Digital Mammography (DM) vs. Dynamic Contrast Enhancement-Magnetic Resonance Imaging (DCE-MRI) in Microcalcifications Assessment: A Radiological-Pathological Comparison.

The aim of this study was to compare the characteristics of breast microcalcification on digital mammography (DM) with the histological and molecular subtypes of breast cancer and to identify the predictive value of DM and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing microcalcifications for radiologic-pathologic correlation. We relied on our prospectively maintained database of suspicious microcalcifications on DM, from which data were retrospectively collected between January 2020 and April 2023. We enrolled 158 patients, all of whom were subjected to biopsy. Additionally, 63 patients underwent breast DCE-MRI. Microcalcifications with a linear branched morphology were correlated with malignancies (p < 0.001), among which an association was highlighted between triple negatives (TNs) and segmental distribution (p < 0.001). Amorphous calcifications were correlated with atypical ductal hyperplasia (ADH) (p = 0.013), coarse heterogeneous (p < 0.001), and fine-pleomorphic (p = 0.008) with atypical lobular hyperplasia (ALH) and fine pleomorphic (p = 0.009) with flat epithelial atypia (FEA). Regarding DCE-MRI, no statistical significance was observed between non-mass lesions and ductal carcinoma in situ (DCIS). Concerning mass lesions, three were identified as DCIS and five as invasive ductal carcinoma (IDC). In conclusion, microcalcifications assessed in DM exhibit promising predictive characteristics concerning breast lesion subtypes, leading to a reduction in diagnostic times and further examination costs, thereby enhancing the clinical management of patients.

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

Purpose: 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. Approach: 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 0.17 mm 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. Results: Prewhitening, nonprewhitening, and nonprewhitening with eye filter numerical observers indicate that the combination of 0.525 to 0.6 mm thick scintillator, 70 kV, and 0.25 to 0.4 mm added copper filtration maximized the detectability index at a mean glandular dose (MGD) of 4.5 mGy. Conclusion: 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.

Assessing the malignancy of suspicious breast microcalcifications: the role of contrast enhanced mammography.

PURPOSE: To assess the role of contrast-enhanced mammography (CEM) in predicting the malignancy of breast calcifications. MATERIAL AND METHODS: We retrospectively evaluated patients with suspicious calcifications (BIRADS 4) who underwent CEM and stereotactic vacuum-assisted biopsy (VAB) at our institution. We assessed the sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) of CEM in predicting malignancy of microcalcifications with a 95% confidence interval; we performed an overall analysis and a subgroup analysis stratified into group A-low risk (BIRADS 4a) and group B-medium/high risk (BIRADS 4b-4c). We then evaluated the correlation between enhancement and tumour proliferation index (Ki-67) for all malignant lesions. RESULTS: Data from 182 patients with 184 lesions were collected. Overall the SE of CEM in predicting the malignancy of microcalcifications was 0.70, SP was 0.85, the PPV was 0.82, the NPV was 0.76 and AUC was 0.78. SE in group A was 0.89, SP was 0.89, PPV was 0.57, NPV was 0.98 and AUC was 0.75. SE in group B was 0.68, SP was 0.80, PPV was 0.87, NPV was 0.57 and AUC was 0.75. Among malignant microcalcifications that showed enhancement (N = 52), 61.5% had Ki-67 ≥ 20% and 38.5% had low Ki-67 values. Among the lesions that did not show enhancement (N = 22), 90.9% had Ki-67 < 20% and 9.1% showed high Ki-67 values 20%. CONCLUSIONS: The absence of enhancement can be used as an indicative parameter for the absence of disease in cases of low-suspicious microcalcifications, but not in intermediate-high suspicious ones for which biopsy remains mandatory and can be used to distinguish indolent lesions from more aggressive neoplasms, with consequent reduction of overdiagnosis and overtreatment.

The interplay of collagen, macrophages, and microcalcification in atherosclerotic plaque cap rupture mechanics.

The rupture of an atherosclerotic plaque cap overlying a lipid pool and/or necrotic core can lead to thrombotic cardiovascular events. In essence, the rupture of the plaque cap is a mechanical event, which occurs when the local stress exceeds the local tissue strength. However, due to inter- and intra-cap heterogeneity, the resulting ultimate cap strength varies, causing proper assessment of the plaque at risk of rupture to be lacking. Important players involved in tissue strength include the load-bearing collagenous matrix, macrophages, as major promoters of extracellular matrix degradation, and microcalcifications, deposits that can exacerbate local stress, increasing tissue propensity for rupture. This review summarizes the role of these components individually in tissue mechanics, along with the interplay between them. We argue that to be able to improve risk assessment, a better understanding of the effect of these individual components, as well as their reciprocal relationships on cap mechanics, is required. Finally, we discuss potential future steps, including a holistic multidisciplinary approach, multifactorial 3D in vitro model systems, and advancements in imaging techniques. The obtained knowledge will ultimately serve as input to help diagnose, prevent, and treat atherosclerotic cap rupture.

Mammographic assessment of breast density as a tool for predicting the response to neoadjuvant therapy in breast cancer patients.

Background and aims: Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer-related death among women worldwide. For locally advanced diseases and high-risk tumors, neoadjuvant therapy (NAT) is the treatment of choice. Some studies show that mammographic density (MD) tumor margins and the presence of microcalcifications play a prognostic role in BC patients. Hence, the objective of this retrospective study was to assess if MD could predict the response to NAT among different molecular subtypes of BC patients undergoing NAT at The "Prof. Dr I. Chiricuta" Oncology Institute of Cluj-Napoca, Romania (IOCN). Furthermore, the association between MD, tumor margins and the presence of microcalcifications with clinico-pathological data was analyzed. Methods: Eighty-four breast cancer patients diagnosed and treated at IOCN were included in this study. The morphological characteristics of the tumors were framed according to the BIRADS lexicon. The presence or absence of microcalcifications was also assessed. First, the significance of associations between breast density, margins and microcalcifications and clinico-pathological parameters of the patients were tested with Fisher or Fisher-Freeman-Halton Exact Test. Next, using multinomial logistic regression, we modelled the associations between the pathological response measured by Miller Payne and Residual cancer burden (RCB) systems and the BI-RADS. Variables having significant univariate tests were selected as candidates for the multivariable analysis (adjusted model). Results: Breast densities were significantly associated with the age of the patients (p=0.01), number of positive lymph nodes (p=0.037), margins (p=0.002) and combined categories of Miller-Payne (p=0.034) and RCB pathological response (p=0.021). Margins was significantly associated with ki67 proliferation index (p=0.029), estrogen receptor (ER) (p=0.007), progesterone receptor (PR) (p=0.019), molecular subtype (p<0.001) and the number of clinically observed positive lymph nodes at diagnosis (p=0.019). Conclusions: In our cohort, BC patients with lower MD had higher odds of achieving pCR following NAT, suggesting the role of MD as a clinical prognostic marker. Larger multicenter studies are warranted to validate the prognostic value of MD, which could aid in patients stratification based on their likelihood to respond to NAT.

Upgrade rate to malignancy of uncertain malignant potential breast lesions (B3 lesions) diagnosed on vacuum-assisted biopsy (VAB) in screen detected microcalcifications: Analysis of 366 cases from a single institution.

PURPOSE: We retrospectively investigated clinical, radiological, and pathological features of B3 lesions associated with the risk of subsequent upgrade to malignancy. METHODS: We included consecutive vacuum-assisted biopsies (VABs) performed during 2011-2020 on suspicious microcalcifications not associated with other radiological signs diagnosed as B3 lesions and followed by surgical excision (SE) with definitive histological examination. Multiple logistic regression models were fitted to identify independent predictors of malignancy. RESULTS: Out of the 366 B3 lesions included, 56 (15.3 %, 95 % CI 11.8-19.4 %) had upgraded to malignancy at SE: of these, 42/366 (11.5 %, 95 % CI 8.4-15.2 %) and 14/366 (3.8 %, 95 % CI 2.1-6.3 %) were in situ and invasive carcinoma, respectively. At univariate analysis, variables positively associated with upgrade to malignancy were age ≥ 60 years (p = 0.008), mixed morphology (p = 0.018), scattered distribution (p = 0,001), extension of microcalcifications > 10 mm (p = 0.001), and mixed B3 lesion (p = 0.017). Among B3 subtypes, the highest rates of upgrade were observed for AIDEP, LCIS/LIN2, FEA + AIDEP, FEA + LCIS/LIN2, and FEA + AIDEP + LCIS/LIN2 (24.6 %, 21.4 %, 25.3 %, 20.0 % and 40.0 % respectively), while FEA and ALH/LIN1 had a lower rates of upgrade (7.5 % and 3.7 %, respectively). Multiple logistic regression analysis confirmed as risk factors older age (p = 0.029), larger extension (p = 0.001) and mixed morphology (p = 0.007) of microcalcifications, AIDEP (p = 0.011) among pure B3 lesions, and FEA + AIDEP (p = 0.001) and FEA + AIDEP + LCIS/LIN2 (p = 0.037) among mixed B3 lesions. CONCLUSIONS: Based on our findings, vacuum-assisted excision is reasonable as definitive management for FEA and ALH/LIN1, while SE should remain the mainstay of treatment for AIDEP and LCIS/LIN2, whose upgrade rates are too high to safely recommend VAE.

Deep learning performance for detection and classification of microcalcifications on mammography.

BACKGROUND: Breast 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. METHODS: Three 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). RESULTS: The 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. CONCLUSIONS: The developed AI models accurately detect and characterize microcalcifications on mammography. RELEVANCE STATEMENT: AI-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.

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

Purpose: 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. Approach: 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. Results: 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). Conclusions: 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 ≲240 µm.

Value of mammographic microcalcifications and MRI-enhanced lesions in the evaluation of residual disease after neoadjuvant therapy for breast cancer.

Background: Microcalcifications persist even if a patient with breast cancer achieves pathologic complete response (pCR) as confirmed by surgery after neoadjuvant treatment (NAT). In practice, surgeons tend to remove all the microcalcifications. This study aimed to explore the correlation between changes in the extent of microcalcification after NAT and pathological tumor response and compare the accuracy of mammography (MG) and magnetic resonance imaging (MRI) in predicting the size of residual tumors. Methods: This was a retrospective study which included a consecutive series of patients in Guangdong Provincial People's Hospital. Between January 2010 and January 2020, 127 patients with breast cancer and Breast Imaging Reporting and Data System (BI-RADS) 4-5 microcalcifications were included in this study. The maximum diameter of the microcalcifications on MG and lesion enhancement on MRI pre- and post-NAT were measured. The correlations between the changes in residual microcalcifications on MG and pCR were analyzed. Intraclass correlation coefficients (ICCs) were computed between the extent of the residual microcalcifications, residual enhancement, and residual tumor size. Results: There were no statistically significant differences in the changes in microcalcifications after NAT according to the RECIST criteria on MRI (P=0.09) and Miller-Payne grade (P=0.14). MRI showed a higher agreement than did residual microcalcifications on MG in predicting residual tumor size (ICC: 0.771 vs. 0.097). Conclusions: MRI is more accurate for evaluating residual tumor size in breast cancer. In our study, the extent of microcalcifications on MG after NAT had nearly no correlation with the pathological size of the residual tumor. Therefore, residual tumors with microcalcifications may not necessarily be a contraindication to breast-conserving surgery.

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

RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: These data suggest that 2Ds + DBT is non-inferior to 2DM + DBT in detecting microcalcifications that will undergo stereotactic biopsy.

Detection of microcalcifications using nonlinear beamforming techniques.

OBJECTIVE: 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. METHODS: 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. DISCUSSION: 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. CONCLUSIONS: Statistically, the results did support the hypothesis that NSI would increase the number of MCs detected in the rat tumors.

The value of DCE- MRI of the breast as a diagnostic tool in assessing amorphous calcifications in screening mammography.

Purpose: To evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging in differentiating benign and malignant amorphous calcifications. Methods: This study included 193 female patients with 197 suspicious amorphous calcifications detected on screening mammography. The patients' demographics, clinical follow-up, imaging, and pathology outcomes were reviewed, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DCE-MRI were calculated. Results: Of 197 lesions (193 patients) included in the study, 50 (25.4%) were histologically proved to be malignant. DCE-MRI based on breast imaging report and diagnosis system (BI-RADS) had a sensitivity of 94.4%, specificity of 85.7%, PPV of 69.1%, and NPV of 97.7% for the detection of malignant amorphous calcifications. Notably, diagnosis solely based on the presence or absence of DCE-MRI enhancement showed the same sensitivity but significantly decreased specificity (44.8%, p < 0.001) and PPV (44.8%, p < 0.001). In patients with a minimal or mild degree of background parenchymal enhancement (BPE), the sensitivity, specificity, PPV, and NPV increased to 100%, 90.6%, 78.6%, and 100%, respectively. However, in patients with a moderate degree of BPE, MRI resulted in three false negatives of ductal carcinoma in situ (DCIS). Overall, the addition of DCE-MRI detected all invasive lesions and could decrease unnecessary biopsy by 65.5%. Conclusion: DCE-MRI based on BI-RADS has the potential to improve the diagnosis of suspicious amorphous calcifications and avoid unnecessary biopsy, especially for those with low-degree BPE.

Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images.

BACKGROUND: The noise in digital breast tomosynthesis (DBT) includes x-ray quantum noise and detector readout noise. The total radiation dose of a DBT scan is kept at about the level of a digital mammogram but the detector noise is increased due to acquisition of multiple projections. The high noise can degrade the detectability of subtle lesions, specifically microcalcifications (MCs). PURPOSE: We previously developed a deep-learning-based denoiser to improve the image quality of DBT. In the current study, we conducted an observer performance study with breast radiologists to investigate the feasibility of using deep-learning-based denoising to improve the detection of MCs in DBT. METHODS: We have a modular breast phantom set containing seven 1-cm-thick heterogeneous 50% adipose/50% fibroglandular slabs custom-made by CIRS, Inc. (Norfolk, VA). We made six 5-cm-thick breast phantoms embedded with 144 simulated MC clusters of four nominal speck sizes (0.125-0.150, 0.150-0.180, 0.180-0.212, 0.212-0.250 mm) at random locations. The phantoms were imaged with a GE Pristina DBT system using the automatic standard (STD) mode. The phantoms were also imaged with the STD+ mode that increased the average glandular dose by 54% to be used as a reference condition for comparison of radiologists' reading. Our previously trained and validated denoiser was deployed to the STD images to obtain a denoised DBT set (dnSTD). Seven breast radiologists participated as readers to detect the MCs in the DBT volumes of the six phantoms under the three conditions (STD, STD+, dnSTD), totaling 18 DBT volumes. Each radiologist read all the 18 DBT volumes sequentially, which were arranged in a different order for each reader in a counter-balanced manner to minimize any potential reading order effects. They marked the location of each detected MC cluster and provided a conspicuity rating and their confidence level for the perceived cluster. The visual grading characteristics (VGC) analysis was used to compare the conspicuity ratings and the confidence levels of the radiologists for the detection of MCs. RESULTS: The average sensitivities over all MC speck sizes were 65.3%, 73.2%, and 72.3%, respectively, for the radiologists reading the STD, dnSTD, and STD+ volumes. The sensitivity for dnSTD was significantly higher than that for STD (p < 0.005, two-tailed Wilcoxon signed rank test) and comparable to that for STD+. The average false positive rates were 3.9 ± 4.6, 2.8 ± 3.7, and 2.7 ± 3.9 marks per DBT volume, respectively, for reading the STD, dnSTD, and STD+ images but the difference between dnSTD and STD or STD+ did not reach statistical significance. The overall conspicuity ratings and confidence levels by VGC analysis for dnSTD were significantly higher than those for both STD and STD+ (p ≤ 0.001). The critical alpha value for significance was adjusted to be 0.025 with Bonferroni correction. CONCLUSIONS: This observer study using breast phantom images showed that deep-learning-based denoising has the potential to improve the detection of MCs in noisy DBT images and increase radiologists' confidence in differentiating noise from MCs without increasing radiation dose. Further studies are needed to evaluate the generalizability of these results to the wide range of DBTs from human subjects and patient populations in clinical settings.

The Diagnostic Role of Shear Wave Elastography and Superb Microvascular Imaging in the Evaluation of Suspicious Microcalcifications.

OBJECTIVES: The aim of this study was to investigate the role of superb microvascular imaging (SMI) and shear wave elastography (SWE) in the prediction of malignancy and invasiveness of isolated microcalcifications (MC) that can be visualized by ultrasonography (US). MATERIAL AND METHODS: Sixty-seven women with MC, who were considered suspicious on mammography were evaluated. Only those lesions that could be visualized by US and presented as non-mass lesion were included. They were evaluated by B-mode US, SMI, and SWE before US-guided core-needle biopsy. B-mode US, SMI (vascular index (SMIvi)), and SWE (E-mean, E-ratio) findings were compared with histopathologic features. RESULTS: Pathology confirmed 45 malignant (21 invasive and 24 in situ carcinomas) and 22 benign lesions. There was a statistically significant difference between malignant and benign groups in terms of size (P = .015), distortion (P = .028), cystic component (P < .001), E-mean (P < .001), E-ratio (P < .001), and SMIvi (P = .006). For differentiation of invasiveness E-mean (P = .002), E-ratio (P = .002), and SMIvi (P = .030) were statistically significant. According to ROC analysis E-mean (cut-off point at 38 kPa) was the most sensitive (78%) and the most specific (95%) value among four numeric parameters (size, SMI, E-mean, and E-ratio) with AUC = 0.895, PPV = 97%, and NPV = 68% in detecting malignancy. In the evaluation of invasiveness, the most sensitive (71.4%) method was SMI (cut-off point at 3.4) and the most specific (72%) method was E-mean (cut-off point at 91.5 kPa). CONCLUSION: Our study shows that adding SWE and SMI to the sonographic evaluation of MC would be an advantage for US-guided biopsy. Including suspicious areas according to SMI and SWE in the sampling area can help target the invasive part of the lesion and avoid underestimation of core biopsy.

A cohort study of mammography-guided vacuum-assisted breast biopsy in patients with compressed thin breasts (≦ 3 cm).

OBJECTIVES: In the women with compressed thin thickness (≦ 3 cm), mammographic guiding vacuum-assist breast biopsy (MG-VABB) is a technical challenge. We herein report their performance of MG-VABB on suspicious microcalcification by modern mammography. METHODS: We retrospectively reviewed the consecutive MG-VABB in our hospital from February 2019 to January 2021. All the patients received biopsy because of suspicious microcalcifications discovered by mammography and had at least one-year post-biopsy follow-up. RESULTS: We reviewed 745 consecutive patients revealing 195 with compressed thin breasts ≦ 3 cm (mean age: 50.12 ± 7.0; breast thickness: 24.99 mm range 11.6-30 mm). Of the 191 patients received biopsy, the microcalcification retrieval rate was 97.9%. Using the half-open notch biopsy or horizontal needle approach, the biopsies were technically achieved in 30.4% and 9.4% of patients respectively. Regarding to the gold standard of surgicohistology, the cancer sensitivities was 88.46% and the atypia upgrade rate was 16.67%. There was no statistical difference of the procedure time between stereotactic guided and tomosynthesis guided. CONCLUSIONS: The modern MG-VABB has technically improve the performance of biopsy to the patients with compressed thin breasts (≦ 3 cm), revealing approximate results to those breasts > 3 cm. The diagnosis helps the management of suspicious microcalcifications discovered by mammography.

DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms.

BACKGROUND AND OBJECTIVE: Breast cancer is the world's most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalised treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa, a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases. METHODS: DeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in a novel automated and accurate pipeline easily customisable to meet radiologists' needs. RESULTS: The proposed segmentation and classification algorithms achieve an area under the ROC curve of 0.95 and 0.89 respectively. Compared to previously proposed works, this method does not require high performance computational resources and provides a visual explanation of the final classification results. CONCLUSION: To conclude, we designed a novel fully automated pipeline for detection and classification of breast microcalcifications. We believe that the proposed system has the potential to provide a second opinion in the diagnosis process giving the clinicians the opportunity to quickly visualise and inspect relevant imaging characteristics. In the clinical practice the proposed decision support system could help reduce the rate of misclassified lesions and consequently the number of unnecessary biopsies.

Utility of detection of breast calcifications with integrated real-time radiography system (IRRS) during digital breast tomosynthesis (DBT)-guided vacuum assisted biopsy (VAB): initial single-center experience.

PURPOSE: To determine whether the presence of calcifications in specimens collected during stereotactic-guided vacuum-assisted breast biopsies (VABB) is sufficient to ascertain their adequacy for final diagnosis at pathology. MATERIALS AND METHODS: Digital breast tomosynthesis (DBT)-guided VABBs were performed on 74 patients with calcifications as target. Each biopsy consisted of the collection of 12 samplings with a 9-gauge needle. This technique was integrated with a real-time radiography system (IRRS) which allowed the operator to determine whether calcifications were included in the specimens at the end of each of the 12 tissue collections through the acquisition of a radiograph of every sampling. Calcified and non-calcified specimens were separately sent to pathology and evaluated. RESULTS: A total of 888 specimens were retrieved, 471 containing calcifications and 417 without. In 105 (22.2%) samples out of 471 with calcifications cancer was detected, while the remaining 366 (77.7%) were non-cancerous. Out of 417 specimens without calcifications 56 (13.4%) were cancerous, whereas 361 (86.5%) were non-cancerous. Seven hundred and twenty-seven specimens out of all 888 were cancer-free (81.8%, 95%CI 79-84%). CONCLUSION: Although there is a statistical significative difference between calcified and non-calcified samples and the detection of cancer (p < 0.001), our study shows that the sole presence of calcifications in the specimens is not sufficient to determine their adequacy for final diagnosis at pathology because non-calcified samples can be cancerous and vice-versa. Ending biopsies when calcifications are first detected through IRRS could lead to false negative results.

Lymph node microcalcifications after neoadjuvant chemotherapy: A therapeutic response marker.

Lymph node microcalcifications are rare events, and when they are accompanied by neoplasia, they usually seem to be associated with a metastatic condition. We present a case of a patient with breast cancer and lymph node microcalcifications undergoing neoadjuvant chemotherapy (NCT). A change in the calcification pattern towards becoming coarse was observed. Calcification represented a marker of axillary disease, and it was resected after NCT. This is the first report of a patient with lymph node microcalcification undergoing NCT. We observed a change in the calcification format, which facilitated lymph node sentinel identification. Pathological evaluation indicated metastatic disease.

Size and proximity of micro-scale hard-inclusions increase the risk of rupture in fibroatheroma-like laboratory models.

Increased mechanical stresses of the fibroatheroma cap tissue is a crucial risk factor on the pathogenesis of asymptomatic coronary artery disease events. Moreover, both numerical and analytical studies have shown that microcalcifications (µCalcs) located in the fibrous cap can multiply the cap tissue stress by a factor of 2-7. This stress amplification depends on the ratio of the gap between particles (h) and their diameter (D) when they are aligned along the tensile axis. However, the synergistic effect of cap stiffness and uCalcs on the ultimate stress and rupture risk of the atheroma cap has not been fully investigated. In this context, we studied the impact of micro-beads (µBeads) of varying diameters and concentration on the rupture of silicone-based laboratory models mimicking human fibroatheroma caps of different stiffness (shear moduli µsoft = 40 kPa, µstiff = 400 kPa) and thickness (650 µm and 100 µm). A total of 145 samples were tested under uniaxial tension up to failure and the true stress and strain response of each model was derived by means of Digital Image Correlation (DIC). Before testing, samples were scanned using high-resolution Micro-CT, to perform morphometry analyses of the embedded micro-beads and determine the number of closely spaced particles (h/D<0.5). The micro-beads structural and spatial features were then compared to the case of 29 non-ruptured human atheroma fibrous caps presenting µCalcs. Samples with and without µBeads exhibited a distinct hyperelastic behavior typical of arterial tissues. Regardless of the sample stiffness, large µBeads (>80 µm) significantly reduced the ultimate tensile stress (UTS) of the thick cap models with the effect being more pronounced as the particle diameter increases. Stiff models experienced early rupture in the presence of µBeads with 40 µm diameter. Smaller µBeads of 6 µm and 20 µm didn't affect the ultimate strength of the thick cap models. However, when 6 µm µBeads where introduced in thinner cap models, we observed more than 20% drop in UTS. Increasing the µBeads concentration was also positively correlated with lower stresses at rupture as more clusters formed resulting in lower values of h/D. Morphometry analyses of cap models and human atheroma show that the 6 µm µBeads groups present very similar size distributions to µCalcs and that human µCalcs occupy an average volume ratio of 0.79 ± 0.85%. Our results clearly capture the influence of µBeads on the rupture threshold of a vascular tissue mimicking material. This effect appears to be dependent on the µBeads-to-cap thickness size ratio as well as their proximity. These findings support previous numerical and analytical studies suggesting that µCalcs located within the fibroatheroma cap may be responsible for significantly increasing the risk of cap rupture that precedes myocardial infarction and sudden death.

Contrast-Enhanced Spectral Mammography in the Evaluation of Breast Microcalcifications: Controversies and Diagnostic Management.

The aim of this study was to evaluate the diagnostic performance of contrast-enhanced spectral mammography (CESM) in predicting breast lesion malignancy due to microcalcifications compared to lesions that present with other radiological findings. Three hundred and twenty-one patients with 377 breast lesions that underwent CESM and histological assessment were included. All the lesions were scored using a 4-point qualitative scale according to the degree of contrast enhancement at the CESM examination. The histological results were considered the gold standard. In the first analysis, enhancement degree scores of 2 and 3 were considered predictive of malignity. The sensitivity (SE) and positive predictive value (PPV) were significative lower for patients with lesions with microcalcifications without other radiological findings (SE = 53.3% vs. 82.2%, p-value < 0.001 and PPV = 84.2% vs. 95.2%, p-value = 0.049, respectively). On the contrary, the specificity (SP) and negative predictive value (NPV) were significative higher among lesions with microcalcifications without other radiological findings (SP = 95.8% vs. 84.2%, p-value = 0.026 and NPV = 82.9% vs. 55.2%, p-value < 0.001, respectively). In a second analysis, degree scores of 1, 2, and 3 were considered predictive of malignity. The SE (80.0% vs. 96.8%, p-value < 0.001) and PPV (70.6% vs. 88.3%, p-value: 0.005) were significantly lower among lesions with microcalcifications without other radiological findings, while the SP (85.9% vs. 50.9%, p-value < 0.001) was higher. The enhancement of microcalcifications has low sensitivity in predicting malignancy. However, in certain controversial cases, the absence of CESM enhancement due to its high negative predictive value can help to reduce the number of biopsies for benign lesions.