Breast Composition Research Articles

Breast Composition assessment by Ultrasonography - Comparison with Mammographic Classification

Breast Composition assessment by Ultrasonography - Comparison with Mammographic Classification

Multiscale Monte Carlo simulations for dosimetry in x-ray breast imaging: Part I - Macroscopic scales.

X-ray breast imaging modalities are commonly employed for breast cancer detection, from screening programs to diagnosis. Thus, dosimetry studies are important for quality control and risk estimation since ionizing radiation is used. To perform multiscale dosimetry assessments for different breast imaging modalities and for a variety of breast sizes and compositions. The first part of our study is focused on macroscopic scales (down to millimeters). Nine anthropomorphic breast phantoms with a voxel resolution of 0.5mm were computationally generated using the BreastPhantom software, representing three breast sizes with three distinct values of volume glandular fraction (VGF) for each size. Four breast imaging modalities were studied: digital mammography (DM), contrast-enhanced digital mammography (CEDM), digital breast tomosynthesis (DBT) and dedicated breast computed tomography (BCT). Additionally, the impact of tissue elemental compositions from two databases were compared. Monte Carlo (MC) simulations were performed with the MC-GPU code to obtain the 3D glandular dose distribution (GDD) for each case considered with the mean glandular dose (MGD) fixed at 4mGy (to facilitate comparisons). The GDD within the breast is more uniform for CEDM and BCT compared to DM and DBT. For large breasts and high VGF, the ratio between the minimum/maximum glandular dose to MGD is 0.12/4.02 for DM and 0.46/1.77 for BCT; the corresponding results for a small breast and low VGF are 0.35/1.98 (DM) and 0.63/1.42 (BCT). The elemental compositions of skin, adipose and glandular tissue have a considerable impact on the MGD, with variations up to 30% compared to the baseline. The inclusion of tissues other than glandular and adipose within the breast has a minor impact on MGD, with differences below 2%. Variations in the final compressed breast thickness alter the shape of the GDD, with a higher compression resulting in a more uniformGDD. For a constant MGD, the GDD varies with imaging modality and breast compression. Elemental tissue compositions are an important factor for obtaining MGD values, being a source of systematic uncertainties in MC simulations and, consequently, in breastdosimetry.

Comparative Analysis of Diagnostic Performance of Automatic Breast Ultrasound, Full-Field Digital Mammography and Contrast-Enhanced Mammography in Relation to Breast Composition.

This single center study includes a comparative analysis of the diagnostic performance of full-field digital mammography (FFDM), contrast-enhanced mammography (CEM) and automatic breast ultrasound (ABUS) in the group of patients with breast American College of Radiology (ACR) categories C and D as well as A and B with FFDM. The study involved 297 patients who underwent ABUS and FFDM. Breast types C and D were determined in 40% of patients with FFDM and low- energy CEM. CEM was performed on 76 patients. Focal lesions were found in 131 patients, of which 115 were histopathologically verified. The number of lesions detected in patients with multiple lesions were 40 from 48 with ABUS, 13 with FFDM and 21 with CEM. Compliance in determining the number of foci was 82% for FFDM and 91% for both CEM and ABUS. In breast types C and D, 72% of all lesions were found with ABUS, 56% with CEM and 29% with FFDM (p = 0.008, p = 0.000); all invasive cancers were diagnosed with ABUS, 83% with CEM and 59% with FFDM (p = 0.000, p = 0.023); 100% DCIS were diagnosed with ABUS, 93% with CEM and 59% with FFDM. The size of lesions from histopathology in breast ACR categories A and B was 14-26 mm, while in breast categories C and D was 11-37 mm. In breast categories C and D, sensitivity of ABUS, FFDM and CEM was, respectively, 78.05, 85.37, 92.68; specificity: 40, 13.33, 8.33; PPV (positive predictive value): 78.05, 72.92, 77.55; NPV (negative predictive value): 40, 25, 25, accuracy: 67.86, 66.07, 73.58. In breast categories A and B, sensitivity of ABUS, FFDM and CEM was, respectively, 81.25, 93.75, 93.48; specificity: 18.18, 18.18, 16.67; PPV: 81.25, 83.33, 89.58; NPV: 18.18, 40, 25; accuracy: 69.49, 79.66, 84.62. The sensitivity of the combination of FFDM and ABUS was 100 for all types of breast categories; the accuracy was 75 in breast types C and D and 81.36 in breast types A and B. The study confirms the predominance of C and D breast anatomy types and the low diagnostic performance of FFDM within that group and indicates ABUS and CEM as potential additive methods in breast cancer diagnostics.

Radiation Dose Tracking in Digital Mammography: Evaluation of Population Profiles through Automatic Data Extraction from the DICOM Header

International regulatory organizations for quality control of X-ray systems, such as the International Atomic Energy Agency (IAEA), have implemented protocols for acceptance testing of digital mammography and breast tomosynthesis equipment, aiming to establish quality standards in radiology services. However, these guidelines are usually based on tests with breast phantoms with standardizedthicknesses and compositions, often not representative of the patient population profiles at those services. In a prior study, we developed a computational system designed to automatically tracking and managing data extracted from the image acquisition processes of digital mammography and breast tomosynthesis, stored on a DICOM SCP (Service Class Provider) server. This approach enables obtaining technical reports characterizing exposure parameters and tests have shown that the reference levels outlined in international standards for breast composition and radiation dose do not accurately reflect the characteristics of the actual patient population. Thus this study describes data collection and corresponding analysis for the dose tracking process primarily on three digital mammography systems of different radiological services. Extensive image datasets from these systems were obtained using a new application described previously, with a focus on dose profiles generated during exposures. Graphical representations resultingfrom the datasets are presented, along with analysis of skin entrance and mean glandular doses distributions, average kV and mAs applied during the exams together with the target/filter combinations, radiographic density distribution, as well as the age and breast thickness characteristic of the respective population submitted to exposures in each of those mammography services. Additionally, the extent of information and ease of acquisition provided by the tool for performance evaluation of digital mammography services is discussed

Quantitative Study on the Breast Density and the Volume of the Mammary Gland According to the Patient’s Age and Breast Quadrant

Objectives: Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patient’s age and breast quadrant. We propose a regression model to estimate PBD and MGV as a function of the patient’s age. Methods: The breast composition in 1027 spiral breast CT (BCT) datasets without soft tissue masses, calcifications, or implants from 517 women (57 ± 8 years) were segmented. The breast tissue volume (BTV), MGV, and PBD of the breasts were measured in the entire breast and each of the four quadrants. The three breast composition features were analyzed in the seven age groups, from 40 to 74 years in 5-year intervals. A logarithmic model was fitted to the BTV, and a multiplicative inverse model to the MGV and PBD as a function of age was established using a least-squares method. Results: The BTV increased from 545 ± 345 to 676 ± 412 cm3, and the MGV and PBD decreased from 111 ± 164 to 57 ± 43 cm3 and from 21 ± 21 to 11 ± 9%, respectively, from the youngest to the oldest group (p < 0.05). The average PBD over all ages were 14 ± 13%. The regression models could predict the BTV, MGV, and PBD based on the patient’s age with residual standard errors of 386 cm3, 67 cm3, and 13%, respectively. The reduction in MGV and PBD in each quadrant followed the ones in the entire breast. Conclusions: The PBD and MGV computed from BCT examinations provide important information for breast cancer risk assessment in women. The study quantified the breast mammary gland reduction and density decrease over the entire breast. It established mathematical models to estimate the breast composition features—BTV, MGV, and PBD, as a function of the patient’s age.

Automated Breast Density Assessment in MRI Using Deep Learning and Radiomics: Strategies for Reducing Inter-Observer Variability.

Accurate breast density evaluation allows for more precise risk estimation but suffers from high inter-observer variability. To evaluate the feasibility of reducing inter-observer variability of breast density assessment through artificial intelligence (AI) assisted interpretation. Retrospective. Six hundred and twenty-one patients without breast prosthesis or reconstructions were randomly divided into training (N = 377), validation (N = 98), and independent test (N = 146) datasets. 1.5 T and 3.0 T; T1-weighted spectral attenuated inversion recovery. Five radiologists independently assessed each scan in the independent test set to establish the inter-observer variability baseline and to reach a reference standard. Deep learning and three radiomics models were developed for three classification tasks: (i) four Breast Imaging-Reporting and Data System (BI-RADS) breast composition categories (A-D), (ii) dense (categories C, D) vs. non-dense (categories A, B), and (iii) extremely dense (category D) vs. moderately dense (categories A-C). The models were tested against the reference standard on the independent test set. AI-assisted interpretation was performed by majority voting between the models and each radiologist's assessment. Inter-observer variability was assessed using linear-weighted kappa (κ) statistics. Kappa statistics, accuracy, and area under the receiver operating characteristic curve (AUC) were used to assess models against reference standard. In the independent test set, five readers showed an overall substantial agreement on tasks (i) and (ii), but moderate agreement for task (iii). The best-performing model showed substantial agreement with reference standard for tasks (i) and (ii), but moderate agreement for task (iii). With the assistance of the AI models, almost perfect inter-observer variability was obtained for tasks (i) (mean κ = 0.86), (ii) (mean κ = 0.94), and (iii) (mean κ = 0.94). Deep learning and radiomics models have the potential to help reduce inter-observer variability of breast density assessment. 3 TECHNICAL EFFICACY: Stage 1.

Fatty Acid Profile, Volatile Organic Compound, and Physical Parameter Changes in Chicken Breast Meat Affected by Wooden Breast and White Striping Myopathies.

The aim of this research was to determine the impact of pectoralis major myopathies on the physical parameters, fatty acid (FA) profile, and volatile organic compound (VOC) composition of chicken breast meat. Samples were collected from pectoralis major of broilers with varying severity scores (normal, mild, and severe) of wooden breast (WB) and white striping (WS) myopathies. Chicken breast meat affected by severe myopathies expressed higher cooking loss, drip loss (p < 0.001), and yellowness (p < 0.05) compared to those of samples that were taken from broilers without myopathies (normal). The amount of monounsaturated fatty acids (MUFAs) was significantly higher in samples affected by mild and severe myopathies than in those without myopathies (p < 0.05). There was significantly more aldehyde hexanal in muscles affected by mild and severe myopathies than in muscles without myopathies (p < 0.05). In conclusion, WB and WS myopathies of the breast muscle not only affected the physical parameters of broiler meat but also may have influenced its FA profile and VOC composition. Additionally, an elevated amount of hexanal in muscles affected by WB together with WS suggests that oxidative stress could be important in the etiopathogenesis of WB and WS myopathies. Therefore, poultry meat affected by myopathies have the potential to alter breast meat flavor and composition.

Computational mechanobiology model evaluating healing of postoperative cavities following breast-conserving surgery

Breast cancer is the most commonly diagnosed cancer type worldwide. Given high survivorship, increased focus has been placed on long-term treatment outcomes and patient quality of life. While breast-conserving surgery (BCS) is the preferred treatment strategy for early-stage breast cancer, anticipated healing and breast deformation (cosmetic) outcomes weigh heavily on surgeon and patient selection between BCS and more aggressive mastectomy procedures. Unfortunately, surgical outcomes following BCS are difficult to predict, owing to the complexity of the tissue repair process and significant patient-to-patient variability. To overcome this challenge, we developed a predictive computational mechanobiological model that simulates breast healing and deformation following BCS. The coupled biochemical-biomechanical model incorporates multi-scale cell and tissue mechanics, including collagen deposition and remodeling, collagen-dependent cell migration and contractility, and tissue plastic deformation. Available human clinical data evaluating cavity contraction and histopathological data from an experimental porcine lumpectomy study were used for model calibration. The computational model was successfully fit to data by optimizing biochemical and mechanobiological parameters through Gaussian process surrogates. The calibrated model was then applied to define key mechanobiological parameters and relationships influencing healing and breast deformation outcomes. Variability in patient characteristics including cavity-to-breast volume percentage and breast composition were further evaluated to determine effects on cavity contraction and breast cosmetic outcomes, with simulation outcomes aligning well with previously reported human studies. The proposed model has the potential to assist surgeons and their patients in developing and discussing individualized treatment plans that lead to more satisfying post-surgical outcomes and improved quality of life.

Diagnostic Performance of Contrast-Enhanced Digital Mammography versus Conventional Imaging in Women with Dense Breasts.

The aim of this prospective study was to compare the diagnostic performance of contrast-enhanced mammography (CEM) versus digital mammography (DM) combined with breast ultrasound (BUS) in women with dense breasts. Between March 2021 and February 2022, patients eligible for CEM with the breast composition category ACR BI-RADS c-d at DM and an abnormal finding (BI-RADS 3-4-5) at DM and/or BUS were considered. During CEM, a nonionic iodinated contrast agent (Iohexol 350 mg I/mL, 1.5 mL/kg) was power-injected intravenously. Images were evaluated independently by two breast radiologists. Findings classified as BI-RADS 1-3 were considered benign, while BI-RADS 4-5 were considered malignant. In case of discrepancies, the higher category was considered for DM+BUS. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated, using histology/≥12-month follow-up as gold standards. In total, 51 patients with 65 breast lesions were included. 59 (90.7%) abnormal findings were detected at DM+BUS, and 65 (100%) at CEM. The inter-reader agreement was excellent (Cohen's k = 0.87 for DM+BUS and 0.97 for CEM). CEM showed a 93.5% sensitivity (vs. 90.3% for DM+BUS), a 79.4-82.4% specificity (vs. 32.4-35.5% for DM+BUS) (McNemar p = 0.006), a 80.6-82.9% PPV (vs. 54.9-56.0% for DM+BUS), a 93.1-93.3% NPV (vs. 78.6-80.0% for DM+BUS), and a 86.1-87.7% accuracy (vs. 60.0-61.5% for DM+BUS). The AUC was higher for CEM than for DM+BUS (0.865 vs. 0.613 for Reader 1, and 0.880 vs. 0.628, for Reader 2) (p < 0.001). In conclusion, CEM had a better diagnostic performance than DM and BUS alone and combined together in patients with dense breasts.

Efficacy of Dietary Fenugreek and Kalonji Seed Powder on Carcass Traits of Guinea Fowls

A study was conducted on 100, day old guinea fowls (Pearl variety) for 12 weeks to study the efficacy of fenugreek (Trigonella foenum-graecum) and kalonji (Nigella sativa) seed powder supplementation on carcass traits and meat composition of thigh and breast. The experimental guinea fowls were randomly divided into five treatment groups viz. T1 control, T2 (2.0% fenugreek seed powder), T3 (1.0% kalonji seed powder), T4 (2.0% fenugreek +1.0% kalonji seed powder) and T5 (1.0% fenugreek + 0.5% kalonji seed powder), with two replications of ten guinea fowls each. The supplementation was done for 84 days. On 84th day, three guinea fowls from each replicate (6 guinea fowls/treatment) were sacrificed for study of carcass yield, cut up parts, organ weights and processing losses. The results indicated that dietary fenugreek and kalonji seed powder supplementation in guinea fowls improved dressed yield in T4 group of guinea fowls. Cut up parts in terms of thigh weights were improved in fenugreek and kalonji seed powder supplemented groups with maximum weight observed in T5 group of guinea fowls whereas breast weight was significantly increased in T4 group of guinea fowls. From the results of present study, it can be concluded that combination of fenugreek (2%) and kalonji (1%) seed powder through feed may be advised to improve carcass yield in guinea fowls.

Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence.

Recently, breast types were categorized into four types based on the Breast Imaging Reporting and Data System (BI-RADS) atlas, and evaluating them is vital in clinical practice. A Japanese guideline, called breast composition, was developed for the breast types based on BI-RADS. The guideline is characterized using a continuous value called the mammary gland content ratio calculated to determine the breast composition, therefore allowing a more objective and visual evaluation. Although a discriminative deep convolutional neural network (DCNN) has been developed conventionally to classify the breast composition, it could encounter two-step errors or more. Hence, we propose an alternative regression DCNN based on mammary gland content ratio. We used 1476 images, evaluated by an expert physician. Our regression DCNN contained four convolution layers and three fully connected layers. Consequently, we obtained a high correlation of 0.93 (p < 0.01). Furthermore, to scrutinize the effectiveness of the regression DCNN, we categorized breast composition using the estimated ratio obtained by the regression DCNN. The agreement rates are high at 84.8%, suggesting that the breast composition can be calculated using regression DCNN with high accuracy. Moreover, the occurrence of two-step errors or more is unlikely, and the proposed method can intuitively understand the estimated results.

Pengaruh Imbangan Minyak Ikan Lemuru dan Minyak Kelapa Sawit terhadap Karkas dan Komposisi Kimia Daging Ayam Broiler

The purpose of this research was to analyze effect of the balancing lemuru fish oil and palm oil on the carcass of broiler and chemical composition of breast of broiler. 200 broilers reared from DOC to 35 days old. Broilers were divided into 4 treatment groups and 5 replications. The treatment consisted of: P0 = diet with 3% crude palm oil, P1 = diet with 1% lemuru fish oil + 2% crude palm oil, P2 = diet with 2% lemuru fish oil + 1% crude palm oil, P3 = diet with 3% lemuru fish oil. Data were analyzed using ANOVA with Duncan's significant difference test. Treatment with lemuru fish oil up to 3% had significant effect (P&lt;0.05) on decreasing carcass but increasing the proportion of thigh. The use of lemuru fish oil feed as much as 2% and palm oil as much as 1% is more effective than control diet on carcass and meat chemistry composition.

Abstract 6448: Processed, red, and white meat consumption and breast composition in a prospective cohort study of Chilean girls

Abstract Meat consumption has been associated with risk of breast cancer. However, the association between different types of meat (processed and unprocessed red, and white) intake during childhood and breast density has not been explored. This prospective study included 290 Chilean girls from the Growth and Obesity Cohort Study. Diet was assessed every 6 months with a 24-hour recall. Dual-energy X-ray absorptiometry was used to assess breast density (absolute fibroglandular volume (FGV)) and percent breast density (FGV/total breast volume, %FGV) at Tanner Breast Staging 4 (B4). Multivariable linear regression models were adjusted for total calorie intake, age at B4, sugar sweetened beverage intake, vegetable intake, TV watching after school, highest maternal education level, BMI z-score, and height. Substitution analysis was assessed using white meat in replacement of processed and unprocessed red meat. There was no association between total, processed and unprocessed red, or white meat intake and %FGV and absolute FGV. In the estimated substitution analyses, we observed a slightly lower breast density when processed or unprocessed red meat was replaced with white meat; however, effect estimates were modest and imprecise. For example, substituting 1 serving of unprocessed white meat for 1 serving of processed or unprocessed red meat was associated -4.1 cm3 (95% CI -10.3, 2.2) and -4.5 cm3 (95% CI -10.3, 1.3) lower absolute FGV, respectively. In this cohort of Chilean girls, there was little association between total or specific types of meat intake and breast density. Substitution analysis of different types of meat and average breast density at Tanner stage 4 Beta coefficient Confidence interval P value Absolute FGV (cm3) Substituting 1 serving of unprocessed white meat for 1 serving of unprocessed red meat -4.1 (-10.3, 2.2) 0.21 Substituting 1 serving of unprocessed white meat for 1 serving of processed red meat -4.5 (-10.3, 1.3) 0.13 Percentage FGV (%) Substituting 1 serving of unprocessed white meat for 1 serving of unprocessed red meat -2.5 (-5.9, 0.8) 0.14 Substituting 1 serving of unprocessed white meat for 1 serving of processed red meat -2.8 (-5.8, 0.3) 0.08 Citation Format: Claire E. Kim, Audrey J. Gaskins, Camila Corvalan, Ana Pereira, John Shepherd, Karin B. Michels. Processed, red, and white meat consumption and breast composition in a prospective cohort study of Chilean girls [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6448.

Lifetime changes in body fatness and breast density in postmenopausal women: the FEDRA study

BackgroundHigh mammographic breast density (MBD) is an established risk factor for breast cancer (BC). Body fatness conveys an increased BC risk in postmenopause but is associated with less dense breasts. Here, we studied the relationship between body fatness and breast composition within the FEDRA (Florence-EPIC Digital mammographic density and breast cancer Risk Assessment) longitudinal study.MethodsRepeated anthropometric data and MBD parameters (obtained through an automated software on BC screening digital mammograms) were available for all participants, as well as information on other BC risk factors. Multivariate linear regression and functional data analysis were used to longitudinally evaluate the association of body fatness, and changes thereof over time, with dense (DV) and non-dense (NDV) breast volumes and volumetric percent density (VPD).ResultsA total of 5,262 women were included, with anthropometric data available at 20 and 40 years of age, at EPIC baseline (mean 49.0 years), and an average of 9.4 years thereafter. The mean number of mammograms per woman was 3.3 (SD 1.6). Body fatness (and increases thereof) at any age was positively associated with DV and NDV (the association being consistently stronger for the latter), and inversely associated with VPD. For instance, an increase by 1 kg/year between the age of 40 years and EPIC baseline was significantly associated with 1.97% higher DV, 8.85% higher NDV, and 5.82% lower VPD.ConclusionBody fatness and its increase from young adulthood until midlife are inversely associated with volumetric percent density, but positively associated with dense and non-dense breast volumes in postmenopausal women.

Artificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial Algorithms

To assess differences in the mammographic and sonographic appearance of breast cancer in African American (AA) and Non-Latina White (NLW) women.We identified AA and NLW women with biopsy proven ductal carcinoma in situ or invasive breast cancer between June 1, 2015 and May 31, 2018. Racial differences in Breast Imaging and Reporting Data System (BI-RADS) imaging features were analyzed by imaging cohorts, i.e. screen detected vs. clinical presentation, using logistic regression adjusted for histology and molecular subtypes.We analyzed 270 AA women with 278 cancers (166 screen detected, 112 clinical) and 586 NLW women with 599 cancers (397 screen detected, 202 clinical). Compared with NLW women, AA women had higher rates of non-dense breast composition (almost entirely fatty 12.0% vs. 4.6%, scattered fibroglandular 50.9% vs. 45.2%; overall P < 0001) in both cohorts and were less likely to have screen detected architectural distortion, (odds ratio (OR) = 0.38, 95% CI 0.18–0.80). AA women were less likely than NLW women to have screen detected irregular than oval/round masses (mammography: OR = 0.36, 95% CI 0.19–0.68; sonography: OR = 0.48, 95% CI 0.24–0.94), and more likely to present clinically with high density masses (OR = 3.03, 95% CI 1.12–8.20) demonstrating posterior enhancement (OR = 3.02, 95% CI 1.11–8.27).There are racial differences in the mammographic and sonographic appearance of breast cancer even after accounting for higher rates of triple negative breast cancer in AA women. Understanding these differences may provide breast imagers with a framework to approach breast cancer diagnosis in the AA population in clinical practice.

Validity of computed mean compressed fibroglandular tissue thickness and breast composition for stratification of masking risk in Japanese women.

The volumetric measurement system for mammographic breast density is a high-precision objective method for evaluating the percentage of fibroglandular tissue volume (FG%). Nonetheless, FG% does not precisely correlate with subjective visual estimation (SVE) and shows poor evaluation performance regarding masking risk in patients with comparatively thin compressed breast thickness (CBT), commonly found in Japanese women. We considered that the mean compressed fibroglandular tissue thickness (mCGT), which incorporates the CBT element into the evaluation of breast density, may better predict masking risk. Volumetric measurements and SVEs were performed on mammograms of 108 breast cancer patients from our center. mCGT was calculated as the product of CBT and FG%. SVE was classified using the Breast Imaging-Reporting and Data System classification, 5th edition. Subsequently, the performance of mCGT, SVE, and FG% in predicting masking risk was estimated using the AUC. The AUC values of mCGT and SVE were 0.84 (95% confidence interval, 0.71-0.92) and 0.78 (0.66-0.86), respectively (P = 0.16). The AUC of the FG% was 0.65 (0.52-0.77), which was significantly lower than that of mCGT (P < 0.001). The sensitivity and specificity of mCGT in predicting negative detection were 89% and 71%, respectively; of SVE 83% and 61% (versus 72% and 57% with FG%), suggesting that mCGT was superior to FG% in both sensitivity and specificity, and comparable with SVE. Objective mCGT calculated from the volumetric measurement system will highly likely be useful in evaluating breast density and supporting visual assessment for masking risk stratification.

Our experience using synthesized mammography vs full field digital mammography in population-based screening

BackgroundSynthesized Mammogram (SM) from Digital Breast Tomosynthesis (DBT) images is introduced to replace the routine Full Field Digital Mammography (FFDM) to reduce radiation dose. Purposeto compare the conspicuity of cancer related findings between SM and FFDM and combination of these methods with DBT MethodsThe study was conducted in a tertiary breast imaging center, where 200 women referred for screening were enrolled in the study sequentially. Patients underwent FFDM and DBT simultaneously and a two-year follow-up was done. Data was evaluated for Breast Imaging Reporting and Data System (BI-RADS) score, breast density, mass lesions, calcification, and focal asymmetry by two expert breast radiologists. Comparison between different methods was made by Cohen Kappa test. Results22 patients with likely malignant findings went under biopsy. Taking histopathologic findings and two-year follow up as reference, the overall sensitivity and specificity for FFDM+DBT (86.1 and 88.9 respectively) and SM+DBT (86.1 and 88.2) didn’t show a meaningful difference. Comparing SM and FFDM, calcification in 20 subjects were overlooked on SM, but later detected when combined with DBT. Considering breast composition and BI-RADS categorization, an excellent agreement existed between the readers. ConclusionScreening with SM+DBT shows comparable results with FFDM+DBT considering BI-RADS categorization of the patients. Although SM showed slightly inferior sensitivity compared to FFDM, after combining DBT with SM no malignant appearing calcification or mass lesion was missed.

Effects of dietary supplementation with herbal extract as methionine replacer on growth performance, meat composition, oxidative stability and liver gene expression in broiler chickens

Abstract In the present study, an herbal feed additive was tested for partial-to-complete replacement of synthetic methionine in poultry diets, along with its effects on performance, breast and thigh meat chemical composition, oxidative stability during refrigerated storage and the expression of five target genes in liver. In a 35 days trial, 600 one-day-old male chicks were randomly allocated to 4 groups with 10 replicates. Birds in the control group were fed a regular maize–soybean-based diet that covered DL-methionine needs while the second group (Meth40) was similar to control but contained only DL – Methionine at 40% of control diet. Diet in third group contained DLMethionine at 40% of control and the herbal feed additive Methiorep TMMeth40+Mrep) with extracts of Boerhavia diffusa, Azadirachta indica, Vigna mungo and Trigonella foenum-graecum. Diet of fourth group was formulated to totally replace DL Methionine by Methiorep TM (Mrep). Body weight gain and feed consumption were weekly recorded. At the end of the trial, all birds were slaughtered and 2 chickens per pen were selected for meat and liver sampling. The liver was tested for the expression of five target genes, namely Methionine synthase (MTR), Tyrosine aminotransferase (TAT), Spermidine synthase (SMS), Methionine sulfoxide reductase (MSRB1) and Betaine homocysteine S-methyltransferase (BHMT). The results showed that the Meth40 group had reduced body weight compared to the Meth40+Mrep group while the Control and Mrep groups had comparable weights. Feed intake and feed conversion ratio did not differ among the experimental groups. Carcass, breast and thigh meat yield were higher in the Mrep and the Control compared to Meth40 and Meth40+Mrep groups. Also, meat oxidation was significantly lower in herbal groups compared to the control group. After normalization to β-actin expression, quantitative real-time PCR analysis revealed an induction in the expression of MTR and SMS genes in the liver of both herbal treated groups. No changes were observed for the TAT, MSRB1and BHMT genes in the herbal treated groups compared to the control or the Meth40 group. In conclusion, herbal feed additives with specific plant extracts may be able to improve both growth performance and antioxidant activity of broiler chickens, phenolic content; yet, they may also support in amino acid efficient use of broiler.

Artificial intelligence assistance for women who had spot compression view: reducing recall rates for digital mammography.

Mammography yields inevitable recall for indeterminate findings that need to be confirmed with additional views. To explore whether the artificial intelligence (AI) algorithm for mammography can reduce false-positive recall in patients who undergo the spot compression view. From January to December 2017, 236 breasts from 225 women who underwent the spot compression view due to focal asymmetry, mass, or architectural distortion on standard digital mammography were included. Three readers who were blinded to the study purpose, patient information, previous mammograms, following spot compression views, and any clinical or pathologic reports retrospectively reviewed 236 standard mammograms and determined the necessity of patient recall and the probability of malignancy per breast, first without and then with AI assistance. The performances of AI and the readers were evaluated with the recall rate, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Among 236 examinations, 8 (3.4%) were cancers and 228 (96.6%) were benign. The recall rates of all three readers significantly decreased with AI assistance (P < 0.05). The reader-averaged recall rates significantly decreased with AI assistance regardless of breast composition (fatty breasts: 32.7% to 24.1%m P = 0.002; dense breasts: 33.6% to 21.2%, P < 0.001). The reader-averaged AUC increased with AI assistance and was comparable to that of standalone AI (0.835 vs. 0.895; P = 0.234). The reader-averaged specificity (71.2% to 79.8%, P < 0.001) and accuracy (71.3% to 79.7%, P < 0.001) significantly improved with AI assistance. AI assistance significantly reduced false-positive recall without compromising cancer detection in women with focal asymmetry, mass, or architectural distortion on standard digital mammography regardless of mammographic breast density.

Evaluation of the peritumoral features using radiomics and deep learning technology in non-spiculated and noncalcified masses of the breast on mammography.

To assess the significance of peritumoral features based on deep learning in classifying non-spiculated and noncalcified masses (NSNCM) on mammography. We retrospectively screened the digital mammography data of 2254 patients who underwent surgery for breast lesions in Harbin Medical University Cancer Hospital from January to December 2018. Deep learning and radiomics models were constructed. The classification efficacy in ROI and patient levels of AUC, accuracy, sensitivity, and specificity were compared. Stratified analysis was conducted to analyze the influence of primary factors on the AUC of the deep learning model. The image filter and CAM were used to visualize the radiomics and depth features. For 1298 included patients, 771 (59.4%) were benign, and 527 (40.6%) were malignant. The best model was the deep learning combined model (2mm), in which the AUC was 0.884 (P < 0.05); especially the AUC of breast composition B reached 0.941. All the deep learning models were superior to the radiomics models (P < 0.05), and the class activation map (CAM) showed a high expression of signals around the tumor of the deep learning model. The deep learning model achieved higher AUC for large size, age >60 years, and breast composition type B (P < 0.05). Combining the tumoral and peritumoral features resulted in better identification of malignant NSNCM on mammography, and the performance of the deep learning model exceeded the radiomics model. Age, tumor size, and the breast composition type are essential for diagnosis.