional breast imaging systems such as digital breast tomosynthesis (DBT) has great promise for improving the detection and diagnosis of breast cancer. With these new technologies comes an essential need for testing methods to assess the resultant image quality. Although randomized clinical trials are the gold standard for assessing image quality, phantom-based studies can provide a simpler and less burdensome approach. In this work, a complete framework is presented for task-based evaluation of microcalcification (MCs) detection performance for DBT imaging systems. The framework consists of three parts. The first part is a realistic anthropomorphic physical breast phantom created through inkjet printing, with parchment paper and iodine-doped ink. The second is a method for inserting realistic MCs fabricated from calcium hydroxyapatite. The reproducibility and stability of the phantom materials were investigated through multiple samples of parchment and ink over 6 months. The final part is an analysis using a four-alternative forced choice (4AFC) reader study. To demonstrate the framework, a task-based 4AFC study was conducted using a clinical system to compare performance from DBT, synthetic mammography (SM), and full-field digital mammography (FFDM). Nine human observers read images containing MC clusters imaged with all three modalities and tried to correctly locate the MCs. The proportion correct (PC) was measured as the number of correctly detected clusters out of all trials. Overall, readers scored the highest with FFDM, (PC=0.95±0.03) then DBT (0.85±0.04), and finally SM (0.44±0.06). For the parchment and ink samples, the linear attenuation properties were very stable over 6 months. In addition, little difference was found between the various parchment and ink samples, indicating good reproducibility. This framework presents a promising methodology for evaluating diagnostic task performance of clinical breast DBT systems.

Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system

BackgroundMicrocalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians’ work with an automatic tool. MethodsIn this work a three-phases approach for clustered microcalcification detection is presented. Specifically, it is made up of a pre-processing step, aimed at highlighting potentially interesting breast structures, followed by a single microcalcification detection step, based on Hough transform, that is able to grasp the innate characteristic shape of the structures of interest. Finally, a cluster identification step to group microcalcifications is carried out by means of a clustering algorithm able to codify expert domain rules. ResultsThe detection performance of the proposed method has been evaluated on 364 mammograms of 182 patients obtaining a true positive ratio of 91.78% with 2.87 false positives per image. ConclusionsExperimental results demonstrated that the proposed method is able to detect microcalcification clusters in digital mammograms showing performance comparable to different methodologies exploited in the state-of-art approaches, with the advantage that it does not require any training phase and a large set of data. The performance of the proposed approach remains high even for more difficult clinical cases of mammograms of young women having high-density breast tissue thus resulting in a reduced contrast between microcalcifications and surrounding dense tissues.

Computer-aided detection and diagnosis of microcalcification clusters on full field digital mammograms based on deep learning method using neutrosophic boosting

Computer-aided detection (CADe) and diagnosis (CADx) system of mammographic microcalcification clusters (MCCs) is built for helping human observers to find suspicious areas of MCC and providing risk predictions of malignancy as a reference, since it is challenging and time consuming for radiologists to manually identify some subtle microcalcifications (MCs) and perform precise interpretation in mammograms. However, the performance of traditional CADe and CADx systems is not good enough, thus it is difficult to combine them into a whole system that integrates detection and diagnosis together. The purpose of this study is to develop a fully automatic computer-aided MCC detection and diagnosis system based on deep learning method. In order to detect subtle MCs accurately, a MC candidate detection system is used to obtain a great number of potential MC candidates, then a deep convolution neural network (DCNN) is trained specially to discriminate true MCs from detected MC candidates. Different from previous literatures committing to finding and selecting effective features, the proposed method replaces manual feature extraction step by using DCNN. To accelerate the training procedure of the DCNN, a neutrosophic boosting (NB) strategy is applied in the training stage. Then a density-based regional clustering method is imposed on those true MCs to form MCCs. Finally, another DCNN is employed to differentiate benign from malignant MCC lesions. For cluster-based MCC detection evaluation, a sensitivity of 90% is achieved at 0.14 false positives (FPs) per image. For case-based MCC classification evaluation, the area under the receiver operating characteristic curves (AUCs) on validation and testing sets are 0.945, 0.933 for proposed system, respectively. Our obtained results demonstrated the effectiveness of the proposed method for automated detection and classification of MCCs.

A microcalcification cluster detection method based on deep learning and multi-scale feature fusion

To accurately identify microcalcification clusters (MCs) in x-ray images to detect breast cancer earlier, a new MC target detection method which combines a deep fine-grained cascade-enhanced network (FCE-Net) and a multi-scale feature fusion algorithm (MFF) is proposed. First, a deep convolutional neural network model FCE-Net is established to extract characteristics of MCs. FCE-Net constructs a convolutional subnetwork within the convolutional module to enhance the multi-branch structure and thus improves the detailed feature extraction performance of MCs. Then, a multi-level, fine-grained convolutional map is generated and a multi-scale feature fusion algorithm (MFF) is constructed. In order to solve the problem of fine-grained target semantic information enhancement, MFF up-samples the basic feature map twice and then laterally connects to the previous layer. Then, through the parallel regression calculation and feature classification, the confidence and regional coordinates of MC objects are obtained. Finally, the target area is classified and bounding box adjustment is performed in the merged layer of the region of interest. We perform the proposed method on the MIAS breast cancer data set. By training and testing, the accuracy of detection is 97.16% under the N5 parameter settings. The overall model improves the detection efficiency of small targets by 5–10%.

Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging.

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.

EVALUATION OF CLUSTERED MICROCALCIFICATIONS, INITIALLY GRADED AS LIKELY MALIGNANT ON BASELINE MAMMOGRAPHY, WITH STEREOTACTIC BIOPSY: A RAD-PATH CORRELATION

Background: Stereotactic-guided breast biopsy is an invincible tool to sample abnormalities visible only on mammography with subtle or occult ultrasound findings. Common mammographic abnormalities which require stereotactic core biopsy include calcifications, architectural distortion and satellite lesions. With advancement in techniques for adequate localisation of lesions, the use of stereotactic needle breast biopsy is exponentially increasing. A study was performed to look for frequency of benignancy or malignancy of microcalcifications and architectural distortions in patients initially falling in malignant category of Breast Imaging-Reporting and Data System of American College of Radiology (BI-RADS®) Assessment Categories.Materials and Methods: Patient presenting in outpatient (OPD) clinic who underwent stereotactic biopsies for labelled malignant on mammograms from May 2015 to May 2018 was included in retrospective study. Age, technique used, baseline mammogram and histopathology were reviewed.Results: A total of 91 patients underwent stereotactic biopsy. Age range varied between 28 and 81 years. Stereotacticguided biopsies were taken using a standard 14-gauge core needle with long throw of 22 mm excursion. None of the biopsy was inconclusive. In comparison with baseline mammograms and histopathology, 40% of the clusters of microcalcification which were initially labelled as malignant turned out to be malignant on histopathology as well, whereas 60% were reported as benign breast parenchyma.Conclusion: Agreement between the diagnostic accuracy of microcalcifications on mammogram and histopathology of same lesions after stereotactic biopsy was comparable to international figures with probability of being benign surpassing malignant on routine screening.Key words: Breast cancer, mammography, microcalcifications, stereotactic biopsy