Search Results (188)

Background/Objectives: Breast cancer is the most common malignancy among women, and early detection is critical for improving outcomes. The Breast Imaging Reporting and Data System (BI-RADS) standardizes reporting, but the BI-RADS 4 category presents a major challenge, with malignancy risk ranging from 2% to 95%. Consequently, most women in this category undergo biopsies that ultimately prove unnecessary. This study evaluated whether exhaled breath analysis could distinguish malignant from benign findings in BI-RADS 4 patients. Methods: Participants referred to the McGill University Health Centre Breast Center with BI-RADS 3–5 findings provided multiple breath specimens. Breathprints were captured using an electronic nose (eNose) powered breathalyzer, and diagnoses were confirmed by imaging and pathology. An autoencoder-based model fused the breath data with BI-RADS scores to predict malignancy. Model performance was assessed using repeated cross-validation with ensemble voting, prioritizing sensitivity to minimize false negatives. Results: The breath specimens of eighty-five participants, including sixty-eight patients with biopsy-confirmed benign lesions and seventeen patients with biopsy-confirmed breast cancer within the BI-RADS 4 cohort were analyzed. The model achieved a mean sensitivity of 88%, specificity of 75%, and a negative predictive value (NPV) of 97%. Results were consistent across BI-RADS 4 subcategories, with particularly strong sensitivity in higher-risk groups. Conclusions: This proof-of-concept study shows that exhaled breath analysis can reliably differentiate malignant from benign findings in BI-RADS 4 patients. With its high negative predictive value, this approach may serve as a non-invasive rule-out tool to reduce unnecessary biopsies, lessen patient burden, and improve diagnostic decision-making. Larger, multi-center studies are warranted. Full article

Background and Objectives: Contrast-enhanced mammography (CEM) provides both morphological and functional information and may reflect breast cancer biology similarly to Magnetic Resonance Imaging (MRI). Materials and Methods: This single-center retrospective study included 399 women with Breast Imaging Reporting and Data System (BI-RADS) category 0 screening mammograms who subsequently underwent CEM. A total of 76 malignant lesions (68 invasive cancers, 8 ductal carcinoma in situ (DCIS)) with complete imaging and pathology data were analyzed. Invasive cancers were classified into luminal A, luminal B, luminal B/Human Epidermal Growth Factor Receptor 2 (HER2)-positive, HER2-enriched, and triple-negative, and grouped as luminal (Group 1) versus HER2-positive/triple-negative (Group 2). Results: Luminal subtypes predominated (47 of 68, 69%), while 21 of 68 (31%) were HER2-positive or triple-negative. Most cancers appeared as masses with spiculated margins and heterogeneous enhancement. Significant differences were observed in mass shape (p = 0.03) and internal enhancement (p = 0.01). Luminal tumors were more often irregular and spiculated with heterogeneous enhancement, whereas the HER2-positive/triple-negative tumors more frequently appeared round with rim or homogeneous enhancement. Deep learning-derived malignancy scores (iCAD ProFound AI^®) demonstrated good diagnostic performance (area under the curve (AUC) = 0.744, 95% confidence interval (CI) 0.654–0.821, p < 0.001). The median AI score was significantly higher in malignant compared with benign lesions (70% [interquartile range (IQR) 47–93] vs. 38% [IQR 25–61]; Mann–Whitney U test, p < 0.001). Among malignant lesions, iCAD scores varied across molecular subtypes, with higher median values observed in Group 1 versus Group 2 (87% vs. 55%), although the difference was not statistically significant (Mann–Whitney U test, p = 0.35). Conclusions: CEM features mirrored subtype-specific phenotypes previously described with MRI, supporting its role as a practical tool for enhanced tumor characterization. Although certain imaging and AI-derived parameters differed descriptively across subtypes, no statistically significant differences were observed. As deep-learning models continue to evolve, the integration of AI-enhanced CEM into clinical workflows holds strong potential to improve lesion characterization and risk stratification in personalized breast cancer diagnostics. Full article

Background: BI-RADS (Breast Imaging Reporting and Data System) mammogram classification is central to early breast cancer detection. Despite being an ordinal scale that reflects increasing levels of malignancy suspicion, most models treat BI-RADS as a nominal task using cross-entropy loss, thereby disregarding the inherent class order. This mismatch between the clinical severity of misclassification and the model’s optimization objective remains underexplored. Methods: We systematically evaluate whether incorporating ordinal-aware loss functions improves BI-RADS classification performance under controlled, architecture-fixed conditions and dataset imbalance. Using a unified training pipeline across multiple datasets, we compare ordinal losses to standard cross-entropy, analyzing the effect of dataset- and label-level balancing. Area under the receiver operating characteristic curve (AUROC) and macro-F1 scores are reported as averages over five seeds. Results: Balanced sampling across datasets during training led to statistically significant improvements. Ordinal loss functions, such as Earth Mover Distance (EMD), consistently achieved higher performance across multiple metrics compared to conventional cross-entropy approaches commonly reported in the literature. Improvements were particularly evident in reducing severe misclassifications, demonstrating that aligning the learning objective with the ordinal structure of BI-RADS enhances robustness and clinical relevance. Conclusions: Aligning the learning objective with the ordinal BI-RADS structure substantially improves classification accuracy without changing the underlying architecture. These findings emphasize the importance of loss design, regularization, and data-balancing strategies in medical AI, supporting more reliable breast cancer screening. Full article

Background: Non-communicable diseases (NCDs) such as cardiovascular diseases, diabetes, obesity, and cancer are the leading cause of mortality globally and in Saudi Arabia, accounting for more than 70% of all deaths. Despite national initiatives offering free preventive services, screening uptake remains low. This study aimed to describe the demographic and clinical characteristics of individuals participating in community-based NCD screening campaigns in the Eastern Province of Saudi Arabia and to evaluate screening uptake, compliance, and diagnostic outcomes. Methods: A retrospective cross-sectional analysis was conducted among 3106 adults screened at volunteer-driven community campaigns held between January 2023 and December 2024. Screening included anthropometric measurements, blood pressure assessment, and glucose testing, followed by eligibility evaluation for osteoporosis and cancer screening. Uptake and compliance were verified using electronic health records. Descriptive and inferential statistical analyses were applied. Results: Participants were 64% male and 36% female, with a mean age of 41.4 ± SD years. Obesity, hypertension, and diabetes were identified in 32%, 31%, and 12% of participants overall. Gender-stratified prevalence showed higher obesity among females at 36% (95% CI 32.3 to 38.1) and higher hypertension and diabetes among males at 36% (95% CI 34.0 to 38.2) and 14% (95% CI 12.1 to 15.2), respectively. Uptake among eligible individuals was 51% for dual-energy X-ray absorptiometry (DEXA), 47% for fecal immunochemical testing (FIT), 43% for Pap smear, and 39% for mammography. Diagnostic findings demonstrated substantial undetected disease burden, including osteoporosis in 41% (95% CI 26.0 to 56.8) of DEXA scans, a FIT positivity rate of 5% (95% CI 1.5 to 10.3), abnormal Pap cytology in 3% (95% CI 1.1 to 7.5), and BI-RADS 0 mammograms in 19% (95% CI 11.9 to 29.5), reflecting incomplete assessments requiring further evaluation. Conclusions: Community-based campaigns can effectively resolve limited engagement in health promotional activities and detect substantial burdens of undiagnosed NCDs. However, improvements in referral tracking, follow-up systems, and culturally tailored health education are essential to enhance screening compliance and early detection outcomes. These results can be utilized to inform public policies by extending screening services to additional areas, increasing investment in preventive health campaigns, and enhancing the capacity of the health system. Full article

TEMPO derivatives are well known as scorch retardants due to their ability to effectively quench free alkyl radicals during peroxide crosslinking of polymer compositions. However, in practice this leads to the loss of crosslinking density due to a irreversible decrease in the number of alkyl radicals involved in the crosslinking process. One approach to solving this problem is the use of TEMPO-based biradical molecules, which, on the one hand, are able to effectively quench alkyl radicals, and on the other hand, can couple macroradicals, partially compensating for the loss of crosslinking density. The aim of this work was to reveal the effect of bis(1-oxyl-2,2,6,6-tetramethylpiperidin-4-yl) sebacate (bis-TEMPO) in the concentration range of 0.11–0.44 phr on the delay in the onset of dynamic crosslinking of polyolefin composites initiated by peroxide, as well as the oxidative stability of the resulting crosslinked composites. The obtained data show that using bis-TEMPO at a concentration of less than 0.27 phr increases the crosslinking density of the polyolefin composite, with a crosslinking onset delay of up to 36 s achieved. Simultaneously, antioxidant functionality of bis-TEMPO in crosslinked composites is considered moderate and leads to an increase in the OIT values by 1.7–2.8 times. The crosslinking onset delay time under dynamic conditions is well described by a first-order kinetic model at a constant temperature. The obtained data confirm the efficiency and predictability of bis-TEMPO as a scorch retardant for polyolefin composites. Full article

In this work, the regio- and stereochemistry as well as the molecular mechanism of the cycloaddition reaction of nitrones with (E)-3-(methylsulfonyl)-propenoic acid derivatives were analyzed based on ωb97xD/6-311G(d,p) quantum chemical calculations. In light of these data, it is possible to propose selectivity of the analyzed processes, which was not clearly determined in light of previous experimental studies. Furthermore, the mechanism of the process was diagnosed. CDFT descriptors indicate that the reaction is triggered by a nucleophilic attack of the nitrone oxygen atom on the electrophilic carbon atom of (E)-3-(methylsulfonyl)-propenoic acid derivatives. In turn, PES analysis shows that, despite the nucleophilic-electrophilic character of the reactants, the corresponding transition states are only weakly polar and highly synchronous. IRC calculations rule out zwitterionic or biradical intermediates, confirming a single-step mechanism. The in silico ADME and PASS predictions indicate that the resulting isoxazolidines possess promising biological profiles, showing potential modulation of the serotonin system through 5-HT2A and 5-HT2C antagonism and stimulation of serotonin release, with structural features compatible with P450-mediated metabolism. Considering this attractive application potential, a detailed mechanistic investigation of their formation becomes essential for understanding and ultimately controlling the reaction pathways leading to these heterocycles. Full article

Background/Objectives: Breast density, as defined by the Breast Imaging Reporting and Data System (BI-RADS), reduces mammographic sensitivity and is a risk factor for breast cancer. However, its association with cancer risk and stage at diagnosis remains debated, with limited large-scale evidence. Methods: We conducted a nationwide retrospective cohort study of 952,755 Korean women who underwent screening mammography between 2013 and 2014, with breast cancer diagnoses identified over a 5-year follow-up period. Breast density was categorized by BI-RADS criteria (A–D). Breast cancer risk was evaluated using logistic regression, with odds ratios representing relative odds of developing breast cancer during the 5-year interval. Stage at diagnosis was classified as localized versus regional/distant disease according to national cancer registry records. Results: During 5 years of follow-up, 11,286 women (1.2%) were diagnosed with breast cancer. Higher breast density was significantly associated with increased breast cancer risk: multivariable-adjusted odds ratios (ORs) were 1.174 (95% CI, 1.093–1.260), 1.268 (95% CI, 1.186–1.356), and 1.287 (95% CI, 1.196–1.385) for BI-RADS B–D, respectively, compared with BI-RADS A (all p < 0.001). However, the risk of advanced stage (regional/distant vs. localized) disease at diagnosis did not significantly differ across breast density categories except for a modest association in BI-RADS B (OR 1.16, 95% CI, 1.01–1.33; p = 0.035). Conclusions: Higher breast density was independently associated with increased breast cancer risk but not with advanced-stage disease at diagnosis. These findings underscore the importance of individualized screening strategies for women with dense breasts. Full article

Background/Objectives: Segmental non-mass enhancement (NME) is the breast MRI distribution pattern with the highest positive predictive value (PPV) for malignancy. Despite its diagnostic relevance, its imaging characteristics have rarely been examined in isolation, leaving uncertainty in clinical practice. This multicenter retrospective cohort study aimed to evaluate multiparametric MRI features—including internal enhancement pattern, dynamic contrast-enhanced (DCE) kinetics, and diffusion restriction—in segmental NME to identify malignancy predictors. Methods: This retrospective cohort review included 14,834 breast MRI reports from five institutions (September 2017–February 2024), identifying 103 women (mean age, 44.4 ± 9.9 years) with segmental NME (70 malignant, 33 benign). MRI was performed at 1.5 T or 3 T using standardized protocols. Two breast radiologists, blinded to pathology, assessed internal enhancement, DCE kinetics, diffusion restriction, and short tau inversion recovery (STIR) features according to BI-RADS. Statistical analyses included chi-square/Fisher’s tests and logistic regression. Results: Clustered ring enhancement (CRE) was significantly associated with malignancy (p = 0.004). Fast initial-phase enhancement (p < 0.001) and delayed-phase washout (p = 0.011) also correlated with malignancy. On multivariate analysis, fast initial-phase enhancement remained an independent predictor (odds ratio [OR] = 5.133, p = 0.031), whereas slow enhancement predicted benignity (OR = 0.194, p = 0.020). Histologies included ductal carcinoma in situ, invasive ductal carcinoma, granulomatous mastitis, and benign hyperplastic lesions. Conclusions: This study, focusing exclusively on segmental NME, identifies CRE, fast initial-phase enhancement, and washout kinetics as reliable imaging biomarkers. Incorporating these features into breast MRI interpretation may improve diagnostic accuracy, risk stratification, and management decisions. Full article

Background: The diagnostic process for probable benign breast lesions involves a 1–40% upgrade rate to malignancy when biopsy (cytology and/or histology) is compared with surgery. In a previously conducted clinical randomized trial, we aimed to examine diagnostic discrepancies between prior biopsy results and subsequent vacuum-assisted excision (VAE). Methods: This study is a post hoc analysis of the Swedish VAE randomized trial. Patients were enrolled between November 2019 and August 2022. All patients who underwent a biopsy before VAE were included in this study. Pathology reports from the initial biopsy, VAE, surgical excision, and recurrence were collected. In addition, we conducted clinical follow-up, including imaging, for at least 2 years. Results: The study population included 169 patients with 169 lesions, of whom 71 underwent fine-needle aspiration cytology (FNA), and 126 underwent core-needle biopsy (CNB) before VAE. The diagnostic discrepancy between FNA and VAE was 38% (27/71). The discrepancy between CNB and VAE was 29% (37/126). The upgrade rate to cancer was 7% (5/71) for FNA and 5% (6/126) for CNB. In the CNB group, the highest upgrade rate to cancer occurred in patients with prior atypical ductal hyperplasia (ADH) on CNB (3/12, 25%). Conclusions: The upgrade rate in histopathological diagnosis between prior CNB and VAE was high (15%), and even higher when comparing FNA with VAE (24%). Our findings support avoiding FNA for BI-RADS 3 and 4a lesions and suggest that multi-round VAE may be a safe and effective alternative to surgery for selected cases, particularly those with ADH on CNB. Full article

Background/Objective: This study aims to better characterize the clinical presentation, histology, and imaging features of breast sarcomas on mammography, ultrasound, and MRI, in addition to analyzing the effectiveness of AI DS in detecting breast sarcomas. Methods: A retrospective review from 2008–2024 yielded 18 patients with histologically proven breast sarcomas with imaging available for review. Mammography was available for 13 lesions, ultrasound for 19 lesions, and MRI for 9 lesions. Imaging features were classified according to the BI-RADS 5th edition lexicon. Images were reviewed by two radiologists, and consensus was obtained regarding imaging features. AI DS was retrospectively applied to the breast masses identified on ultrasound. Data analysis was performed using descriptive statistics. Results: 17 females and 1 male were included in this study. Mammographic findings varied from solitary masses (3/13 [23.1%]), asymmetries (3/13 [23.1%]), architectural distortion (1/13 [7.7%]), skin thickening (3/13 [23.1%]), focal asymmetry with calcifications (1/13 [7.7%]), or no suspicious findings (2/13 [15.4%]). Sonography often revealed masses with an irregular shape (13/16 [81.2%]), non-circumscribed margins (15/16 [93.7%]), hypoechoic echo pattern (10/16 [62.5%]), and vascular flow (12/16 [75%]). MRI showed heterogeneously enhancing masses (6/9 [66.7%]) or isolated skin enhancement (3/9 [33.3%]). AI DS analyzed 16 masses on ultrasound and identified 15 (93.8%) as suspicious. Conclusions: Breast sarcomas had a variable appearance on breast imaging, ranging from a solitary mass to isolated skin findings. Awareness of how breast sarcomas can present across imaging modalities while using AI DS as an aid may help radiologists in making the correct diagnosis of this rare and aggressive disease. Full article

Background/Objectives: Precise breast ultrasound (BUS) segmentation supports reliable measurement, quantitative analysis, and downstream classification yet remains difficult for small or low-contrast lesions with fuzzy margins and speckle noise. Text prompts can add clinical context, but directly applying weakly localized text–image cues (e.g., CAM/CLIP-derived signals) tends to produce coarse, blob-like responses that smear boundaries unless additional mechanisms recover fine edges. Methods: We propose XBusNet, a novel dual-prompt, dual-branch multimodal model that combines image features with clinically grounded text. A global pathway based on a CLIP Vision Transformer encodes whole-image semantics conditioned on lesion size and location, while a local U-Net pathway emphasizes precise boundaries and is modulated by prompts that describe shape, margin, and Breast Imaging Reporting and Data System (BI-RADS) terms. Prompts are assembled automatically from structured metadata, requiring no manual clicks. We evaluate the model on the Breast Lesions USG (BLU) dataset using five-fold cross-validation. The primary metrics are Dice and Intersection over Union (IoU); we also conduct size-stratified analyses and ablations to assess the roles of the global and local paths and the text-driven modulation. Results: XBusNet achieves state-of-the-art performance on BLU, with a mean Dice of 0.8766 and IoU of 0.8150, outperforming six strong baselines. Small lesions show the largest gains, with fewer missed regions and fewer spurious activations. Ablation studies show complementary contributions of global context, local boundary modeling, and prompt-based modulation. Conclusions: A dual-prompt, dual-branch multimodal design that merges global semantics with local precision yields accurate BUS segmentation masks and improves robustness for small, low-contrast lesions. Full article

Purpose: The purpose of this study is to assess whether AI-based models improve reproducibility of breast density (BD) and background parenchymal enhancement (BPE) classification and to explore whether contrast-enhanced mammography (CEM) can serve as a proof-of-concept platform for systemic risk surrogates. Materials and Methods: In this retrospective single-center study, 213 women (mean age 58.3 years; range 28–80) underwent CEM in 2022–2023. Histology was obtained when lesions were present (BI-RADS 4/5). Five radiologists independently graded BD and BPE; consensus served as the ground truth. Linear regression and a deep neural network (DNN) were compared with a simple linear baseline. Inter-reader agreement was measured with Fleiss’ κ. External validation was performed on 500 BI-RADS C/D cases from VinDr-Mammo targeted density endpoints. A secondary exploratory analysis tested a multi-output DNN to predict BD/BPE together with bone mineral density and systolic blood pressure surrogates. Results: Baseline inter-reader agreement was κ = 0.68 (BD) and κ = 0.54 (BPE). With AI support, agreement improved to κ = 0.82. Linear regression reduced the prediction error by 26% versus the baseline (MSE 0.641 vs. 0.864), while DNN achieved similar performance (MSE 0.638). AI assistance decreased false positives in C/D by 22% and shortened the reading time by 35% (6.3→4.1 min). Validation confirmed stability (MSE ~0.65; AUC 0.74–0.75). In exploratory analysis, surrogates correlated with DXA (r = 0.82) and sphygmomanometry (r = 0.76). Conclusions: AI significantly improves reproducibility and efficiency of BD/BPE assessments in CEM and supports feasibility of systemic risk profiling. Full article

Timely and precise identification of breast cancer is crucial for enhancing clinical outcomes; however, current AI systems frequently exhibit deficiencies in transparency, trustworthiness, and the capacity to assimilate varied data modalities. We introduce a reliable, multi-agent, multimodal AI system for individualised early breast cancer diagnosis, created on the CBIS-DDSM dataset. The system consists of four specialised agents that cooperatively analyse diverse data. An Imaging Agent employs convolutional and transformer-based models to analyse mammograms for lesion classification and localisation; a Clinical Agent extracts structured features including breast density (ACR), view type (CC/MLO), laterality, mass shape, margin, calcification type and distribution, BI-RADS score, pathology status, and subtlety rating utilising optimised tabular learning models; a Risk Assessment Agent integrates outputs from the imaging and clinical agents to produce personalised malignancy predictions; and an Explainability Agent provides role-specific interpretations through Grad-CAM for imaging, SHAP for clinical features, and natural language explanations customised for radiologists, general practitioners, and patients. Predictive dependability is assessed by Expected Calibration Error (ECE) and Brier Score. The framework employs a modular design with a Streamlit interface, facilitating both comprehensive deployment and interactive demonstration. This paradigm enhances the creation of reliable AI systems for clinical decision assistance in oncology by the integration of strong interpretability, personalised risk assessment, and smooth multimodal integration. Full article

Objectives: To identify factors correlated with post-surgery residue in cases of breast cancer incidentally found via vacuum-assisted excision (VAE). Methods: A total of 6083 patients were enrolled in a retrospective study. Ultrasound evaluation and ultrasound-guided VAE were performed on these patients. According to the pathology of VAE, 53 patients with incidentally found breast cancer were included in the final analysis. Either breast-conserving surgery or mastectomy was performed. The maximal diameter, depth, location, BIRADS category, and Adler’s grade of all lesions before VAE was reviewed and recorded. VAE and post-surgery pathologies were used as gold standards. Either Pearson’s chi-square test or Fisher’s exact test was used for comparison of categorical variables. Results: The mean age of the enrolled patients was 49 years (IQR: 43–55 years). The mean maximal diameter of the lesions was 11.3 mm (IQR: 7–15 mm). There were twenty-eight ductal carcinomas in situ, twelve invasive ductal carcinomas, five lobular carcinomas in situ, two invasive lobular carcinomas, four intraductal papillary carcinomas, and two mucinous carcinomas. Post-surgery pathology showed 15 cases with residual cancer and 38 cases with no residual cancer. The maximal diameter, depth, and pathology derived via VAE were statistically correlated with post-surgery residue (p < 0.05). Conclusions: Small incidentally found noninvasive carcinomas located comparatively deep in the breast could be totally excised by ultrasound-guided vacuum-assisted excision. Both large and superficially invasive carcinomas were more likely to be associated with residue. Full article

Objective: To investigate the prevalence and progression of macrocalcifications or sporadic scattered microcalcifications, breast arterial calcifications (BAC) and grouped microcalcifications in women undergoing lung transplantation (LTX). Materials and Methods: In this retrospective single-center cohort study, 176 adult female patients who underwent mammography between 2008 and 2025 were included: 82 LTX recipients and 94 age-matched controls. Mammographic findings were assessed using standardized BI-RADS criteria and a visual BAC scoring system. Clinical and demographic data were extracted from electronic medical records. Multivariable logistic regression and cumulative incidence analysis were used to evaluate associations and progression patterns. Interobserver agreement was assessed using Fleiss’ kappa. Results: BAC and grouped microcalcifications were significantly more prevalent in the LTX group in the last mammography (BAC: OR 6.57, 95% CI 2.34–20.7; microcalcifications: OR 14.6, 95% CI 3.93–73.9; both p < 0.001). Cumulative incidence analysis showed accelerated progression of BAC and grouped microcalcifications in LTX recipients (p ≤ 0.01), while macrocalcifications or sporadic scattered microcalcification progression did not differ significantly. BAC was often more extensive and potentially mimicked malignant findings. Interobserver agreement was highest for the four-level BAC scoring system (κ = 0.61), followed by BAC presence (κ = 0.59) and macrocalcifications (κ = 0.51), while grouped microcalcifications showed only fair agreement (κ = 0.33). Conclusions: Lung transplant recipients demonstrate significantly higher prevalence and faster progression of BAC and grouped microcalcifications compared to controls, complicating mammographic interpretation. Given their elevated risk of aggressive malignancies and diagnostic overlap between benign and suspicious calcifications, transplant recipients may benefit from tailored screening strategies. Full article