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Background US is clinically established for breast imaging, but its diagnostic performance depends on operator experience. Computer-assisted (real-time) image analysis may help in overcoming this limitation. Purpose To develop precise real-time-capable US-based breast tumor categorization by combining classic radiomics and autoencoder-based features from automatically localized lesions. Materials and Methods A total of 1619 B-mode US images of breast tumors were retrospectively analyzed between April 2018 and January 2024. nnU-Net was trained for lesion segmentation. Features were extracted from tumor segments, bounding boxes, and whole images using either classic radiomics, autoencoder, or both. Feature selection was performed to generate radiomics signatures, which were used to train machine learning algorithms for tumor categorization. Models were evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity and were statistically compared with histopathologically or follow-up-confirmed diagnosis. Results The model was developed on 1191 (mean age, 61 years ± 14 [SD]) female patients and externally validated on 50 (mean age, 55 years ± 15]). The development data set was divided into two parts: testing and training lesion segmentation (419 and 179 examinations) and lesion categorization (503 and 90 examinations). nnU-Net demonstrated precision and reproducibility in lesion segmentation in test set of data set 1 (median Dice score [DS]: 0.90 [IQR, 0.84-0.93]; P = .01) and data set 2 (median DS: 0.89 [IQR, 0.80-0.92]; P = .001). The best model, trained with 23 mixed features from tumor bounding boxes, achieved an AUC of 0.90 (95% CI: 0.83, 0.97), sensitivity of 81% (46 of 57; 95% CI: 70, 91), and specificity of 87% (39 of 45; 95% CI: 77, 87). No evidence of difference was found between model and human readers (AUC = 0.90 [95% CI: 0.83, 0.97] vs 0.83 [95% CI: 0.76, 0.90]; P = .55 and 0.90 vs 0.82 [95% CI: 0.75, 0.90]; P = .45) in tumor classification or between model and histopathologically or follow-up-confirmed diagnosis (AUC = 0.90 [95% CI: 0.83, 0.97] vs 1.00 [95% CI: 1.00,1.00]; P = .10). Conclusion Precise real-time US-based breast tumor categorization was developed by mixing classic radiomics and autoencoder-based features from tumor bounding boxes. ClinicalTrials.gov identifier: NCT04976257 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Bahl in this issue.

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The aim of this study was to investigate the measurement of the incident angle of the main blood vessel, and the benefits of its integral with ultrasound malignant features of breast nodules for the assessment of breast malignancy based on BI-RADS. The incident angles of main blood vessels of 185 breast nodules in 185 patients who underwent breast nodule surgical excision or biopsy were quantitatively measured using color Doppler ultrasound from October 2022 to October 2023 in a tertiary hospital, and related data were collected and analyzed. Based on histopathology as the gold standard, the breast nodules were classified into benign and malignant groups. The incident angle values of both groups were compared, Receiver Operating Characteristic (ROC) curves were plotted, and the optimal cutoff value for distinguishing between benign and malignant breast nodules was determined. The malignancy risk of the breast nodules was assessed using the incident angle of the breast main vessel, BI-RADS classification, and a combination of ultrasound malignant features with the incident angle. The areas under the ROC curves (AUC) for each method were calculated and compared. The average incident angle of the main vessel of the breast nodule for the benign and malignant breast nodule groups was (41.47 ± 14.27)° and (22.65 ± 11.09)°, respectively, with a statistically significant difference (t = 10.027, P < 0.001). There was a significant negative correlation between the breast nodule vessel incident angle and histopathological malignancy (r = - 0.593, P < 0.001). The ROC curve and Youden index suggested that the optimal cutoff value for distinguishing between benign and malignant breast nodules using the vascular incident angle was 26.9°, with a sensitivity of 76.34%, specificity of 84.78%, positive predictive value of 83.53%, negative predictive value of 78.00%, and AUC of 0.853. The diagnostic performance of BI-RADS classification based on ultrasound malignant features of the breast nodules alone in assessing the malignancy risk of breast nodules showed a sensitivity of 78.50%, specificity of 92.39%, positive predictive value of 91.25%, negative predictive value of 79.95%, and AUC of 0.905. The integral of ultrasound malignant features and vascular incident angle for BI-RADS based assessment for breast nodule malignancy risk demonstrated a sensitivity of 90.32%, specificity of 89.13%, positive predictive value of 89.36%, negative predictive value of 90.11%, and AUC of 0.940. The differences in negative predictive value and AUC between ultrasound malignant features BI-RADS classification alone and the combination of ultrasound malignant features BI-RADS classification with the incident angle of the main vessel of the breast nodule were all statistically significant (x2 = 3.243, P = 0.042; Z = 2.955, P = 0.003). Measuring the incident angle of the main blood vessel of breast nodules and combining this measurement with ultrasound malignant features for BI-RADS classification can enhance the effectiveness of malignancy risk assessment of breast nodules, increase the negative predictive value, and potentially reduce unnecessary biopsies.

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Objective: The study aimed to investigate the subjective method of estimating linear breast dimensions in comparison to the objective method. Methods: The reproducibility and accuracy of the subjective method of estimating linear breast dimensions during a simplified breast shape analysis were examined. Four linear breast dimensions including the distance from the sternal notch to the nipple, distance from the nipple to the inframammary fold, distance from the nipple to the midline and under-breast width were evaluated based on subjective estimates. Images from 100 women with natural breasts and without any history of breast surgery were reviewed by two examiners three times each. The cases were obtained from a large database of breast images captured using the Vectra Camera System (Canfield Scientific Inc., USA). The subjective data were then compared with the objective linear data from the Vectra Camera System in the automated analysis. Statistical evaluation was conducted between the three repeated estimates of each examiner, between the two examiners and between the objective and subjective data. Results: The intra-individual variations of the three subjective estimates were significantly greater in one examiner than in the other. This trend was consistent across all eight parameters in the majority of the comparisons of the standard deviations and variation coefficients, and the differences were significant in 14 out of 16 comparisons (p<0.05). Conversely, in the comparison between the subjective and objective data, the estimates were closer to the measurements in one examiner than the other. In contrast to the reproducibility observed, the assessment of the accuracy revealed that the examiner who previously presented with less reproducibility of the estimated data overall showed better accuracy in comparison to the objective data. The overall differences were inconsistent, with some being positive and others being negative. Regarding the distances from the sternal notch to the nipple and breast width, both examiners underestimated the values. However, the deviations were at different levels, particularly when considering the objective data from the Vectra Camera System as the gold standard data for comparison. Regarding the distance from the nipple to the inframammary fold, one examiner underestimated the distance, while the other overestimated it. An opposite trend was noted for the distance from the nipple to the midline. There were no differences in the estimates between the right and left sides of the breasts. The correlations between the measured and estimated distances were positive: as the objective distances increased, the subjective distances also increased. In all cases, the correlations were significant. However, the correlation for the breast width was notably weaker than that for the other distances. Conclusions: The error assessment of the subjective method reveals that it varies significantly and unsystematically between examiners. This is true when assessing the reproducibility as well as the accuracy of the method in comparison to the objective data obtained with an automated system.

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BACKGROUND: Fibroadenoma (FA) is documented as the most common benign breast disease typically presenting as a lump. A wide variety of other diseases including breast cancer can similarly present as lumps hence the need for further differentiation. Ultrasonography plays a vital role in the evaluation and treatment of breast lumps with histological analysis as the gold standard. OBJECTIVE: This study compared the physical and sonographic features of the breast in women with FA and women with breast lumps due to other diseases. MATERIALS AND METHODS: This is a single-centre comparative study. Clinical and sonographic breast evaluations of the recruited patients with lumps were done and reported using the American College of Radiology Breast Imaging Reporting and Data System score. The lumps were biopsied, and histological diagnosis was documented. Clinical and imaging features of the breasts of women with FA were then compared with those of women with lumps from other breast diseases, and collated data were analysed using SPSS Statistical version 23.0. RESULTS: Data from 118 subjects (59 in each group) were used for this study. There was a significant difference in the physical and sonographic appearance of FA concerning the patient's age, parity, change in lesion size, perilesional architecture, echogenicity, borders, capsule and background breast density. No FA was found in women with less dense breasts. CONCLUSION: The sonographic features of breasts showed some differences from the corresponding features of FA and other breast lesions. This has the potential to increase the efficiency of pre-operative diagnosis of FA and could be further applied in developing diagnostic criteria for FA in our environment.

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In our study, we evaluated the diagnostic performance of grayscale ultrasonography (USG) in risk stratification of mass-forming breast lesions. Our study included 90 cases, in which 88 were females and 2 cases were male with age ranging from 16 to 73 years. Out of 90 lesions, 51 (58%) lesions were benign and 39 (39%) lesions were malignant. High-resolution USG done in all 90 lesions revealed sensitivity (90.2%), specificity (74.36%), positive predictive value (PPV) (82.14%), negative predictive value (NPV) (85.29%), and accuracy (83.33%). Calculated weighted kappa value 0.665, indicating better level of agreement in predicting malignant lesions compared to gold standard. Our study revealed that USG is sensitive and specific test in detecting malignant lesions with high PPV and NPV; the calculated weighted kappa value was 0.665, indicating better level of agreement in predicting malignant lesions compared to gold standard.

RésuméDans notre étude, nous avons évalué les performances diagnostiques de l'échographie en niveaux de gris (USG) dans la stratification du risque de lésions mammaires formant une masse. Notre étude a inclus 90 cas, dont 88 femmes et 2 hommes âgés de 16 à 73 ans. Sur 90 lésions, 51 (58 %) étaient bénignes et 39 (39 %) étaient malignes. L'USG haute résolution réalisée sur les 90 lésions a révélé une sensibilité (90,2 %), une spécificité (74,36 %), une valeur prédictive positive (VPP) (82,14 %), une valeur prédictive négative (VPN) (85,29 %) et une précision (83,33 %). Valeur kappa pondérée calculée de 0,665, indiquant un meilleur niveau d'accord dans la prévision des lésions malignes par rapport à l'étalon-or. Notre étude a révélé que l'USG est un test sensible et spécifique pour détecter les lésions malignes avec une PPV et une VPN élevées; la valeur kappa pondérée calculée était de 0,665, ce qui indique un meilleur niveau de concordance dans la prévision des lésions malignes par rapport à l'étalon-or.

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BACKGROUND: Mammographic density has been associated with breast cancer risk, and is modulated by established breast cancer risk factors, such as reproductive and hormonal history, as well as lifestyle. Recent epidemiological and biological findings underscore the recognized benefits of breastfeeding in reducing breast cancer risk, especially for aggressive subtypes. Current research exploring the association among mammographic density, breastfeeding, and breast cancer is sparse. MAIN FINDINGS: Changes occur in the breasts during pregnancy in preparation for lactation, characterized by the proliferation of mammary gland tissues and the development of mammary alveoli. During lactation, the alveoli fill with milk, and subsequent weaning triggers the involution and remodeling of these tissues. Breastfeeding influences the breast microenvironment, potentially altering mammographic density. When breastfeeding is not initiated after birth, or is abruptly discontinued shortly after, the breast tissue undergoes forced and abrupt involution. Conversely, when breastfeeding is sustained over an extended period and concludes gradually, the breast tissue undergoes slow remodeling process known as gradual involution. Breast tissue undergoing abrupt involution displays denser stroma, altered collagen composition, heightened inflammation and proliferation, along with increased expression of estrogen receptor α (ERα) and progesterone receptor. Furthermore, elevated levels of pregnancy-associated plasma protein-A (PAPP-A) surpass those of its inhibitors during abrupt involution, enhancing insulin-like growth factor (IGF) signaling and collagen deposition. Prolactin and small molecules in breast milk may also modulate DNA methylation levels. Drawing insights from contemporary epidemiological and molecular biology studies, our review sheds light on how breastfeeding impacts mammographic density and explores its role in influencing breast cancer. CONCLUSION: This review highlights a clear protective link between breastfeeding and reduced breast cancer risk via changes in mammographic density. Future research should investigate the effects of breastfeeding on mammographic density and breast cancer risk among various ethnic groups and elucidate the molecular mechanisms underlying these associations. Such comprehensive research will enhance our understanding and facilitate the development of targeted breast cancer prevention and treatment strategies.

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OBJECTIVE: Aim: To assess the initial results of using 3 Tesla contrast-enhanced breast magnetic resonance imaging in Ukraine. PATIENTS AND METHODS: Materials and Methods: Our study included 498 diagnostic breast magnetic resonance imaging performed in Neuromed medical center in Kyiv, between March 2020 and December 2022. Patients were positioned prone, with breasts suspended in a dedicated 7-channel bilateral breast coil. MR-images were acquired with the PHILIPS Achieva 3.0Tesla x-series scanner. All studies were made by standard protocol: localizer, morphological and dynamic studies were performed. RESULTS: Results: Our study revealed a statistically significant increase in problem-solving contrast-enhanced breast magnetic resonance examinations compared to other indications. Additionally, we observed a higher incidence of women with a greater amount of fibroglandular tissue (p-value<0.05). CONCLUSION: Conclusions: The utilization of 3Tesla contrast-enhanced breast magnetic resonance imaging has become prevalent in Ukraine as a problem-solving tool for inconclusive findings in ultrasound (US) or/and mammography (MG). It is particularly useful in preoperative local breast cancer staging for women with a significant amount of fibroglandular breast tissue. However, the implementation of breast magnetic resonance imaging in Ukraine is in its nascent stages and requires further investigation, especially in middle-income country settings.

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Secretory carcinoma is a rare, low-grade, special histological type of invasive breast carcinoma. Although it is the most common primary breast cancer in the pediatric population, most cases are diagnosed in adults, with a median age of 48 years (range 3 to 91 years). It most often presents as a painless and slowly growing palpable lump. Imaging findings are nonspecific. Secretory carcinomas have abundant periodic acid-Schiff positive intracytoplasmic and extracellular secretions on histopathology. Nearly all secretory carcinomas have mild to moderate nuclear pleomorphism with low mitotic activity. Over 80% (86/102) of secretory carcinomas display the translocation of t(12;15)(p13;q25), resulting in ETV6::NTRK3 gene fusion. Secretory carcinoma generally has an indolent course and has a better prognosis and overall survival than invasive breast carcinoma of no special type. A good prognosis is associated with age <20 years, tumor size <2 cm, and ≤3 axillary lymph node metastases. Metastases beyond the ipsilateral axillary lymph nodes are rare, with the most common sites involving the lung and liver. Except for the potential addition of targeted drug therapy for NTRK fusion-positive tumors, the treatment approach is otherwise similar to invasive breast carcinomas of similar receptor status.

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INTRODUCTION: Screening and diagnostic mammography are associated with some risk of radiation-induced breast cancer. This study was conducted to establish the National Diagnostic Reference Levels (NDRLs) for digital diagnostic and screening mammography in Uganda to achieve breast radiation dose optimization. METHODS: A cross-sectional study was conducted among female participants recruited by consecutive sampling from three selected hospitals with digital mammography in Uganda. The study variables extracted from the mammography machines were exposure factors, compressed breast thickness (CBT), and Average Glandular Dose (AGD) of two standard mammogram views. The stratified National DRL was derived by calculating the 75th percentile of the AGD across all the samples at various CBT ranges for both screening and diagnostic mammography in craniocaudal (CC) and mediolateral oblique (MLO) views. RESULTS: We included 300 participants with mean ages of 50.28±9.32 and 47.45±13.45 years for the screening and diagnostic mammography, respectively. There were statistically significant positive correlations between AGD and exposure factors (mAs, kVp) (all p-values<0.0001). For screening mammography, mAs demonstrated a strong positive correlation (r = 0.8369 in CC, 0.8133 in MLO), whereas kVp showed a positive correlation with relatively lower coefficients (r = 0.3700 in CC, 0.3080 in MLO). For diagnostic mammography, mAs exhibited an even stronger positive correlation (r = 0.8987 in CC, 0.8762 in MLO), and kVp maintained a positive correlation with somewhat lower coefficients (r = 0.4954 in CC, 0.3597 in MLO). In screening mammography, for CBT within the range of (7-39) mm, the NDRLs were (1.5mGy, 1.66mGy) in CC) and MLO views. For CBT in the range of (40-59) mm, the NDRLs were (1.78mGy, 1.87mGy), and for CBT in the range of (60-99) mm, the NDRLs were (2.18mGy, 2.22mGy). For diagnostic mammography, the NDRLs were established as (1.7mGy, 1.91mGy), (2.00mGy, 2.09mGy), and (2.63mGy, 2.81mGy) for CBT ranges of (7-39) mm, (40-59) mm, and (60-99) mm, respectively. CONCLUSION: The NDRLs for digital screening and diagnostic mammography in Uganda have been proposed for the first time. The NDRL values in mammography should be specific to CBT ranges and mammographic views for both diagnostic and screening mammography.

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Skin inflammation with the potential sequel of moist epitheliolysis and edema constitute the most frequent breast radiotherapy (RT) acute side effects. The aim of this study was to compare the predictive value of tissue-derived radiomics features to the total breast volume (TBV) for the moist cells epitheliolysis as a surrogate for skin inflammation, and edema. Radiomics features were extracted from computed tomography (CT) scans of 252 breast cancer patients from two volumes of interest: TBV and glandular tissue (GT). Machine learning classifiers were trained on radiomics and clinical features, which were evaluated for both side effects. The best radiomics model was a least absolute shrinkage and selection operator (LASSO) classifier, using TBV features, predicting moist cells epitheliolysis, achieving an area under the receiver operating characteristic (AUROC) of 0.74. This was comparable to TBV breast volume (AUROC of 0.75). Combined models of radiomics and clinical features did not improve performance. Exclusion of volume-correlated features slightly reduced the predictive performance (AUROC 0.71). We could demonstrate the general propensity of planning CT-based radiomics models to predict breast RT-dependent side effects. Mammary tissue was more predictive than glandular tissue. The radiomics features performance was influenced by their high correlation to TBV volume.

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Background It is unclear whether breast US screening outcomes for women with dense breasts vary with levels of breast cancer risk. Purpose To evaluate US screening outcomes for female patients with dense breasts and different estimated breast cancer risk levels. Materials and Methods This retrospective observational study used data from US screening examinations in female patients with heterogeneously or extremely dense breasts conducted from January 2014 to October 2020 at 24 radiology facilities within three Breast Cancer Surveillance Consortium (BCSC) registries. The primary outcomes were the cancer detection rate, false-positive biopsy recommendation rate, and positive predictive value of biopsies performed (PPV3). Risk classification of participants was performed using established BCSC risk prediction models of estimated 6-year advanced breast cancer risk and 5-year invasive breast cancer risk. Differences in high- versus low- or average-risk categories were assessed using a generalized linear model. Results In total, 34 791 US screening examinations from 26 489 female patients (mean age at screening, 53.9 years ± 9.0 [SD]) were included. The overall cancer detection rate per 1000 examinations was 2.0 (95% CI: 1.6, 2.4) and was higher in patients with high versus low or average risk of 6-year advanced breast cancer (5.5 [95% CI: 3.5, 8.6] vs 1.3 [95% CI: 1.0, 1.8], respectively; P = .003). The overall false-positive biopsy recommendation rate per 1000 examinations was 29.6 (95% CI: 22.6, 38.6) and was higher in patients with high versus low or average 6-year advanced breast cancer risk (37.0 [95% CI: 28.2, 48.4] vs 28.1 [95% CI: 20.9, 37.8], respectively; P = .04). The overall PPV3 was 6.9% (67 of 975; 95% CI: 5.3, 8.9) and was higher in patients with high versus low or average 6-year advanced cancer risk (15.0% [15 of 100; 95% CI: 9.9, 22.2] vs 4.9% [30 of 615; 95% CI: 3.3, 7.2]; P = .01). Similar patterns in outcomes were observed by 5-year invasive breast cancer risk. Conclusion The cancer detection rate and PPV3 of supplemental US screening increased with the estimated risk of advanced and invasive breast cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Helbich and Kapetas in this issue.

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Background Artificial intelligence (AI) systems can be used to identify interval breast cancers, although the localizations are not always accurate. Purpose To evaluate AI localizations of interval cancers (ICs) on screening mammograms by IC category and histopathologic characteristics. Materials and Methods A screening mammography data set (median patient age, 57 years [IQR, 52-64 years]) that had been assessed by two human readers from January 2011 to December 2018 was retrospectively analyzed using a commercial AI system. The AI outputs were lesion locations (heatmaps) and the highest per-lesion risk score (range, 0-100) assigned to each case. AI heatmaps were considered false positive (FP) if they occurred on normal screening mammograms or on IC screening mammograms (ie, in patients subsequently diagnosed with IC) but outside the cancer boundary. A panel of consultant radiology experts classified ICs as normal or benign (true negative [TN]), uncertain (minimal signs of malignancy [MS]), or suspicious (false negative [FN]). Several specificity and sensitivity thresholds were applied. Mann-Whitney U tests, Kruskal-Wallis tests, and χ2 tests were used to compare groups. Results A total of 2052 screening mammograms (514 ICs and 1548 normal mammograms) were included. The median AI risk score was 50 (IQR, 32-82) for TN ICs, 76 (IQR, 41-90) for ICs with MS, and 89 (IQR, 81-95) for FN ICs (P = .005). Higher median AI scores were observed for invasive tumors (62 [IQR, 39-88]) than for noninvasive tumors (33 [IQR, 20-55]; P < .01) and for high-grade (grade 2-3) tumors (62 [IQR, 40-87]) than for low-grade (grade 0-1) tumors (45 [IQR, 26-81]; P = .02). At the 96% specificity threshold, the AI algorithm flagged 121 of 514 (23.5%) ICs and correctly localized the IC in 93 of 121 (76.9%) cases, with 48 FP heatmaps on the mammograms for ICs (rate, 0.093 per case) and 74 FP heatmaps on normal mammograms (rate, 0.048 per case). The AI algorithm correctly localized a lower proportion of TN ICs (54 of 427; 12.6%) than ICs with MS (35 of 76; 46%) and FN ICs (four of eight; 50% [95% CI: 13, 88]; P < .001). The AI algorithm localized a higher proportion of node-positive than node-negative cancers (P = .03). However, no evidence of a difference by cancer type (P = .09), grade (P = .27), or hormone receptor status (P = .12) was found. At 89.8% specificity and 79% sensitivity thresholds, AI detection increased to 181 (35.2%) and 256 (49.8%) of the 514 ICs, respectively, with FP heatmaps on 158 (10.2%) and 307 (19.8%) of the 1548 normal mammograms. Conclusion Use of a standalone AI system improved early cancer detection by correctly identifying some cancers missed by two human readers, with no differences based on histopathologic features except for node-positive cancers. © RSNA, 2024 Supplemental material is available for this article.

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Accurate specimen marking is crucial during breast cancer surgery to avoid misorientation, which can lead to inadequate re-excision and tumor recurrence. We studied the marking methods at various breast cancer centers to create a tool that would prevent specimen misorientation. An online questionnaire was used to survey marking procedures at major breast cancer centers in Hungary, and a tool was developed using a troubleshooting method. Twelve out of twenty units responded (60%). Nine use an institutionally standardized marking system. Less than half of the surgical teams found specimen mammograms to be unambiguous. In more than 70% of departments, pathologists were uncertain about breast specimen orientation. Ambiguous marking methods caused orientation errors in half of the cases, while unclear marking directions caused the rest. Most pathologists (85%) and surgeons (75%) believed that coronal plane specimen mammography would help solve the problem. A plastic specimen plate has been developed to anchor breast tissue to a coronal breast scheme as seen in mammography images, providing clear localization information throughout the surgical process. There is a lack of standardization in breast specimen orientation and marking in Hungary. An optimized orientation toolkit is being developed to ensure consistent interpretation of specimen mammograms by surgeons and pathologists.

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ABSTRACT: This study aims to explore the value of real-time strain elastography (RTE) and contrast-enhanced ultrasonography (CEUS) in the diagnosis of breast BI-RADS 4 lesions. It collected 85 cases (totaling 85 lesions) diagnosed with breast BI-RADS 4 through routine ultrasound from October 2020 to December 2022 in Huangshan City People's Hospital. All lesions underwent RTE and CEUS examination before surgery, and the ImageJ software was used to measure the periphery of lesion images in the enhancement peak mode and grayscale mode to calculate the contrast-enhanced ultrasound area ratio. The diagnostic capabilities of single-modal and multimodal ultrasound examination for the malignancy of breast BI-RADS 4 lesions were compared using the receiver operating characteristic curve; the Spearman correlation analysis was adopted to evaluate the correlation between multimodal ultrasound and CEUS area ratio. As a result, among the 85 lesions, 51 were benign, and 34 were malignant. The areas under the curve (AUCs) of routine ultrasound (US), US + RTE, US + CEUS, and US + RTE + CEUS were 0.816, 0.928, 0.953, and 0.967, respectively, with the combined method showing a higher AUC than the single application. The AUC of the CEUS area ratio diagnosing breast lesions was 0.888. There was a strong positive correlation (r = 0.819, P < 0.001) between the diagnostic performance of US + RTE + CEUS and the CEUS area ratio. In conclusion, based on routine ultrasound, the combination of RTE and CEUS can further improve the differential diagnosis of benign and malignant lesions in breast BI-RADS 4.

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ABSTRACT: The non-mass breast lesions on ultrasound (US) are a group of challenging pathology. We aimed to standardize these grayscale findings and investigate the effectiveness of superb microvascular imaging (SMI) and shear wave elastography (SWE). A total of 195 lesions were evaluated by B-mode US, SWE, and SMI in the same session. A "NON-MASS model" was built on grayscale US to group the lesions only as areas and those with associated features: microcalcifications, architectural distortion, ductal changes, and microcysts. The mean stiffness parameters Emean, Eratio, and mean vascular index (VI) were recorded following consecutive measurements. Besides, the microvascularity was graded based on Adler's classification (grades 0 to 3). Lesions were divided into 3 groups: benign, category B3, and malignant. One hundred twelve (57.4%) lesions were benign, 23 (11.8%) were B3, and 60 were (30.8%) in the malignant category. Thirty-eight (19.5%) lesions were observed only as an area, whereas associated features were present in 157 lesions (80.5%). Distortion was the only associated feature predicting malignancy among the grayscale findings (P < 0.001). There was a significant difference between malignant and nonmalignant (benign and B3) groups in terms of Adler's grade, Emean, Eratio, and VI values (P < 0.001). Sensitivity, specificity, and accuracy increased when advanced imaging parameters were added to grayscale findings (P < 0.001). In the presence of microcalcifications, architectural distortion, high elasticity, and hypervascularity in the "NON-MASS" imaging model, the suspicion of malignancy increases. The non-mass findings and advanced imaging techniques have the potential to find greater coverage in the following versions of BI-RADS atlas.

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In computed tomography (CT), organ dose modulation (ODM) reduces radiation exposure from the anterior side to reduce radiation dose received by the radiosensitive organs located anteriorly. We investigated the effects of ODM applied to a part of the scan range on radiation dose in body CT. The thorax and thoraco-abdominopelvic region of an anthropomorphic whole-body phantom were imaged with and without ODM. ODM was applied to various regions, and the tube current modulation curves were compared. Additionally, the dose indices were compared with and without ODM in thoracic and thoraco-abdominopelvic CTs in 800 patients. ODM was applied to the thyroid in male patients and to the thyroid and breast in female patients. In phantom imaging of the thorax, the application of ODM below the scan range decreased the tube current, and that to the breast showed a further decrease. Decreased tube current was also observed in phantom imaging of the thoraco-abdominopelvic regions with ODM below the scan range, and the application of ODM to the whole scan range, thyroid, breast, and both thyroid and breast further reduced the tube current in the region to which ODM was applied. In patient imaging, the dose indices were significantly lower with ODM than without ODM, regardless of the scan range or sex. The absolute reduction in dose-length product was larger for thoraco-abdominopelvic CT (male, 43.2 mGy cm; female, 59.7 mGy cm) than for thoracic CT (male, 30.8 mGy cm; female, 37.6 mGy cm) in both sexes, indicating dose reduction in the abdominopelvic region to which ODM was not applied. In conclusion, The application of ODM in body CT reduces radiation dose not only in the region to which ODM is applied but also outside the region. In radiation dose management, it should be considered that even ODM applied to a limited region affects the dose indices.

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Background A curve-shaped compression paddle could reduce the pain experienced by some women at breast cancer screening. Purpose To compare curved and standard compression systems in terms of pain experience and image quality in mammography screening. Materials and Methods In this randomized controlled trial conducted between October 2021 and February 2022, participants screened at three screening sites in the Netherlands were randomized to either a curved-paddle or sham-paddle group. The sham paddle was a standard paddle that was presented as a new paddle. At a standard screening examination, one additional image was acquired with a curved or sham paddle. Pain was measured on a numerical rating scale (range, 0-10). Participants provided a pain score after compression with the standard and test paddles, resulting in two scores per participant. Differences in pain scores were compared between groups using analysis of covariance, adjusting for pain score after standard-paddle compression. Two radiographers and two radiologists performed unblinded paired comparisons of curved-paddle vs standard-paddle images, using standard image quality criteria (radiographers evaluated 1246 image pairs using 12 criteria; radiologists evaluated 320 image pairs using six criteria). The one-sample Wilcoxon signed-rank test was used to determine if there was a significant preference for either paddle. Results In total, 2499 female participants (mean age, 61.6 years ± 7.1 [SD]) were studied; 1250 in the curved-paddle group and 1249 in the sham-paddle group. The mean pain score decreased by an additional 0.19 points in the curved-paddle group compared with the sham-paddle group (95% CI: 0.09, 0.28; P < .001). In terms of image quality, the observers showed no preference or a preference for the standard paddle. Decreased image contrast (range Bonferroni-corrected P values: P < .001 to P > .99) and visibility of structures were the main concerns for curved-paddle images. Conclusion The use of the curved paddle resulted in a minimal pain reduction during mammography breast compression but image quality was reduced. © RSNA, 2024 Supplemental material is available for this article.

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INTRODUCTION: Breast cancer is the most common cancer in women and a leading cause of mortality. This systematic review and meta-analysis aims to evaluate the correlation between breast density measurements obtained from various software and visual assessments by radiologists using full-field digital mammography (FFDM). METHODS: Following the PRISMA 2020 guidelines, five databases (Pubmed, Google Scholar, Science Direct, Cochrane Library, and MEDLINE) were searched for studies correlating volumetric breast density with breast cancer risk. The Newcastle-Ottawa Scale and the Joanna Briggs Institute Checklist were used to assess the quality of the included studies. Meta-analysis of correlation was applied to aggregate correlation coefficients using a random-effects model using MedCalc Statistical Software version 19.2.6. RESULTS: The review included 22 studies with a total of 58,491 women. The pooled correlation coefficient for volumetric breast density amongst Volpara™ and Quantra™ was found to be 0.755 (95% CI 0.496-0.890, p < 0.001), indicating a high positive correlation, albeit with a significant heterogeneity (I2 = 99.89%, p < 0.0001). Subgroup analyses based on study origin, quality, and methodology were performed but did not reveal the heterogeneity cause. Egger's and Begg's tests showed no significant publication bias. CONCLUSION: Volumetric breast density is strongly correlated with breast cancer risk, underscoring the importance of accurate breast density assessment in screening programs. Automated volumetric measurement tools like Volpara™ and Quantra™ provide reliable assessments, potentially improving breast cancer risk prediction and management. IMPLICATIONS FOR PRACTICE: Implementing fully automated breast density assessment tools could enhance consistency in clinical practice, minimizing observer variability and improving screening accuracy. These tools should be further validated against standardized criteria to ensure reliability in diverse clinical settings.

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