Introduction Breast density is a well-established predictor of breast cancer risk, affecting both the probability of malignancy and the sensitivity of mammography. Background parenchymal enhancement (BPE), which is observed in contrast-enhanced breast imaging, has been investigated as a potential independent biomarker for breast cancer risk. However, the literature presents conflicting results regarding its relationship with both breast density and malignancy. Unlike breast density, which is classified using the ACR BI-RADS system, BPE lacks a standardized reporting system in contrast-enhanced mammography (CEM), leading to inconsistencies in its assessment and clinical application. This study aims to determine whether breast density influences BPE in CEM and to establish a structured lexicon for BPE classification, which will enhance consistency in clinical practice and improve comparability across research studies. Materials and Methods This retrospective study included 213 patients who underwent CEM, mammography (MG), and ultrasound (US) between May 2022 and June 2023 at the P.O. ‘A. Perrino’ Hospital, Brindisi. BPE was categorized into four levels (Minimal, Light, Moderate, and Marked), while breast density was rated according to the ACR BI-RADS (A–D) classification. Statistical analysis was performed to assess the correlation between BPE and breast density, with the findings supporting the need for a BPE-CEM Standard Scale (BCSS) to standardize reporting and improve clinical consistency. Results Among the 213 patients, 57% exhibited minimal BPE, 31% light BPE, 10% moderate BPE, and 2% marked BPE. Higher breast density (ACR C–D) was significantly associated with increased BPE levels, while lower breast density (ACR A–B) correlated with lower BPE levels. Regression analysis confirmed a significant positive correlation (p < 0.05) between breast density and BPE, with no significant association found between BPE and age. These findings underline the necessity for a standardized BPE classification system, leading to the development of the BCSS, a structured lexicon aimed at enhancing clinical reporting and interobserver agreement. Conclusion BPE in CEM is significantly influenced by breast density, highlighting the need for a standardized classification system. To address the current variability in reporting, we propose the BPE-CEM Standard Scale (BCSS), which aims to ensure greater consistency in both clinical practice and research. Standardizing BPE characterization will facilitate its integration into breast cancer risk assessment models. Future research should focus on validating the BCSS framework and evaluating its impact on diagnostic accuracy and clinical decision-making.

Background: Accurate preoperative tumor sizing is critical for optimal surgical planning in breast cancer. Contrast-enhanced mammography (CEM) has emerged as a promising modality, yet its accuracy relative to conventional imaging and pathology requires further validation. Objective: To prospectively evaluate the dimensional accuracy and reproducibility of CEM compared to mammography and ultrasound, using surgical pathology as the reference standard. Methods: A total of 205 patients with 267 breast lesions underwent preoperative CEM, mammography, and ultrasound. Tumor sizes were measured independently by two radiologists. Accuracy was assessed via mean absolute error (MAE), Pearson and Spearman correlations, and inter-reader agreement evaluated by intraclass correlation coefficient (ICC) and Gwet’s AC1. Sensitivity analyses included bootstrap confidence intervals and log-transformed data. The surgical impact of additional lesions detected by CEM was also analyzed. Results: CEM showed superior accuracy with a mean absolute error of 0.46 mm (95% CI: 0.24–0.68) compared to mammography (4.06 mm) and ultrasound (3.52 mm) (p < 0.00001). Pearson’s correlation between CEM and pathology was exceptionally high (r = 0.995; 95% CI: 0.994–0.996), with similar robustness after log transformation. Inter-reader agreement for CEM was excellent (ICC 0.93; Gwet’s AC1 ~0.96, 95% CI: 0.93–0.98). CEM detected additional lesions in 13.1% of patients, leading to altered surgical management in 6.4%. Background parenchymal enhancement was independently associated with measurement error. Conclusions: CEM provides highly accurate and reproducible tumor size estimation superior to conventional imaging modalities, with potential clinical impact through detection of additional lesions. Its ability to detect additional lesions not seen on mammography or ultrasound has direct implications for surgical decision making, with the potential to reduce reoperations and improve oncologic and cosmetic outcomes. However, high correlation values and selective patient cohorts warrant cautious interpretation. Further multicenter studies are needed to confirm these findings and define CEM’s role in clinical practice.

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.

Introduction: Breast density is a well-recognized factor in breast cancer risk assessment, with higher density linked to increased malignancy risk and reduced sensitivity of conventional mammography. Background parenchymal enhancement (BPE), observed in contrast-enhanced imaging, reflects physiological contrast uptake in non-pathologic breast tissue. While extensively characterized in breast MRI, the role of BPE in contrast-enhanced mammography (CEM) remains uncertain due to inconsistent findings regarding its correlation with breast density and cancer risk. Unlike breast density—standardized through the ACR BI-RADS lexicon—BPE lacks a uniform classification system in CEM, leading to variability in clinical interpretation and research outcomes. To address this gap, we introduce the BPE-CEM Standard Scale (BCSS), a structured four-tiered classification system specifically tailored to the two-dimensional characteristics of CEM, aiming to improve consistency and diagnostic alignment in BPE evaluation. Materials and Methods: In this retrospective single-center study, 213 patients who underwent mammography (MG), ultrasound (US), and contrast-enhanced mammography (CEM) between May 2022 and June 2023 at the “A. Perrino” Hospital in Brindisi were included. Breast density was classified according to ACR BI-RADS (categories A–D). BPE was categorized into four levels: Minimal (< 10% enhancement), Light (10–25%), Moderate (25–50%), and Marked (> 50%). Three radiologists independently assessed BPE in a subset of 50 randomly selected cases to evaluate inter-observer agreement using Cohen’s kappa. Correlations between BPE, breast density, and age were examined through regression analysis. Results: BPE was Minimal in 57% of patients, Light in 31%, Moderate in 10%, and Marked in 2%. A significant positive association was found between higher breast density (BI-RADS C–D) and increased BPE (p < 0.05), whereas lower-density breasts (A–B) were predominantly associated with minimal or light BPE. Regression analysis confirmed a modest but statistically significant association between breast density and BPE (R² = 0.144), while age showed no significant effect. Inter-observer agreement for BPE categorization using the BCSS was excellent (κ = 0.85; 95% CI: 0.78–0.92), supporting its reproducibility. Conclusions: Our findings indicate that breast density is a key determinant of BPE in CEM. The proposed BCSS offers a reproducible, four-level framework for standardized BPE assessment tailored to the imaging characteristics of CEM. By reducing variability in interpretation, the BCSS has the potential to improve diagnostic consistency and facilitate integration of BPE into personalized breast cancer risk models. Further prospective multicenter studies are needed to validate this classification and assess its clinical impact.

This study presents the development of a custom dataset of L2 gap-fill exercises designed to enhance Long Short-Term Memory (LSTM) neural networks in CLIL (Content and Language Integrated Learning) settings for subject-specific courses. Targeting English for Special Purposes (ESP) vocabulary in cybersecurity, privacy, and data protection, the model addresses the dual challenge of domain-specific context mastery and language practice through structured neural network training. The custom dataset of gap-fill exercises for this LSTM model enables simultaneous prediction of missing words and semantic classification, offering learners contextualized language training that is a core requirement of CLIL methodology. Experimental results validate the model’s efficacy, demonstrating its potential as an adaptive support tool for CLIL-based education. This framework establishes a novel synergy between AI-enhanced language learning and subject-specific instruction, providing a scalable template for integrating neural networks into CLIL pedagogy.

This study presents the development of a custom dataset of L2 gap-fill exercises designed to enhance Long Short-Term Memory (LSTM) neural networks in CLIL (Content and Language Integrated Learning) settings for subject-specific courses. Targeting English for Special Purposes (ESP )vocabulary in cybersecurity, privacy, and data protection, the model addresses the dual challenge of domain-specific context mastery and language practice through structured neural network training. The custom dataset of gap-fill exercises for this LSTM model enables simultaneous prediction of missing words and semantic classification, offering learners contextualized language training that is a core requirement of CLIL methodology. Experimental results validate the model’s efficacy, demonstrating its potential as an adaptive support tool for CLIL-based education. This framework establishes a novel synergy between AI-enhanced language learning and subject-specific instruction, providing a scalable template for integrating neural networks into CLIL pedagogy.