Detection Of Microcalcifications Research Articles

Maximizing microcalcification detectability in low-dose dedicated cone-beam breast CT: parallel cascades-based theoretical analysis.

We aim to determine the combination of X-ray spectrum and detector scintillator thickness that maximizes the detectability of microcalcification clusters in dedicated cone-beam breast CT. A cascaded linear system analysis was implemented in the spatial frequency domain and was used to determine the detectability index using numerical observers for the imaging task of detecting a microcalcification cluster with 0.17mm diameter calcium carbonate spheres. The analysis considered a thallium-doped cesium iodide scintillator coupled to a complementary metal-oxide semiconductor detector and an analytical filtered-back-projection reconstruction algorithm. Independent system parameters considered were the scintillator thickness, applied X-ray tube voltage, and X-ray beam filtration. The combination of these parameters that maximized the detectability index was considered optimal. Prewhitening, nonprewhitening, and nonprewhitening with eye filter numerical observers indicate that the combination of 0.525 to 0.6mm thick scintillator, 70kV, and 0.25 to 0.4mm added copper filtration maximized the detectability index at a mean glandular dose (MGD) of 4.5mGy. Using parallel cascade systems' analysis, the combination of parameters that could maximize the detection of microcalcifications was identified. The analysis indicates that a harder beam than that used in current practice may be beneficial for the task of detecting microcalcifications at an MGD suitable for breast cancer screening.

A novel approach for segmentation and quantitative analysis of breast calcification in mammograms.

Breast cancer is a major threat to women's health globally. Early detection of breast cancer is crucial for saving lives. One important early sign is the appearance of breast calcification in mammograms. Accurate segmentation and analysis of calcification can improve diagnosis and prognosis. However, small size and diffuse distribution make calcification prone to oversight. This study aims to develop an efficient approach for segmenting and quantitatively analyzing breast calcification from mammograms. The goal is to assist radiologists in discerning benign versus malignant lesions to guide patient management. This study develops a framework for breast calcification segmentation and analysis using mammograms. A Pro_UNeXt algorithm is proposed to accurately segment calcification lesions by enhancing the UNeXt architecture with a microcalcification detection block, fused-MBConv modules, multiple-loss-function training, and data augmentation. Quantitative features are then extracted from the segmented calcification, including morphology, size, density, and spatial distribution. These features are used to train machine learning classifiers to categorize lesions as malignant or benign. The proposed Pro_UNeXt algorithm achieved superior segmentation performance versus UNet and UNeXt models on both public and private mammogram datasets. It attained a Dice score of 0.823 for microcalcification detection on the public dataset, demonstrating its accuracy for small lesions. For quantitative analysis, the extracted calcification features enabled high malignant/benign classification, with AdaBoost reaching an AUC of 0.97 on the private dataset. The consistent results across datasets validate the representative and discerning capabilities of the proposed features. This study develops an efficient framework integrating customized segmentation and quantitative analysis of breast calcification. Pro_UNeXt offers precise localization of calcification lesions. Subsequent feature quantification and machine learning classification provide comprehensive malignant/benign assessment. This end-to-end solution can assist clinicians in early diagnosis, treatment planning, and follow-up for breast cancer patients.

Adaptive fuzzy weighted median filter for microcalcifications detection in digital breast tomosynthesis images

Breast cancer is a global leading cause of female mortality. Digital breast tomosynthesis (DBT) is pivotal for early breast cancer detection, with microcalcifications serving as crucial indicators. However, the movement of the DBT machine introduces blurry artefacts, potentially impacting accurate diagnosis. This study addresses this challenge by proposing an adaptive fuzzy weighted median filter (AFWMF) to enhance DBT images and aid microcalcification diagnosis. AFWMF automatically determines optimal parameters based on input images, outperforming conventional methods with a threshold range (C) from peak to end of switching. Quantitative assessment reveals peak signal to noise ratio (PSNR), and mean absolute error (MAE) values of 96.2267 and 0.0000636, respectively, demonstrating a significant improvement in microcalcification detection. This study contributes an effective and adaptive enhancement technique for DBT images, promising better breast cancer diagnosis, particularly in microcalcification scenarios.

Optimized signal of calcifications in wide-angle digital breast tomosynthesis: a virtual imaging trial.

Evaluate microcalcification detectability in digital breast tomosynthesis (DBT) and synthetic 2D mammography (SM) for different acquisition setups using a virtual imaging trial (VIT) approach. Medio-lateral oblique (MLO) DBT acquisitions on eight patients were performed at twice the automatic exposure controlled (AEC) dose. The noise was added to the projections to simulate a given dose trajectory. Virtual microcalcification models were added to a given projection set using an in-house VIT framework. Three setups were evaluated: (1) standard acquisition with 25 projections at AEC dose, (2) 25 projections with a convex dose distribution, and (3) sparse setup with 13 projections, every second one over the angular range. The total scan dose and angular range remained constant. DBT volume reconstruction and synthetic mammography image generation were performed using a Siemens prototype algorithm. Lesion detectability was assessed through a Jackknife-alternative free-response receiver operating characteristic (JAFROC) study with six observers. For DBT, the area under the curve (AUC) was 0.97 ± 0.01 for the standard, 0.95 ± 0.02 for the convex, and 0.89 ± 0.03 for the sparse setup. There was no significant difference between standard and convex dose distributions (p = 0.309). Sparse projections significantly reduced detectability (p = 0.001). Synthetic images had a higher AUC with the convex setup, though not significantly (p = 0.435). DBT required four times more reading time than synthetic mammography. A convex setup did not significantly improve detectability in DBT compared to the standard setup. Synthetic images exhibited a non-significant increase in detectability with the convex setup. Sparse setup significantly reduced detectability in both DBT and synthetic mammography. This virtual imaging trial study allowed the design and efficient testing of different dose distribution trajectories with real mammography images, using a dose-neutral protocol. • In DBT, a convex dose distribution did not increase the detectability of microcalcifications compared to the current standard setup but increased detectability for the SM images. • A sparse setup decreased microcalcification detectability in both DBT and SM images compared to the convex and current clinical setups. • Optimal microcalcification cluster detection in the system studied was achieved using either the standard or convex dose setting, with the default number of projections.

GravityNet for end-to-end small lesion detection

This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be “attracted” by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks.

Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review.

Breast cancer, affecting both genders, but mostly females, exhibits shifting demographic patterns, with an increasing incidence in younger age groups. Early identification through mammography, clinical examinations, and breast self-exams enhances treatment efficacy, but challenges persist in low- and medium-income countries due to limited imaging resources. This review assesses the feasibility of employing breast ultrasound as the primary breast cancer screening method, particularly in resource-constrained regions. Following the PRISMA guidelines, this study examines 52 publications from the last five years. Breast ultrasound, distinct from mammography, offers advantages like radiation-free imaging, suitability for repeated screenings, and preference for younger populations. Real-time imaging and dense breast tissue evaluation enhance sensitivity, accessibility, and cost-effectiveness. However, limitations include reduced specificity, operator dependence, and challenges in detecting microcalcifications. Automatic breast ultrasound (ABUS) addresses some issues but faces constraints like potential inaccuracies and limited microcalcification detection. The analysis underscores the need for a comprehensive approach to breast cancer screening, emphasizing international collaboration and addressing limitations, especially in resource-constrained settings. Despite advancements, notably with ABUS, the primary goal is to contribute insights for optimizing breast cancer screening globally, improving outcomes, and mitigating the impact of this debilitating disease.

Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification.

Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated and error-prone process due to their diverse sizes, shapes, and subtle variations. In this study, we propose a radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, and malignant microcalcifications. Radiomic features were extracted from a proprietary dataset, composed of 380 healthy tissue, 136 benign, and 242 malignant microcalcifications ROIs. Subsequently, two distinct signatures were selected to differentiate between healthy tissue and microcalcifications (detection task) and between benign and malignant microcalcifications (classification task). Machine learning models, namely Support Vector Machine, Random Forest, and XGBoost, were employed as classifiers. The shared signature selected for both tasks was then used to train a multi-class model capable of simultaneously classifying healthy, benign, and malignant ROIs. A significant overlap was discovered between the detection and classification signatures. The performance of the models was highly promising, with XGBoost exhibiting an AUC-ROC of 0.830, 0.856, and 0.876 for healthy, benign, and malignant microcalcifications classification, respectively. The intrinsic interpretability of radiomic features, and the use of the Mean Score Decrease method for model introspection, enabled models' clinical validation. In fact, the most important features, namely GLCM Contrast, FO Minimum and FO Entropy, were compared and found important in other studies on breast cancer.

Task-based detectability in anatomical background in digital mammography, digital breast tomosynthesis and synthetic mammography

Objective. Determining the detectability of targets for the different imaging modalities in mammography in the presence of anatomical background noise is challenging. This work proposes a method to compare the image quality and detectability of targets in digital mammography (DM), digital breast tomosynthesis (DBT) and synthetic mammography. Approach. The low-frequency structured noise produced by a water phantom with acrylic spheres was used to simulate anatomical background noise for the different types of images. A method was developed to apply the non-prewhitening observer model with eye filter (NPWE) in these conditions. A homogeneous poly(methyl) methacrylate phantom with a 0.2 mm thick aluminium disc was used to calculate 2D in-plane modulation transfer function (MTF), noise power spectrum (NPS), noise equivalent quanta, and system detective quantum efficiency for 30, 50 and 70 mm thicknesses. The in-depth MTFs of DBT volumes were determined using a thin tungsten wire. The MTF, system NPS and anatomical NPS were used in the NPWE model to calculate the threshold gold thickness of the gold discs contained in the CDMAM phantom, which was taken as reference. Main results. The correspondence between the NPWE model and the CDMAM phantom (linear Pearson correlation 0.980) yielded a threshold detectability index that was used to determine the threshold diameter of spherical microcalcifications and masses. DBT imaging improved the detection of masses, which depended mostly on the reduction of anatomical background noise. Conversely, DM images yielded the best detection of microcalcification s. Significance. The method presented in this study was able to quantify image quality and object detectability for the different imaging modalities and levels of anatomical background noise.

Model-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach

Objective. Digital breast tomosynthesis (DBT) is a quasi-three-dimensional breast imaging modality that improves breast cancer screening and diagnosis because it reduces fibroglandular tissue overlap compared with 2D mammography. However, DBT suffers from noise and blur problems that can lower the detectability of subtle signs of cancers such as microcalcifications (MCs). Our goal is to improve the image quality of DBT in terms of image noise and MC conspicuity. Approach. We proposed a model-based deep convolutional neural network (deep CNN or DCNN) regularized reconstruction (MDR) for DBT. It combined a model-based iterative reconstruction (MBIR) method that models the detector blur and correlated noise of the DBT system and the learning-based DCNN denoiser using the regularization-by-denoising framework. To facilitate the task-based image quality assessment, we also proposed two DCNN tools for image evaluation: a noise estimator (CNN-NE) trained to estimate the root-mean-square (RMS) noise of the images, and an MC classifier (CNN-MC) as a DCNN model observer to evaluate the detectability of clustered MCs in human subject DBTs. Main results. We demonstrated the efficacies of CNN-NE and CNN-MC on a set of physical phantom DBTs. The MDR method achieved low RMS noise and the highest detection area under the receiver operating characteristic curve (AUC) rankings evaluated by CNN-NE and CNN-MC among the reconstruction methods studied on an independent test set of human subject DBTs. Significance. The CNN-NE and CNN-MC may serve as a cost-effective surrogate for human observers to provide task-specific metrics for image quality comparisons. The proposed reconstruction method shows the promise of combining physics-based MBIR and learning-based DCNNs for DBT image reconstruction, which may potentially lead to lower dose and higher sensitivity and specificity for MC detection in breast cancer screening and diagnosis.

Finding the Missing Calcifications: How Deep to Cut?

Abstract Introduction/Objective Mammographic documentation of microcalcifications requires biopsy of the microcalcifications with histologic study to demonstrate that the area of mammographic interest is examined histologically to exclude carcinoma. Initial levels may not demonstrate these microcalcifications requiring study of additional levels. Little data exists as to how deeply levels should be cut to document microcalcifications. Methods/Case Report A search of pathology records at the University of Missouri between January 1, 2022 and March 30, 2023 for reports containing the term “microcalcifications” in the clinical history or specimen designation was performed. For each identified case, all slides of blocks designated by the radiologist as containing microcalcifications were pulled for review. Our protocol was to cut initially two levels separated by 50 microns. If no microcalcifications were detected an additional 10 levels each separated by 100 microns were examined. The level at which microcalcifications were first detected was recorded. Results (if a Case Study enter NA) The search revealed four-hundred and thirty-one specimens meeting study criteria. Four-hundred and fifteen of these samples contained microcalcifications on histologic examination. Probability of finding microcalcifications in the initial level was 0.629 and 0.935 in the first six levels. The incremental cost per calcification was 1.5 for the first level and 66.7 for the sixth level. The relative cost per calcification in level 6 was 44.5. Levels beyond level 6 rarely disclose microcalcifications. Conclusion 94% of microcalcifications documented by imaging were detected histologically in the first six levels using our protocol. The six-level protocol appears optimal for detection of microcalcifications. This study has significant implications for other specimen types. When a strong suspicion for a pathologic lesion is present on imaging but histologic sampling reveals no lesion, four further levels cut at 100 microns should be obtained.

Abstract 15815: Non-Contrast Computed Tomography Attenuation Measurement for Early Detection of Aortic Valve Microcalcifications

Introduction: Calcific aortic stenosis (AS) is often diagnosed at its late stage. 18F- sodium fluoride (18F-NaF) PET can identify areas of active calcification before appearance of macrocalcifications. Hypothesis: We hypothesized that CT attenuation measurement could detect areas of microcalcification as determined by 18F-Na F PET. Methods: 18F-NaF PET/CT data were evaluated in 102 patients (age 71 ± 8 years, 21% female) with calcific AS at baseline and 2-year follow-up. Multiple 3 mm regions of interest were identified based on 18Na-F PET uptake and presence of macrocalcification (Hounsfield units, HU>130): PET-/CT-, PET+/CT- and PET+/CT+. Assessment of CT attenuation was also performed in 20 control patients with no AS at baseline and after 1 year. Results: At baseline, PET+/CT- regions had greater HU than PET-/CT- regions (median 50 [IQR 42-55] vs. 39 [IQR 32-46]; p < 0.001). Further, PET+/CT- regions showed greater 24-month change vs PET-/CT- (median 16 [IQR) 7-28] vs. 0.83 [IQR -2.56-6.91]; p < 0.001) (Figure 1). A cut off value of 48.76 HU yielded sensitivity/specificity of 57%/85% for detection of microcalcifications, AUC = 0.760. Applying this threshold to the control group the calculated 24-month increase in HU was greater in patients above the threshold vs those below (median 9.89 [IQR -2.92-29.82] vs. 0.57[IQR -7.16-4.31]; p < 0.001) (Figure 2). Conclusions: Aortic valve non-contrast CT X-ray attenuation value above 48.76 HU was able to identify regions of 18F-NaF uptake in patients with AS. Therefore, measuring X-ray attenuation on non-contrast CT has the potential to identify regions of developing microcalcification within the aortic valve.

Deep learning performance for detection and classification of microcalcifications on mammography

BackgroundBreast cancer screening through mammography is crucial for early detection, yet the demand for mammography services surpasses the capacity of radiologists. Artificial intelligence (AI) can assist in evaluating microcalcifications on mammography. We developed and tested an AI model for localizing and characterizing microcalcifications.MethodsThree expert radiologists annotated a dataset of mammograms using histology-based ground truth. The dataset was partitioned for training, validation, and testing. Three neural networks (AlexNet, ResNet18, and ResNet34) were trained and evaluated using specific metrics including receiver operating characteristics area under the curve (AUC), sensitivity, and specificity. The reported metrics were computed on the test set (10% of the whole dataset).ResultsThe dataset included 1,000 patients aged 21–73 years and 1,986 mammograms (180 density A, 220 density B, 380 density C, and 220 density D), with 389 malignant and 611 benign groups of microcalcifications. AlexNet achieved the best performance with 0.98 sensitivity, 0.89 specificity of, and 0.98 AUC for microcalcifications detection and 0.85 sensitivity, 0.89 specificity, and 0.94 AUC of for microcalcifications classification. For microcalcifications detection, ResNet18 and ResNet34 achieved 0.96 and 0.97 sensitivity, 0.91 and 0.90 specificity and 0.98 and 0.98 AUC, retrospectively. For microcalcifications classification, ResNet18 and ResNet34 exhibited 0.75 and 0.84 sensitivity, 0.85 and 0.84 specificity, and 0.88 and 0.92 AUC, respectively.ConclusionsThe developed AI models accurately detect and characterize microcalcifications on mammography.Relevance statementAI-based systems have the potential to assist radiologists in interpreting microcalcifications on mammograms. The study highlights the importance of developing reliable deep learning models possibly applied to breast cancer screening.Key points• A novel AI tool was developed and tested to aid radiologists in the interpretation of mammography by accurately detecting and characterizing microcalcifications.• Three neural networks (AlexNet, ResNet18, and ResNet34) were trained, validated, and tested using an annotated dataset of 1,000 patients and 1,986 mammograms.• The AI tool demonstrated high accuracy in detecting/localizing and characterizing microcalcifications on mammography, highlighting the potential of AI-based systems to assist radiologists in the interpretation of mammograms.Graphical

Task-based assessment of digital mammography microcalcification detection with deep learning denoising algorithmss using in silico and physical phantom studies.

Recent research suggests that image quality degradation with reduced radiation exposure in mammography can be mitigated by postprocessing mammograms with denoising algorithms based on convolutional neural networks. Breast microcalcifications, along with extended soft-tissue lesions, are the primary breast cancer biomarkers in a clinical x-ray examination, with the former being more sensitive to quantum noise. We test one such publicly available denoising method to observe if an improvement in detection of small microcalcifications can be achieved when deep learning-based denoising is applied to half-dose phantom scans. An existing denoiser model (that was previously trained on clinical data) was applied to mammograms of an anthropomorphic physical phantom with hydroxyapatite microcalcifications. In addition, another model trained and tested using all synthetic (Monte Carlo) data was applied to a similar digital compressed breast phantom. Human reader studies were conducted to assess and compare image quality in a set of binary signal detection 4-AFC experiments, with proportion of correct responses used as a performance metric. In both physical phantom/clinical system and simulation studies, we saw no apparent improvement in small microcalcification signal detection in denoised half-dose mammograms. However, in a Monte Carlo study, we observed a noticeable jump in 4-AFC scores, when readers analyzed denoised half-dose images processed by the neural network trained on a dataset composed of 50% signal-present (SP) and 50% signal-absent regions of interest (ROIs). Our findings conjecture that deep-learning denoising algorithms may benefit from enriching training datasets with SP ROIs, at least in cases with clusters of 5 to 10 microcalcifications, each of size .

Evaluating denoising performances of basic filters in the detection of microcalcifications on mammogram images

This research evaluates the denoising abilities of some image-processing filters used in facilitating the early detection of microcalcifications in breast tissues. The mean, median and Gaussian filters were employed to denoise mammogram images of microcalcification breast phantoms of various densities. The performances of the filters were assessed by evaluating the mean squared error (MSE), peak signal-to-noise ratio (PSNR), and signal-to-noise ratio (SNR). All experiments were carried out on MATLAB R2020a platform. The results revealed that the Gaussian filter recorded optimal performance in denoising images with all 3 types of added noises compared to the mean and median filters. The PSNR value of the heterogeneous phantom (PVAL/H) was superior to those of the less dense (PVAL/E), dense (PVAL), and extremely dense (PVAL/G) phantoms for all the tested filters. The results of this work agree with the high contrast recorded by the original image of PVAL/H.

Learnable DoG convolutional filters for microcalcification detection

Difference of Gaussians (DoG) convolutional filters are one of the earliest image processing methods employed for detecting microcalcifications on mammogram images before machine and deep learning methods became widespread. DoG is a blob enhancement filter that consists in subtracting one Gaussian-smoothed version of an image from another less Gaussian-smoothed version of the same image. Smoothing with a Gaussian kernel suppresses high-frequency spatial information, thus DoG can be regarded as a band-pass filter. However, due to their small size and overimposed breast tissue, microcalcifications vary greatly in contrast-to-noise ratio and sharpness. This makes it difficult to find a single DoG configuration that enhances all microcalcifications. In this work, we propose a convolutional network, named DoG-MCNet, where the first layer automatically learns a bank of DoG filters parameterized by their associated standard deviations. We experimentally show that when employed for microcalcification detection, our DoG layer acts as a learnable bank of band-pass preprocessing filters and improves detection performance by 4.86% AUFROC over baseline MCNet and 1.53% AUFROC over state-of-the-art multicontext ensemble of CNNs.

Stepwise Implementation of 2D Synthesized Screening Mammography and Its Effect on Stereotactic Biopsy of Microcalcifications.

Information evaluating the efficacy of 2D synthesized mammography (2Ds) reconstructions in microcalcification detection is limited. This study used stereotactic biopsy data for microcalcifications to evaluate the stepwise implementation of 2Ds in screening mammography. The study aim was to identify whether 2Ds + digital breast tomosynthesis (DBT) is non-inferior to 2D digital mammography (2DM) + 2Ds + DBT, 2DM + DBT, and 2DM in identifying microcalcifications undergoing further diagnostic imaging and stereotactic biopsy. Retrospective stereotactic biopsy data were extracted following 151,736 screening mammograms of healthy women (average age, 56.3 years; range, 30-89 years), performed between 2012 and 2019. The stereotactic biopsy data were separated into 2DM, 2DM + DBT, 2DM + 2Ds + DBT, and 2Ds + DBT arms and examined using Fisher's exact test to compare the detection rates of all cancers, invasive cancers, DCIS, and ADH between modalities for patients undergoing stereotactic biopsy of microcalcifications. No statistical significance in cancer detection was seen for 2Ds + DBT among those calcifications that underwent stereotactic biopsy when comparing the 2Ds + DBT to 2DM, 2DM + DBT, and 2DM + 2Ds + DBT imaging combinations. These data suggest that 2Ds + DBT is non-inferior to 2DM + DBT in detecting microcalcifications that will undergo stereotactic biopsy.

Enhancing Detection of Microcalcifications using FADHECAL for Early Stage Breast Cancer

Microcalcifications (MCCs) are reliable early signs of breast cancer. However, the small size of calcifications and low radiation factors used in digital mammograms cause low and poor quality mammogram images in detecting MCCs. This paper presents an image enhancement technique called Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) to enhance the details of MCCs in mammogram images by reducing the image noise while conserving contrast and brightness. A total of 23 mammogram images with MCCs were retrieved from the Mammographic Image Analysis Society’s database. The enhancement performance of FADHECAL was compared with Recursive Mean-Separate Histogram Equalization, Histogram Equalization and Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization. Image quality measurement tools of absolute mean brightness error (AMBE), structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used. The results showed that FADHECAL had the most superior results among other enhancement techniques, with 6.302 of AMBE, 20.453 of PSNR and 0.851 of SSIM. The proposed FADHECAL exhibited a high accuracy of 91.30% for the detection of MCCs. Hence, FADHECAL can be used as an ideal tool for identifying MCCs in early-stage breast cancer.

FUZZY WEIGHTED MEDIAN FILTER WITH UNSHARP MASKING FOR ENHANCEMENT OF DBT IMAGES IN BREAST CANCER DETECTION

Breast cancer survival rates can be increased by providing early treatment to patients; thereby, microcalcification detection is critical because microcalcifications are an early sign of breast cancer. The visibility of microcalcifications can be improved by using Digital Breast Tomosynthesis (DBT) images, which have been shown to improve the overlapping issue in mammograms. However, since DBT screening techniques generate blurry artefacts and noise, this study proposes a DBT image enhancement procedure. As a result, this study indicated an enhancement method based on Non-Linear Unsharp Masking filters (NLUM). A filter, such as the Median Filter in conventional NLUM, is required to complete the non-linear element in the algorithm. Other researchers have previously proposed and demonstrated the Fuzzy Weighted Median Filter (FWMF) to improve medical images; thus, these filters can be adapted to the NLUM and replaced with the conventional filter. Following that, the enhancement process's performance will be evaluated using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). When compared to the Median Filter, the results show that the FWMF is the best filter to use in NLUM and successfully enhances DBT images with MSE and PSNR averages of 0.0171 and 67.0574, respectively.

Comparison between cone-beam breast-CT and full-field digital mammography for microcalcification detection depending on breast density.

The purpose of this study was to evaluate the impact of breast density on the diagnostic performance of cone-beam breast-CT (CBBCT) in comparison to full-field digital mammography (FFDM) for the detection of microcalcifications. This retrospective IRB-approved study was conducted between December 2015 and March 2017 and enrolled 171 women with Breast Imaging Reporting and Data System category 4 or 5 lesions on FFDM and additional CBBCT; 56 of which were ineligible. The inclusion was restricted to 83 women (90 breasts, 90 lesions) with microcalcifications. All lesions underwent histology or were monitored by FFDM and a clinical examination at least 2 years after enrollment. Two breast radiologists independently read each data set twice. Sensitivity, specificity and area under the curve were compared between the modalities. Thirty-two breasts (35.5%) were grouped as non-dense breasts (American College of Radiology types a/b) and 58 breasts (64.5%) as dense breasts (American College of Radiology types c/d). Histopathological assessment was performed in 61 of 90 breast lesions (32 malignant, 1 high-risk and 28 benign). Area under the curve was larger for FFDM than for CBBCT (P = .085). The sensitivity was significantly higher for FFDM compared to CBBCT (P = .009). The specificity showed no significant differences comparing FFDM (both readers: 0.62) versus CBBCT (reader 1: 0.76, reader 2: 0.60; P = .192). Inter-observer-reliability on BI-RADS readings was almost perfect for FFDM and moderate for CBBCT (κ = 0.84, κ = 0.54, respectively). Intra-observer agreement was substantial to almost perfect for both methods and readers. Compared with FFDM, CBBCT demonstrated non-comparable results for microcalcification detection in dense and non-dense breasts.

Artificial intelligence-based diagnosis of breast cancer by mammography microcalcification

Mammography is the mainstream imaging modality used for breast cancer screening. Identification of microcalcifications associated with malignancy may result in early diagnosis of breast cancer and aid in reducing the morbidity and mortality associated with the disease. Computer-aided diagnosis (CAD) is a promising technique due to its efficiency and accuracy. Here, we demonstrated that an automated deep-learning pipeline for microcalcification detection and classification on mammography can facilitate early diagnosis of breast cancer. This technique can not only provide the classification results of mammography, but also annotate specific calcification regions. A large mammography dataset was collected, including 4,810 mammograms with 6,663 microcalcification lesions based on biopsy results, of which 3,301 were malignant and 3,362 were benign. The system was developed and tested using images from multiple centers. The overall classification accuracy values for discriminating between benign and malignant breasts were 0.8124 for the training set and 0.7237 for the test set. The sensitivity values of malignant breast cancer prediction were 0.8891 for the training set and 0.7778 for the test set. In addition, we collected information regarding pathological sub-type (pathotype) and estrogen receptor (ER) status, and we subsequently explored the effectiveness of deep learning-based pathotype and ER classification. Automated artificial intelligence (AI) systems may assist clinicians in making judgments and improve their efficiency in breast cancer screening, diagnosis, and treatment.