Brief communication: The current status of contrast-enhanced mammography in breast imaging.

Improvising and developing state of the art techniques for breast cancer detection have always been an area of great interest in the field of imaging. Adding intravenous contrast to any imaging study, is well-known to increase the sensitivity and specificity of detection of a pathological process, especially in the setting of neoplasia secondary to tumor neoangiogenesis. Contrast enhanced MRI is known to be highly sensitive breast cancer screening tool till date, however, has been limited by long scan times, claustrophobia experienced by some women and high false positive findings. Despite continued advances in digital mammography technique, significant limitations have always been experienced in detection of small cancers especially in the setting of dense breast parenchyma. Implementing dual energy subtraction technique to digital mammography, made contrast enhanced mammography a viable technique to improve cancer detection. We aim to discuss the status of contrast enhanced mammography in this brief communication, emphasizing technical background, image acquisition, clinical applications, and future directions.

A deep learning approach for virtual contrast enhancement in Contrast Enhanced Spectral Mammography.

Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first requires intravenously administering an iodinated contrast medium. Then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image. The two scans are combined to get a recombined image showing contrast enhancement. Despite CESM diagnostic advantages for breast cancer diagnosis, the use of contrast medium can cause side effects, and CESM also beams patients with a higher radiation dose compared to standard mammography. To address these limitations, this work proposes using deep generative models for virtual contrast enhancement on CESM, aiming to make CESM contrast-free and reduce the radiation dose. Our deep networks, consisting of an autoencoder and two Generative Adversarial Networks, the Pix2Pix, and the CycleGAN, generate synthetic recombined images solely from low-energy images. We perform an extensive quantitative and qualitative analysis of the model's performance, also exploiting radiologists' assessments, on a novel CESM dataset that includes 1138 images. As a further contribution to this work, we make the dataset publicly available. The results show that CycleGAN is the most promising deep network to generate synthetic recombined images, highlighting the potential of artificial intelligence techniques for virtual contrast enhancement in this field.

State-of-the-art for contrast-enhanced mammography.

Contrast-enhanced mammography (CEM) is an emerging breast imaging technology with promise for breast cancer screening, diagnosis, and procedural guidance. However, best uses of CEM in comparison with other breast imaging modalities such as tomosynthesis, ultrasound, and MRI remain inconclusive in many clinical settings. This review article summarizes recent peer-reviewed literature, emphasizing retrospective reviews, prospective clinical trials, and meta-analyses published from 2020 to 2023. The intent of this article is to supplement prior comprehensive reviews and summarize the current state-of-the-art of CEM.

Meteorology-driven PM2.5 interannual variability over East Asia.

Atmospheric fine particulate matter (PM2.5) is a human health risk factor, but its ambient concentration depends on both precursor emissions and meteorology. While emission reductions are used to set PM2.5-related health policies, the effect of meteorology is often overlooked. To explore this aspect, we examined PM2.5 interannual variability (IAV) associated with meteorological parameters using the long-term simulation from the Community Earth System Model (CESM1), a global climate-chemistry model, with fixed emissions. The results are subsequently contrasted with the MERRA-2 reanalysis dataset, which inherently considers emission and meteorology effects. Over continental East Asia, the CESM1 domain-average PM2.5 IAV is 6.7 %, mainly attributed to humidity, precipitation, and ventilation variation. The grid-cell PM2.5 IAVs over southern East China are larger, up to 12 % due to the more substantial influence of El Niño-induced meteorological anomalies. Under such climate extreme, sub-regional PM2.5 concentration may occasionally exceed WHO air quality guideline levels despite the compliance of the long-term mean. The simulated PM2.5 IAV over continental East Asia is ~25 % of that derived from the MERRA-2 data, which highlights the influence of both emission and meteorology-driven variations and trends inherent in the latter. Although emission-driven variability is significant to PM2.5 IAV, in remote areas downwind of major source regions in East Asia, North America, and Western Europe, the MERRA-2 data revealed that meteorological variations contributed more to PM2.5 IAV than emission variations. Thus, when setting policies for complying with the WHO PM2.5-related air quality guideline levels, the highest annual PM2.5 associated with climate extremes should be considered instead of that based on average climate conditions.

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.

Radiomic Features Applied to Contrast Enhancement Spectral Mammography: Possibility to Predict Breast Cancer Molecular Subtypes in a Non-Invasive Manner.

We aimed to investigate the association between the radiomic features of contrast-enhanced spectral mammography (CESM) images and a specific receptor pattern of breast neoplasms. In this single-center retrospective study, we selected patients with neoplastic breast lesions who underwent CESM before a biopsy and surgical assessment between January 2013 and February 2022. Radiomic analysis was performed on regions of interest selected from recombined CESM images. The association between the features and each evaluated endpoint (ER, PR, Ki-67, HER2+, triple negative, G2-G3 expressions) was investigated through univariate logistic regression. Among the significant and highly correlated radiomic features, we selected only the one most associated with the endpoint. From a group of 321 patients, we enrolled 205 malignant breast lesions. The median age at the exam was 50 years (interquartile range (IQR) 45-58). NGLDM_Contrast was the only feature that was positively associated with both ER and PR expression (p-values = 0.01). NGLDM_Coarseness was negatively associated with Ki-67 expression (p-value = 0.02). Five features SHAPE Volume(mL), SHAPE_Volume(vx), GLRLM_RLNU, NGLDM_Busyness and GLZLM_GLNU were all positively and significantly associated with HER2+; however, all of them were highly correlated. Radiomic features of CESM images could be helpful to predict particular molecular subtypes before a biopsy.

Contrast-Enhanced Spectral Mammography and tumor size assessment: a valuable tool for appropriate surgical management of breast lesions.

PURPOSE: To compare the accuracy of Contrast-Enhanced Spectral Mammography (CESM), MG, US, and breast MRI in estimating the size of breast lesions requiring surgery. The postoperative histology size of the lesion was used as the gold standard. MATERIAL AND METHODS: Two hundred thirty-three non-benign lesions in 189 patients were included in the analyses. All the selected patients underwent CESM and at least one other conventional diagnostic exam (US, MG, or MRI). Subsequently, all the patients underwent surgery preceded by cytological/histological examination. The largest diameter of the lesion at imaging was measured by a radiologist with more than 10 years' experience and then compared with the size of the lesion in the histological sample at the surgery (gold standard). RESULTS: Among the 233 breast lesions, 196 were evaluated with US, 206 with MG and 160 with MRI. We found no statistically significant differences between size measurements using CESM and MRI compared with the measurements at the surgery (p value 0.63 and 0.51), whereas a significant difference was found for MG and US (p < 0.001). CONCLUSION: CESM is a reliable method for estimating the size of breast lesions: its performance seems superior to US and MG and comparable to MRI.

A Score to Predict the Malignancy of a Breast Lesion Based on Different Contrast Enhancement Patterns in Contrast-Enhanced Spectral Mammography.

Background: To create a predictive score of malignancy of a breast lesion based on the main contrast enhancement features ascertained by contrast-enhanced spectral mammography (CESM). Methods: In this single-centre prospective study, patients with suspicious breast lesions (BIRADS > 3) were enrolled between January 2013 and February 2022. All participants underwent CESM prior to breast biopsy, and eventually surgery. A radiologist with 20 years' experience in breast imaging evaluated the presence or absence of enhancement and the following enhancement descriptors: intensity, pattern, margin, and ground glass. A score of 0 or 1 was given for each descriptor, depending on whether the enhancement characteristic was predictive of benignity or malignancy (both in situ and invasive). Then, an overall enhancement score ranging from 0 to 4 was obtained. The histological results were considered the gold standard in the evaluation of the relationship between enhancement patterns and malignancy. Results: A total of 321 women (median age: 51 years; range: 22−83) with 377 suspicious breast lesions were evaluated. Two hundred forty-nine lesions (66%) have malignant histological results (217 invasive and 32 in situ). Considering an overall enhancement score ≥ 2 as predictive of malignancy, we obtain an overall sensitivity of 92.4%; specificity of 89.8%; positive predictive value of 94.7%; and negative predictive value of 85.8%. Conclusions: Our proposed predictive score on the enhancement descriptors of CESM to predict the malignancy of a breast lesion shows excellent results and can help in early breast cancer diagnosis and in avoiding unnecessary biopsies.

Improved Parameterization for the Size Distribution of Emitted Dust Aerosols Reduces Model Underestimation of Super Coarse Dust.

Aircraft measurement campaigns have revealed that super coarse dust (diameter >10 µm) surprisingly accounts for approximately a quarter of aerosols by mass in the atmosphere. However, most global aerosol models either underestimate or do not include super coarse dust abundance. To address this problem, we use brittle fragmentation theory to develop a parameterization for the emitted dust size distribution that includes emission of super coarse dust. We implement this parameterization in the Community Earth System Model (CESM) and find that it brings the model in good agreement with aircraft measurements of super coarse dust close to dust source regions. However, the CESM still underestimates super coarse dust in dust outflow regions. Thus, we conclude that the model underestimation of super coarse atmospheric dust is in part due to the underestimation of super coarse dust emission and likely in part due to errors in deposition processes.

Diagnostic Accuracy of Contrast-Enhanced, Spectral Mammography (CESM) and 3T Magnetic Resonance Compared to Full-Field Digital Mammography plus Ultrasound in Breast Lesions: Results of a (Pilot) Open-Label, Single-Centre Prospective Study.

Introduction: To assess the diagnostic accuracy of CESM and 3T MRI compared to full-field digital mammography (FFDM), plus US, in the evaluation of advanced breast lesions. Materials and Methods: Consenting women with suspicious findings underwent FFDM, US, CESM and 3T MRI. Breast lesions were histologically assessed, with histology being the gold standard. Two experienced breast radiologists, blinded to cancer status, read the images. Diagnostic accuracy of (1) CESM as an adjunct to FFDM and US, and (2) 3T MRI as an adjunct to CESM compared to FFDM and US, was assessed. Measures of accuracy were sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV). Results: There were 118 patients included along with 142 histologically characterized lesions. K agreement values were 0.69, 0.68, 0.63 and 0.56 for concordance between the gold standard and FFDM, FFDM + US, CESM and MRI, respectively (p < 0.001, for all). K concordance for CESM was 0.81 with FFDM + US and 0.73 with MRI (p value < 0.001 for all). Conclusions: CESM may represent a valuable alternative and/or an integrating technique to MRI in the evaluation of breast cancer patients.

Interannual relationship between displacement and intensity of East Asian jet stream and haze over eastern China in winter.

A recent case study indicated that the weakening of the East Asian subtropical jet was an important cause of the severe haze in North China in the winter of 2015. However, the interannual relationship between two key features, the displacement and the intensity of the East Asian jet stream (EAJS) and the haze days over eastern China (HDEC), remains unclear. Observed data, ERA-Interim reanalysis, and Community Earth System Model Large Ensemble Numerical Simulation(CESM-LENS) were used to investigate the interannual relationship between the EAJS and HDEC during the winter season from 1980 to 2017 and its possible associated atmospheric mechanisms. The results show that the northward movement of the EAJS is conducive to more HDEC by weakening synoptic-scale transient eddy activities (STEA) and baroclinicity, forming an upper-level anticyclonic anomaly over eastern China (EC). The local meteorological conditions (e.g., stronger temperature inversion potential, higher relative humidity, descending motion) are favorable for the accumulation of HDEC, showing consistent variations in more haze in the entire region of EC. The southward movement of the EAJS has the opposite effect. The strong East Asian subtropical jet (weak polar-front jet) could result in the distribution of the meridional dipole with less haze in north EC and more haze in south EC. The mean flow loses energy to the STEA over north EC and increases the baroclinicity, which is favorable for dispersing HDEC. However, the configuration of upper-level cyclonic and low-level southwest wind anomalies that appeared in south EC weakened the STEA, which favored the accumulation of HDEC. The observed results were further verified by CESM-LENS.

Influence of double layer filter on mean glandular dose (MGD) and image quality in low energy image of contrast enhanced spectral mammography (LE-CESM).

INTRODUCTION: The aim of this study was to evaluate mean glandular dose (MGD) and image quality in low energy imaging from contrast-enhanced spectral mammography (CESM) when using double-layer filtration. METHODOLOGY: A dedicated phantom was used to quantitatively estimate the MGD and image quality. The target slab of the phantom consisted of three iodine coins having a concentration of 1.0 mgI/cm3, 2.0 mgI/cm3, 4.0 mgI/cm3, a 100% adipose equivalent coin and a 100% glandular equivalent coin. The phantom was exposed using a semiautomated function at 28 k, 30 kV and 32 kV. MGD. Contrast to noise ratio (CNR) and figure of merit (FOM) were estimated for Mo/Rh, Mo/Rh + Cu, Mo/Rh + Al and Mo/Rh + Cd combinations using three breast equivalent compositions. RESULTS: MGD was reduced up to a maximum of 1.03 mGy from 1.17 mGy for 100% adipose tissue. 1.18 mGy from 1.34 mGy for 50% glandular tissue and 1.39 mGy from 1.72 mGy for the 100% glandular phantom when using double-layer filtration. All of the above-mentioned results were obtained for the 50 mm phantom using 32 kV. CNR and FOM values were not significantly reduced with a double-layer filter when compared to a single-layer filter. CONCLUSION: The present study concluded that Mo/Rh + Cu is the best combination to reduce the MGD significantly when compared to Mo/Rh + Al or Mo/Rh + Cd. Mo/Rh + Cu also achieved optimal image quality when compared to the Mo/Rh single filter combination. IMPLICATIONS OF PRACTICE: The use of a double-layer filter in low energy imaging of CESM results in a significant reduction in MGD without degrading the quality of the image.

Current Status of Contrast Enhanced Mammography: A Comprehensive Review.

OBJECTIVES: The purpose of this article is to provide a detailed and updated review of the physics, techniques, indications, limitations, reporting, implementation and management of contrast enhanced mammography. BACKGROUND: Contrast enhanced mammography (CEM), is an emerging iodine-based modified dual energy mammography technique. In addition to having the same advantages as standard full-field digital mammography (FFDM), CEM provides information regarding tumor enhancement, relying on tumor angiogenesis, similar to dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). This article reviews current literature on CEM and highlights considerations that are critical to the successful use of this modality. CONCLUSION: Multiple studies point to the advantage of using CEM in the diagnostic setting of breast imaging, which approaches that of DCE-MRI.

Artifacts in contrast-enhanced mammography: are there differences between vendors?

PURPOSE: Contrast-Enhanced Mammography (CEM) produces a dual-energy subtracted (DES) image that demonstrates iodine uptake (neovascularity) in breast tissue. We aim to review a range of artifacts on DES images produced using equipment from two different vendors and compare their incidence and subjective severity. METHODS: We retrospectively reviewed CEM studies performed between September 2013 and March 2017 using GE Senographe Essential (n = 100) and Hologic Selenia Dimensions (n = 100) equipment. Artifacts were categorized and graded in severity by a subspecialist breast radiologist and one of two medical imaging technologists in consensus. The incidence of artifacts between vendors was compared by calculating the relative risk, and the severity gradings were compared using a Wilcoxon rank-sum test. RESULTS: Elephant rind, corrugations and the black line on chest wall artifact were seen exclusively in Hologic images. Artifacts such as cloudy fat, negative rim around lesion and white line on pectoral muscle were seen in significantly more Hologic images (p < 0.05) whilst halo, ripple, skin line enhancement, black line on pectoral muscle, bright pectorals, chest wall high-lighting and air gap were seen in significantly more GE images (p < 0.05). The severity gradings for cloudy fat had a significantly higher mean rank in Hologic images (p < 0.001) whilst halo and ripple artifacts had a significantly higher mean rank in GE images (p < 0.001 and p = 0.028 respectively). CONCLUSION: The type, incidence and subjective severity of CEM-specific artifacts differ between vendors. Further research is needed, but differences in algorithms used to produce the DE image are postulated to be a significant contributor.

Does airborne pollen influence COVID-19 outbreak?

The fast spread of SARS-CoV-2 presented a worldwide challenge to public health, economy, and educational system, affecting wellbeing of human society. With high transmission rates, there are increasing evidences of COVID-19 spread via bioaerosols from an infected person. The current review was conducted to examine airborne pollen impact on COVID-19 transmission and to identify the major gaps for post-pandemic research. The study used all key terms to identify revenant literature and observation were collated for the current research. Based on existing literature, there is a potential association between pollen bioaerosols and COVID-19. There are few studies focusing the impact of airborne pollen on SARS-CoV-2, which could be useful to advance future research. Allergic rhinitis and asthma patients were found to have pre-modified immune activation, which could help to provide protection against COVID-19. However, does airborne pollen acts as a potent carrier for SARS-CoV-2 transport, dispersal and its proliferation still require multidisciplinary research. Further, a clear conclusion cannot be drawn due to limited evidence and hence more research is needed to show how pollen bioaerosols could affect virus survivals. The small but growing literature review focuses on searching for every possible answer to provide additional security layers to overcome near future corona-like infectious diseases.

The effectiveness of contrast-enhanced spectral mammography and magnetic resonance imaging in dense breasts.

PURPOSE: Breast cancer is the most common cause of death from neoplastic disease in women. Among all breast anatomy types, glandular type is the most problematic concerning evaluation. While digital mammography still remains the basic diagnostic tool, one must be aware of its limitations in dense breasts. Although magnetic resonance imaging (MRI) has greatly improved sensitivity, its specificity is low. Moreover, there are contraindications for MRI for some patients, so a substitute has been searched for. This study was performed to check if contrast-enhanced spectral mammography (CESM) can be a viable option for patients with dense breasts. MATERIAL AND METHODS: The study involved 121 patients with abnormalities detected on base-line diagnostic imaging (ultrasound or mammography). The patients had subsequent examinations, both CESM and MRI performed within a maximum 2-month time interval. The sensitivity and specificity of both methods in the whole group as well as in specific breast structure types were measured and compared. RESULTS: Contrast enhancement was visible in all 121 cases on MRI, while on CESM lack of enhancement was noted in 13 cases. All of those 13 lesions turned out to be benign. There were 40 (33%) benign and 81 (69%) malignant tumours. The analysed group included 53 (44%) glandular type breast patients, 39 (32%) mixed type, and 29 (23%) fatty type. Although MRI proved to be slightly more effective in dense breasts, both methods showed similar results in the whole study group. CONCLUSION: CESM can be used with confidence in patients with glandular breast type when MRI is not available or there are reported contraindications to MRI.

Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images.

Contrast-enhanced spectral mammography (CESM) is an advanced instrument for breast care that is still operator dependent. The aim of this paper is the proposal of an automated system able to discriminate benign and malignant breast lesions based on radiomic analysis. We selected a set of 58 regions of interest (ROIs) extracted from 53 patients referred to Istituto Tumori "Giovanni Paolo II" of Bari (Italy) for the breast cancer screening phase between March 2017 and June 2018. We extracted 464 features of different kinds, such as points and corners of interest, textural and statistical features from both the original ROIs and the ones obtained by a Haar decomposition and a gradient image implementation. The features data had a large dimension that can affect the process and accuracy of cancer classification. Therefore, a classification scheme for dimension reduction was needed. Specifically, a principal component analysis (PCA) dimension reduction technique that includes the calculation of variance proportion for eigenvector selection was used. For the classification method, we trained three different classifiers, that is a random forest, a naïve Bayes and a logistic regression, on each sub-set of principal components (PC) selected by a sequential forward algorithm. Moreover, we focused on the starting features that contributed most to the calculation of the related PCs, which returned the best classification models. The method obtained with the aid of the random forest classifier resulted in the best prediction of benign/malignant ROIs with median values for sensitivity and specificity of 88.37% and 100%, respectively, by using only three PCs. The features that had shown the greatest contribution to the definition of the same were almost all extracted from the LE images. Our system could represent a valid support tool for radiologists for interpreting CESM images.

Analysis of background parenchymal enhancement (BPE) on contrast enhanced spectral mammography compared with magnetic resonance imaging.

OBJECTIVES: With the growing number of new breast cancer cases in women, new methods of imaging arise. Contrast enhanced spectral mammography (CESM) and magnetic resonance imaging (MRI) are comparable methods regarding sensitivity. The aim of this study is to check if analysis of background parenchymal enhancement on CESM can improve its usefulness. MATERIAL AND METHODS: A total of 64 patients with breast lesions found previously on ultrasound or mammography underwent MRI and CESM within less than one month. On MRI the contrast enhancement kinetics and visual BPE were evaluated. On CESM the enhancement of lesions was noted as well as a quantitative level of BPH. The gathered data was analysed in terms of patterns and relations. RESULTS: A total of 66 lesions were identified both on MRI and CESM, including 11 (17%) benign and 55 (83%) malignant lesions. Among malignant lesions 13 (20%) were assessed as intraductal and 42 (64%) as infiltrating carcinomas. The study showed correlation between the level of enhancement on CESM and the type of kinetic curve on MRI and lesion enhance-ment on CESM as well as confirmed the fact that the BPE is a destimulant in both methods of imaging. CONCLUSIONS: Evaluation of BPE level on CESM can help reading radiologists to define a lesion as malignant with higher probability than based only on the qualitative lesion enhancement level.

Interannual and Seasonal Drivers of Carbon Cycle Variability Represented by the Community Earth System Model (CESM2).

Earth system models are intended to make long-term projections, but they can be evaluated at interannual and seasonal time scales. Although the Community Earth System Model (CESM2) showed improvements in a number of terrestrial carbon cycle benchmarks, relative to its predecessor, our analysis suggests that the interannual variability (IAV) in net terrestrial carbon fluxes did not show similar improvements. The model simulated low IAV of net ecosystem production (NEP), resulting in a weaker than observed sensitivity of the carbon cycle to climate variability. Low IAV in net fluxes likely resulted from low variability in gross primary productivity (GPP)-especially in the tropics-and a high covariation between GPP and ecosystem respiration. Although lower than observed, the IAV of NEP had significant climate sensitivities, with positive NEP anomalies associated with warmer and drier conditions in high latitudes, and with wetter and cooler conditions in mid and low latitudes. We identified two dominant modes of seasonal variability in carbon cycle flux anomalies in our fully coupled CESM2 simulations that are characterized by seasonal amplification and redistribution of ecosystem fluxes. Seasonal amplification of net and gross carbon fluxes showed climate sensitivities mirroring those of annual fluxes. Seasonal redistribution of carbon fluxes is initiated by springtime temperature anomalies, but subsequently negative feedbacks in soil moisture during the summer and fall result in net annual carbon losses from land. These modes of variability are also seen in satellite proxies of GPP, suggesting that CESM2 appropriately represents regional sensitivities of photosynthesis to climate variability on seasonal time scales.