Density Categories Research Articles

Abstract PO3-07-08: Breast density estimation with a microwave-frequency imaging system

Abstract Background: Microwave imaging has been proposed as an alternative method of breast imaging that is low-cost and comfortable for women as it avoids excessive compression. Microwave properties of tissues relate to water content and behavior (Gabriel et al, 1996); specifically, fatty tissues have lower properties and glandular tissues have greater properties (Lazebnik et al, 2007). These differences in microwave signatures of fatty and glandular tissues provide the opportunity to map the composition of the breast and create a density score without a mammogram. This density score may find utility in risk stratification or monitoring interventions aimed at decreasing breast density (Salazar et a, 2020). Purpose: We examine the feasibility of developing a density score based on microwave images that correlates to mammographic breast density (VOLPARA score) in a pilot study with healthy volunteers. Imaging System: We have developed a microwave imaging system that facilitates scanning of large groups of patients, as well as comparison to x-ray mammography (Mojabi et al, 2023). The system consists of two plates which are placed in contact with the breast. Microwave transmitters and receivers are embedded in the plates; signals transmitted through the breast are used to estimate microwave frequency properties of tissues, and maps of these estimates form a 2D image. Methods: 50 patients provided informed consent (study approved by Health Ethics Research Board of Alberta CC-21-0082). Both breasts of each volunteer were scanned. Previously performed mammograms were available for 21 of the volunteers. The number of volunteers with VOLPARA scores A, B, C, and D is 2, 8, 6, and 5, respectively. The percent density reported with the VOLPARA score is also available for these volunteers. Microwave images were formed for each scan and analyzed to predict density with three approaches: (1) average permittivity, (2) segmented regions, and (3) pixel-based intensities. The results demonstrate that the average permittivity of the breast typically increases from VOLPARA A to D, with some overlap in average values observed between the density categories. A correlation between average permittivity and percent breast density was observed. Regions representing glandular tissues are segmented from microwave images; the average values of these regions clearly differentiate between VOLPARA A and D, however do not show consistent ranges for VOLPARA scores B and C. Finally, the microwave breast density estimated using pixel-based intensities shows good correlation with the percentage density calculations from mammograms. Conclusions: Microwave images contain features related to the glandular tissues embedded in fat. By analyzing the composition of the breast in these images, density scores are created. While average permittivity appears to correlate to percent density calculated from mammograms, area or pixel-based approaches appear to have greater potential for categorizing into density classes. Expanding the number of participants, identifying biomarkers, and exploring deep learning techniques for density prediction are considered for future work Citation Format: Elise Fear, Jeremie Bourqui, Pedram Mojabi, Bobbie-Jo Docktor, Anita Garland, Danielle Deutscher, Zahra Lasemiimeni, Kathleen McMahon, Brendon Besler, Roger Tsang. Breast density estimation with a microwave-frequency imaging system [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-08.

Thyroid Density in CT Imaging as a Potential Marker of Lung Involvement in COVID-19: A Retrospective Analysis.

Background The SARS-CoV-2 pandemic has underscored the multifaceted impact of the virus on human health, extending beyond the respiratory system to involve other organ systems, including the endocrine system. Emerging evidence suggests a notable interaction between COVID-19 and thyroid function, characterized by alterations in thyroid hormone levels and structural changes within the gland. This study aims to explore the association between thyroid density on CT imaging and lung involvement in patients with COVID-19, potentially offering new insights into the systemic effects of the virus. Methodology A retrospective cross-sectional analysis was conducted on 1,066 patients withCOVID-19 who underwent chest CT scans without contrast at Government Medical College, Omandurar Government Estate, Chennai, which was designated as theCOVID-19 care center from April to June 2021. Thyroid density and lung involvement were quantitatively assessed, and their correlation was analyzed using descriptive and inferential statistics, including the Kruskal-Wallis H test and Shapiro-Wilk test for normality. Results The study population predominantly exhibited normal thyroid density (749, 70.3%), followed by altered (212, 19.9%), nodular (104, 9.8%), and a single instance (0.1%) of absent thyroid density. Despite variability in lung involvement across different thyroid density categories, statistical analysis revealed no significant association between thyroid density and the extent of lung involvement in patients with COVID-19. Conclusions This study found no significant correlation between thyroid density and lung involvement in patients with COVID-19, suggesting that thyroid density on CT imaging may not serve as a reliable marker for lung involvement in this population. Further research is warranted to explore the complex interactions between COVID-19 and thyroid function, as well as the potential implications for patient management and prognosis.

Cancer detection rate of breast-MR in supplemental screening after negative mammography in women with dense breasts. Preliminary results of the MA-DETECT-Study after 200 participants

BACKGROUNDDue to increased cancer detection rates (CDR), breast MR (breast MRI) can reduce underdiagnosis of breast cancer compared to conventional imaging techniques, particularly in women with dense breasts. The purpose of this study is to report the additional breast cancer yield by breast MRI in women with dense breasts after receiving a negative screening mammogram. METHODSFor this study we invited consecutive participants of the national German breast cancer Screening program with breast density categories ACR C & D and a negative mammogram to undergo additional screening by breast MRI. Endpoints were CDR and recall rates. This study reports interim results in the first 200 patients. At a power of 80% and considering an alpha error of 5%, this preliminary population size is sufficient to demonstrate a 4/1000 improvement in CDR. RESULTSIn 200 screening participants, 8 women (40/1000, 17.4–77.3/1000) were recalled due to positive breast MRI findings. Image-guided biopsy revealed 5 cancers in 4 patients (one bilateral), comprising four invasive cancers and one case of DCIS. 3 patients revealed 4 invasive cancers presenting with ACR C breast density and one patient non-calcifying DCIS in a woman with ACR D breast density, resulting in a CDR of 20/1000 (95%-CI 5.5–50.4/1000) and a PPV of 50% (95%-CI 15.7–84.3%). CONCLUSIONOur initial results demonstrate that supplemental screening using breast MRI in women with heterogeneously dense and very dense breasts yields an additional cancer detection rate in line with a prior randomized trial on breast MRI screening of women with extremely dense breasts. These findings are highly important as the population investigated constitutes a much higher proportion of women and yielded cancers particularly in women with heterogeneously dense breasts.

Tailored 3D breast models for development of microwave based breast tumor screening

Portable breast monitoring devices, which could be used outside the hospital and even for self-monitoring of risk groups, are considered as promising eHealth applications for future telemedicine. Microwave technique is one of the most promising emerging techniques for portable monitoring devices since it enables low-cost, high accuracy, and user-friendly devices. The technique is based on detecting differences in radio channel responses since tumors have different dielectric properties than the breast glandular or fat tissues. Breast density categories affect the detectability of the tumors with also microwave technique. This paper presents a study on breast tumor detectability with different simulation and measurement models tailored to correspond different breast types. The simulations are carried out using electromagnetic simulation software with human voxel models as well as developed breast models. The evaluation results show that even small-sized breast tumors can be detected with microwave technique within all the breast density categories. Differences in channel responses caused by tumors are breast-type dependent. The results highlight the importance of developing extensive reference databank covering all the breast density categories for microwave-based breast tumor detection applications.

Back-to-back high category atmospheric river landfalls occur more often on the west coast of the United States

The catastrophic December 2022-January 2023 nine atmospheric rivers in California underscore the urgent need to better understand such high-risk weather extremes. Here we applied a machine learning clustering tool to understand the activity of atmospheric river clusters. Reanalysis results show that clusters with high density, that is the time fraction under atmospheric river conditions within a cluster, exhibit more frequent high-category atmospheric rivers, alongside an increased likelihood for extreme precipitation and severe land surface responses. The key circulation patterns of atmospheric river clusters are primarily attributed to subseasonal variability. Furthermore, the occurrence and density of atmospheric river clusters are modulated by the daily variability of the geopotential height field. Climate model projections suggest that atmospheric river clusters with higher density and higher categories will be more frequent as warming level increases. Our findings emphasize the important role of atmospheric river clusters in the development of climate adaptation and resilience strategies.

AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway

ObjectiveTo explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program.Materials and methodWe included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013–2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1–4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1–10 were stratified by VDG.ResultsWe found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1–91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2–91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9–95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3–99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5–70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8–68.7) for VDG4.ConclusionThe tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4.Clinical relevance statementOur study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density.Key Points• Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening.• Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density.• Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.

Abstract MP29: Ready-to-Eat Food Environment and Risk of Incident Type 2 Diabetes: A Prospective Cohort Study

Introduction: Access to environments providing ready-to-eat food has been linked with a higher prevalence of type 2 diabetes (T2D), however, no study has prospectively assessed the food environment with T2D incidence. Hypothesis: We hypothesized that being exposed to ready-to-eat food environments was prospectively associated with a higher risk of developing T2D. Method: The current study included 480,909 participants free of T2D at baseline from the UK Biobank. Individual food environment variables, including pubs or bars, restaurants or cafeterias, and fast-food outlets, were objectively measured within 1 km catchment areas of the residential street of the participants. A composite food environment variable was created as the sum of the density of all three individual food environment variables. Cox proportional hazard model was used to assess the prospective relationship between the ready-to-eat food environments and incident T2D. Results: During a median of 12 years of follow-up, a total of 28,762 T2D cases were documented. Being exposed to a higher density of ready-to-eat food environments was associated with a higher risk of developing T2D ( Table ): HR = 1.22 (95% CI: 1.16-1.27) for comparing the extreme categories of pubs and bars density; HR=1.05 (95% CI: 1.00-1.10) for restaurants and cafeterias, and HR=1.14 (95% CI: 1.09-1.18) for fast-food outlets. Being exposed to the highest density of a composite food environment was associated with a 26% higher risk of developing T2D (HR=1.26, 95% CI: .1.19-1.33). In addition, we found that the higher T2D risk associated with exposure to ready-to-eat food environments was more pronounced among women than men (p-interaction=0.025). Conclusions: Our results indicate that exposure to ready-to-eat food environments is associated with an increased risk of developing T2D.

PROSPECTIVE DIRECTIONS OF URBAN CONCEPTS TRANSFORMATION IN THE CONTEXT OF THE IMMERSIVE PRESENCE ENVIRONMENTS DEVELOPMENT

The proliferation of immersive presence environments (an artificially created multimedia environment of an alternative reality) is evolving into a widespread social phenomenon that gradually influences how people live and interact. As a result, it has the potential to reshape expectations and demands regarding living spaces and their arrangement. While numerous studies have explored the impact of virtual and augmented reality technologies on urban planning, relatively little attention has been given to the possibility that the behavioral changes brought about by these technologies could prompt a reevaluation of the fundamental principles underlying urban planning. Previously, immersive environments were primarily seen as tools for a limited group of individuals to enhance their problem-solving capabilities within the existing framework of urban planning. However, with the widespread adoption of virtual presence tools, there is a potential for a significant shift in this paradigm. The exact nature and implications of these changes are still not well understood and remain uncertain. However, through the method of behavioral interpolation, we can begin to model the key aspects and potential outcomes of this paradigm shift. The object of this article is to present a theoretical framework for potential transformations in the contemporary urban planning paradigm resulting from the widespread adoption of personalized virtual reality technologies. Through analysis, five key categories within modern post-industrial urban planning are identified as likely to undergo changes: a) residential areas; b) accessibility radius; c) social cohesion; d) transportation requirements, and e) optimal density. Residential units will necessitate additional opportunities and spaces, altering living requirements and lifestyles. The significance of the accessibility radius, particularly for pedestrians, may diminish as a considerable number of desirable locations become accessible directly from individuals' homes. Reducing strain on transportation networks will enhance cities' environmental sustainability and create new opportunities for green spaces. The concept of social cohesion will experience stratified transformations, as differences related to ethnicity, religion, gender, race, age, social status, etc., become neutralized through the development of shifting virtual personas and non-deterministic identities. These interrelated changes will also diminish the weight of the optimal density category. Collectively, these transformations form a comprehensive conceptual framework that reimagines the city. This article outlines two potential directions of change in the urban paradigm that could arise from the proliferation of personalized metaverse communication technologies. The first direction pertains to the contemporary interpretation of deurbanistic concepts, incorporating elements of self-sufficiency, stability, security, and communication infrastructure characteristic of the information age. The second direction is likely to focus on various approaches to transforming the existing urban environment. This transformation will occur through the involvement of market regulatory mechanisms and the development and implementation of specialized municipal programs aimed at modernization and stimulating population growth.

Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation.

This study evaluates the effectiveness of artificial intelligence (AI) in mammography in a diverse population from a middle-income nation and compares it to traditional methods. A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results. Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (κ =0.606, p < 0.001) and the BI-RADS category with and without AI (κ =0.74, p < 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively. AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients. • The use of artificial intelligence (AI) in mammography for population-based breast cancer screening has been validated in high-income nations, with reported improved diagnostic performance. Our study evaluated the usage of an AI tool in an opportunistic screening setting in a multi-ethnic and middle-income nation. • The application of AI in mammography enhances diagnostic accuracy, potentially leading to reduced unnecessary biopsies. • AI integration into the workflow did not disrupt the performance of trained breast radiologists, as there is a substantial inter-reader agreement for BI-RADS category assessment and breast density.

The association between mammographic density and breast cancer risk in Chinese women: a systematic review and meta-analysis

PurposeBreast density has consistently been shown to be an independent risk factor for breast cancer in Western populations; however, few studies have evaluated this topic in Chinese women and there is not yet a unified view. This study investigated the association between mammographic density (MD) and breast cancer risk in Chinese women.MethodsThe PubMed, Cochrane Library, Embase, and Wanfang databases were systematically searched in June 2023 to include all studies on the association between MD and breast cancer risk in Chinese women. A total of 13,977 breast cancer cases from 14 studies were chosen, including 10 case-control/cross-sectional studies, and 4 case-only studies. For case-control/cross-sectional studies, the odds ratios (ORs) of MD were combined using random effects models, and for case-only studies, relative odds ratios (RORs) were combinations of premenopausal versus postmenopausal breast cancer cases.ResultsWomen with BI-RADS density category II-IV in case-control/cross-sectional studies had a 0.93-fold (95% confidence interval [CI] 0.55, 1.57), 1.08-fold (95% CI 0.40, 2.94), and 1.24-fold (95% CI 0.42, 3.69) higher risk compared to women with the lowest density category. Combined RORs for premenopausal versus postmenopausal women in case-only studies were 3.84 (95% CI 2.92, 5.05), 22.65 (95% CI 7.21, 71.13), and 42.06 (95% CI 4.22, 419.52), respectively, for BI-RADS density category II-IV versus I.ConclusionsFor Chinese women, breast cancer risk is weakly associated with MD; however, breast cancer risk is more strongly correlated with mammographic density in premenopausal women than postmenopausal women. Further research on the factors influencing MD in premenopausal women may provide meaningful insights into breast cancer prevention in China.

Abstract B078: Artificial-intelligence-driven breast density assessment in the transition from full-field digital mammograms to digital breast tomosynthesis

Abstract Introduction: To enhance reproducibility and robustness in mammographic density assessment, various artificial intelligence (AI) models have been proposed to automatically classify mammographic images into BI-RADS density categories. Despite their promising performances, so far density AI models have been assessed primarily in traditional full-field digital mammography (FFDM) images. Our study aims to assess the potential of AI in breast density assessment in FFDM versus the newer synthetic mammography (SM) images acquired with digital breast tomosynthesis. Methods: We retrospectively analyzed negative (BI-RADS 1 or 2) routine mammographic screening exams (Selenia or Selenia Dimensions; Hologic) acquired at sites within the Barnes-Jewish/Christian (BJC) Healthcare network in St. Louis, MO from 2015 to 2018. BI-RADS breast density assessments of radiologists were obtained from BJC’s mammography reporting software (Magview 7.1). For each mammographic imaging modality, a balanced dataset of 4,000 women was selected so there were equal numbers of women in each of the four BI-RADS density categories, and each woman had at least one mediolateral oblique (MLO) and one craniocaudal (CC) view per breast in that mammographic imaging modality. Previously validated pre-processing steps were applied to all FFDM and SM images to standardize image orientation and intensity. Images were then split into training, validation, and test sets at ratios of 80%, 10%, and 10%, respectively, while maintaining the distribution of breast density categories and ensuring that all images of the same woman appear only in one set. Our AI model was based on the widely used ResNet50 architecture and was designed to accept as an input a mammographic image and predict the BI-RADS breast density category that the image belongs to. Our AI model was optimized, trained, and evaluated separately for each mammographic imaging modality. We report on the AI model’s predictive accuracy on the test set for each mammographic imaging modality, for both views as well as separately for CC and MLO; accuracy differences in FFDM versus SM were assessed via bootstrapping. Results: A batch size of 32, learning rate of e-6, and Adam optimizer were chosen as the optimal hyperparameters for our AI model. Using the same hyperparameters, the AI model demonstrated substantially higher accuracy on the test set for FFDM than for SM (FFDM: accuracy = 71% ± 4.5% versus SM: accuracy = 66% ± 4.2%; p-value&amp;lt;0.001 for comparison). Similar conclusion held when CC and MLO views were evaluated separately (accuracy = 72% ± 4.6% versus 66% ± 4.3% for CC; accuracy = 69% ± 4.5% versus 62% ± 4.3% for MLO; p-value&amp;lt;0.001 for both comparisons). Conclusions: AI performance in BI-RADS breast density assessment was significantly higher on FFDM versus SM, even under the same AI model design, dataset size and training process. Our preliminary findings suggest that further AI optimizations and adaptations may be needed as we translate AI models from FFDM to the newer SM format acquired with digital breast tomosynthesis. Citation Format: Krisha Anant, Juanita Hernandez Lopez, Debbie Bennett, Aimilia Gastounioti. Artificial-intelligence-driven breast density assessment in the transition from full-field digital mammograms to digital breast tomosynthesis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B078.

Development of Auto Dense-breast Classification on Mammography Images Using Image Similarity

In Japan, radiologists perform qualitative visual classification to define four categories of mammary gland density. However, an objective estimation of mammary gland density is necessary. To address this, we developed an automatic classification software using image similarity. We prepared 741 cases of mediolateral oblique images (MLO) for evaluation, and they were diagnosed as normal among the mammography images taken at our hospital. Image matching was performed using the evaluation images and an image database for breast density determination. In this study, the image similarity used zero normalized cross-correlation (ZNCC) as an index. In addition, if the breast thickness is less than 30 mm and each breast density category ZNNC has the same value, the category is evaluated on the fat side. We compared the results of qualitative visual classification and automatic classification methods to assess consistency. The agreement with the subjective breast composition classification was 78.5%, and the weighted kappa coefficient was 0.98. One mismatched case was evaluated on the higher density side with the same ZNCC value between categories and a breast thickness greater than 30 mm. Image similarity provides an excellent estimation of quantification of breast density. This system could contribute to improving the efficiency of the mammography screening system.

Diagnostic Value of Radiographic Singh Index Compared to Dual-Energy X-Ray Absorptiometry Scan in Diagnosing Osteoporosis: A Systematic Review.

Since various medications can control the rate of fractures and subsequent complications of osteoporosis, the early detection of the disease is crucial. This systematic study aimed to compare the diagnostic accuracy of Singh index (SI) with dual-energy X-ray absorptiometry (DEXA) as a benchmark standard for diagnosing osteoporosis. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) were utilized in the current study. A detailed search was carried out using PubMed and Scopus from inception to 30 May 2022. Examining quality of the studies was performed by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). A total of 22 studies were included. In general, 50% of the studies considered SI a poor screening tool for detecting osteoporosis due to a negligible inter-observer agreement between SI and DEXA or a poor correlation of SI with the bone mineral density (BMD) category or DEXA T-score. A moderate inter-observer agreement was reported for SI in 5 (55.6%) studies. Among the studies assessing the sensitivity and specificity of SI compared to DEXA (n=13), six studies estimated a low sensitivity for SI. While there is supporting evidence indicating the potential usefulness of SI for predicting femoral neck fractures in individuals with suspected osteoporosis, numerous studies challenge its reliability and diagnostic value as a screening tool for identifying femoral neck osteoporosis. Further primary studies are required to verify the effectiveness of the SI index in identifying populations at risk of osteoporosis.

Effectiveness of chemical road dust suppressants on paved roads of Pimpri Chinchwad

Introduction: Currently, reducing road dust emissions is a serious problem for safe and environment-protective transportation, leading to Particulate Matter (PM) limit violations and high population exposures to dust containing poisonous metals and mineral particles. Nowadays chemical road dust suppressants are being employed more frequently as a viable remedy to reduce roadside dust emissions.&#x0D; Materials and methods: Two seasons winter and summer were selected for the study and during which, typically 3 types of roads as per average traffic density and land-use category identified in the city of Pimpri-Chinchwad were sprayed. Dry samples were taken on day one from a 1 m2 area using a portable vacuum cleaner that was run for 8 to 10 min each. Similarly, samples were collected in the same area after 5-10 min after spraying with Calcium Chloride (CaCl ) and Magnesium Chloride (MgCl ). Also, the second set of 2 samples was collected after 24 h of chemical applied.&#x0D; Results: Maximum reduction in silt loading occurred i.e., 99.73% on the application of 30 g/m2 of CaCl . Reduction observed in emission factor of PM2.5 was 97.78% and in the case of PM10 it was 97.59%. While increase in average particle size was observed up to 27.88 µm.&#x0D; Conclusion: Both the suppressant efficient in suppressing the road dust but the day 1 result shows more dust suppression as compared to the day 2 dust suppression also as per the dust loading CaCl2 is a better dust suppressant than MgCl2.

Imaging characteristics of interval cancers detected on Full Field Digital Mammography (FFDM) versus Digital Breast Tomosynthesis (DBT)

ObjectiveTo compare imaging features of interval cancers detected in patients screened with full field digital mammography (FFDM) versus digital breast tomosynthesis (DBT). Materials/MethodsThis retrospective observational study consisted of female patients undergoing screening DM or FFDM at an academic medical center and two outpatient imaging facilities between January 2012 and June 2017. A natural language processing algorithm queried breast imaging reports for breast density and BI-RADS category. This was cross-referenced to an institutional breast cancer registry to identify interval cancers. Retrospective consensus review of the cases was done to categorize imaging features of interval cancers on FFDM vs DBT. ResultsThe rate of interval cancers was comparable in patients screened with FFDM (30/39793) and DBT (29/32180) (p = 0.58). There was no significant difference in the rate, histopathology, or imaging features of interval cancers in patients screened with FFDM versus DBT. The most common mammographic features on diagnostic imaging across both groups was the presence of a mass (13/47). Almost equally common was negative diagnostic mammogram with mass detected only on ultrasound (11/47). The rate of interval cancers detected by high-risk surveillance breast MRI was increased in patients who previously had screening with DBT relative to those who had screening with FFDM (p = 0.0419). ConclusionThere is no significant difference in rate of detection, histopathology, or imaging features of interval cancers in patients screened with FFDM versus DBT. However, across both cohorts, the most common features on diagnostic mammogram were either the presence of a mass or a negative mammogram.

Development and investigation on physical and mechanical properties of mahogany sawdust particleboards made with tannic powder of Indian tamarind (Pithecellobium Dulce Benth)

The recycling of forest residues was investigated in the present study. It arms to determine the physical and mechanical properties of particleboards from mahogany sawdust bonded by pearls bone glue or tannic powder of peel of Indian tamarind (Pithecellobium Dulce). The influence of the size of particles on the physical and mechanical properties of the particleboards is studied. Five different granulometries (raw sawdust; 2.5 &lt; g £ 5; 1.6 &lt; g £ 2.5 and 0.8 &lt; g £ 1.6, and g £ 0.8) were investigated. The rate of the adhesives used were 5; 6.5; 7; 7.5; 8; 10, 12.5 and 15 percent. Physically, the density, thickness swelling (TS) and water absorption (WA) were evaluated. Mechanically, the modulus of elasticity (MOE), the modulus of rupture (MOR), the modulus of Young E, the tensile modulus of rupture (MOT) and the internal bond (IB) were determined. The results obtained showed that the granulometry has a great influence on the physical as well as the mechanical properties of the Mahogany sawdust particleboards. The pressing temperature is 160 °C. The threshold fixed by the American standard ANSI A208.1-2016 for the MOE and the MOR are widely reached. The values of the density classified the particleboards from sawdust of Mahogany wood in the categories of low and mean density. The thickness swelling and water absorption has not respectful to the American standard ANSI A208.1–2016 [1]. So, the manufactured boards can only be used in dry conditions.

Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy.

Background and Objective: 2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian-cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. Methods: For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1403 and 1595 features for the 2D and 3D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM)-based prediction models were developed and optimized for each feature set. Five-fold cross-validation was used to assess the performance of each individual model. Results: The results show that the 2D feature-based model achieved an AUC (area under the ROC curve (receiver operating characteristic)) of 0.84 ± 0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86 ± 0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91 ± 0.01. Conclusions: This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future.

Mammographic breast density and cardiovascular disease risk in women

AimsWe aimed to determine the predictive role of mammographic breast density in addition to the Framingham Risk Score (FRS) on subsequent CVD events in women. Methods and ResultsThis cohort study included 4,268,579 women aged ≥40 years who underwent mammography screening between 2009 and 2010 with follow-up until 2020. Breast density was reported following the Breast Imaging Reporting and Data System. Primary outcomes included coronary heart disease, cerebrovascular disease, peripheral arterial disease, and heart failure. The incremental predictive ability of breast density added to the FRS model was assessed using the ROC and net reclassification index (NRI) among all women and strata based on FRS risk categories (<5% as low-risk, 5%–10% as moderate-risk, and ≥10% as high-risk). In total, 135,475 CVD events were recorded after a median follow-up of 10.9 years. A lower category of breast density was associated with a higher risk of CVD. Compared to the extremely dense breast group, the hazard ratios (95% CI) for CVDs were 1.12 (1.09–1.14), 1.19 (1.17–1.22), and 1.29 (1.26–1.32) in women with heterogeneously dense, scattered fibroglandular densities, and almost entirely fat breast density, respectively. Adding breast density to the FRS showed a slight improvement in AUROC but a modest improvement in NRI; the C-statistic difference was 0.083% (95% CI 0.069–0.096) with a 7.15% (6.85–7.69) increase in NRI, with the strongest improvement observed in the low-risk group. ConclusionsMammographic breast density is an independent predictor of incident CVD among women. The addition of mammographic breast density to FRS improves the prediction of CVDs, especially in low-risk individuals.

Trends in cardiovascular care in Japan from 2010 to 2019 in relation to population density

Abstract Background As Japan faces a declining birthrate and an ageing population, it is also necessary to transfer the population from urban areas to rural areas to prevent depopulation in rural areas. On the other hand, it is necessary to verify whether cardiovascular medical care systems are equalized regardless of population density. Purpose The aim of this study is to describe the evolution of healthcare in Japan by population density. Methods This descriptive study used data obtained from the Japanese Registry of All Cardiac and Vascular Diseases database from 2010 to 2019. The nationwide 10-year trend in cardiovascular medical care systems was described in total and according to quartiles of hospitals based on population density. Results The number of patients admitted to cardiovascular facilities and the number of cardiologists showed a 1.26-fold and 1.18-fold increase from 2010 to 2019, respectively (P for trend &amp;lt;0.01 for both). The very high population density category had a lower number of hospitals and cardiology beds per population, whereas the very low population density category had a lower number of cardiologists per population. A greater increase in the number of patients was observed in the higher population density category (P for interaction&amp;lt;0.01). The difference in the number of hospitalized patients per cardiologist decreased among the population density categories. Conclusion Based on population density, the highest increase in the number of patients and cardiologists per hospital was observed in the higher population density category; however, the difference in the number of patients per cardiologist decreased among the categories. Figure legend Hospital-level trends in cardiovascular medical care systems according to population density. (A, B, and C) The number of patients to cardiologists according to population density. (D)

Regional differences in historical diphtheria and scarlet fever notification rates in The Netherlands, 1905-1925: a spatial-temporal analysis.

We describe how rates of two frequently occurring notifiable diseases-diphtheria and scarlet fever-varied between regions of The Netherlands in the early twentieth century, and identify potential factors underlying this variation. Digitized weekly mandatory notification data for 1905-1925, municipality level, were aggregated into 27 'spatial units' defined by unique combinations of province and population density category (high: more than 4500; mid : 1250-4500; low: less than 1250 inhabitants km-2). Generalized additive regression models were fitted to estimate the associations between notification rates and population density, infant mortality rate and household income, while adjusting for temporal trends per spatial unit. Annual per capita notification rates for both diphtheria and scarlet fever tended to rise from the beginning of the period 1905-1925 until peaking around 1918/1919. Adjusted diphtheria notification rates were higher for high- and mid- compared with low-density municipalities (by 71.6 cases per 100 000, 95% confidence interval (CI) : 52.7-90.5; 39.0/100 k, 95% CI : 24.7-53.3, respectively). Scarlet fever showed similar associations with population density (35.7 cases per 100 000, 95% CI : 9.4-62.0; 21.4/100 k, 95% CI: 1.5-41.3). There was considerable spatial variation in notification rates for both diseases in early twentieth century Netherlands, which could partly be explained by factors capturing variation in living conditions and socio-economic circumstances. These findings aid understanding of contemporary respiratory infection transmission.