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.

The widespread implementation of population-based mammographic screening has markedly increased the detection of ductal carcinoma in situ (DCIS), without a proportional reduction in breast cancer-specific mortality. This divergence has intensified concerns regarding overdiagnosis and overtreatment and has prompted increasing interest in treatment de-escalation and active surveillance strategies. Breast imaging remains indispensable for DCIS detection, extent assessment, and longitudinal monitoring. However, although imaging features correlate with histopathologic risk factors at the population level, their ability to predict individual biological progression is inherently probabilistic and limited. Overinterpretation of imaging phenotypes as surrogates of invasive destiny risks inappropriate reassurance or unjustified therapeutic escalation, particularly in the context of high-sensitivity modalities that may overestimate disease extent or trigger additional interventions without proven outcome benefits. This review examines the modality-specific roles of mammography, ultrasound, breast magnetic resonance imaging (MRI), contrast-enhanced mammography (CEM), and emerging artificial intelligence (AI) approaches within contemporary DCIS management, with particular attention to their implementation in active surveillance trials such as LORIS, COMET, LORD, and LORETTA. Across modalities, imaging primarily reflects lesion morphology, spatial distribution, and vascular behaviour, and functions most reliably as a risk-filtering and safety-gating instrument aimed at excluding radiologically unsafe scenarios, including occult invasion, underestimated disease extent, or imaging evolution incompatible with continued observation. By delineating both the capabilities and the epistemological limits of imaging, this review proposes a structured clinical decision framework in which imaging supports—but does not independently determine—risk-adapted management. Disciplined integration of imaging into multidisciplinary decision-making is essential to enable safe de-escalation, prevent false reassurance, and align DCIS care with patient-centred and value-based principles.

Building upon the BCSS framework validated in Part 1, this study addresses the persistent interobserver variability in background parenchymal enhancement (BPE) assessment through computational automation. Utilizing the same dataset of 213 contrast-enhanced mammography (CEM) cases, we developed a deep learning model (artificial neural networks) to automate BCSS categorization. The model reduced prediction error by 26% compared to traditional methods and improved inter-reader agreement (κ = 0.82, a 40% increase from baseline), with the strongest performance observed in high breast density cases (BI-RADS C/D), where interpretative variability most significantly affects clinical decisions. Key performance metrics including AUC (0.75), precision (0.72), and recall (0.69) demonstrate the system’s clinical viability. This AI-driven implementation of the BCSS significantly enhances standardization while preserving the diagnostic role of radiologists. Combined with the observational foundations established in Part 1, it provides an end-to-end solution for consistent BPE assessment in CEM.

Background: Preoperative contrast-enhanced breast imaging, particularly magnetic resonance imaging (MRI) and contrast-enhanced mammography (CEM), has become increasingly integrated into the surgical management of breast cancer. Although these techniques improve detection of multifocal, multicentric, and contralateral disease, their clinical value cannot be defined by sensitivity alone. Methods: This article was conceived as a critical narrative review supported by a targeted literature search primarily conducted in PubMed/MEDLINE, with additional cross-checking in Scopus and reference lists. English-language studies addressing preoperative MRI or CEM in relation to surgical planning, re-excision, mastectomy, additional disease detection, and oncologic outcomes were prioritized. Overall, 38 clinically relevant studies were included in the qualitative narrative synthesis. Results: A decision-oriented reading of the literature suggests that preoperative contrast-enhanced imaging does not produce a uniform clinical effect, but rather reshapes management through recurring patterns. Three main patterns can be identified: (1) refinement of surgical extent through improved disease mapping, most clearly in invasive lobular carcinoma, where MRI may reduce repeat surgery without consistently increasing mastectomy rates; (2) pre-emptive escalation aimed at avoiding reoperation, particularly in ductal carcinoma in situ, where lower re-excision rates are often accompanied by higher initial mastectomy rates; and (3) decision amplification driven by the detection of additional or indeterminate disease, frequently resulting in additional biopsies and treatment escalation without consistent evidence of long-term oncologic benefit. Across large observational cohorts, routine preoperative MRI has not been consistently associated with improvements in overall survival, disease-free survival, or locoregional control in unselected populations. These patterns should be understood as interpretive categories derived from recurring trends in a heterogeneous literature, rather than as formally validated decision classifications. Conclusions: The value of preoperative contrast-enhanced breast imaging depends less on sensitivity itself than on how imaging findings are translated into surgical action. Selective, question-driven use appears most valuable in settings such as invasive lobular carcinoma and assessment of nipple–areola complex involvement, whereas routine use in unselected populations often appears to increase surgical intensity more clearly than it improves long-term oncologic outcomes. Contrast-enhanced imaging should therefore be regarded as a decision-shaping tool whose clinical usefulness depends on explicit interpretive and multidisciplinary thresholds.

Introduction Breast density (BD) influences breast cancer risk and back-ground parenchymal enhancement (BPE) in contrast-enhanced mammography (CEM), yet BPE assessment lacks standardization. This study establishes observational foun-dations for a unified classification sys-tem. Materials and Methods We analyzed 213 CEM cases (retrospective single-center co-hort, 2022–2023). BD was categorized via ACR BI-RADS (A–D), and BPE via a novel 4-tier scale (BPE-CEM Standard Scale, BCSS: Minimal/Light/Moderate/Marked). In-terobserver agreement (3 radiologists; 50 random cases) and BD-BPE correlations were assessed using Cohen’s kappa and regression analysis. ResultsHigher BD (BI-RADS C/D) significantly cor-related with elevated BPE (p < 0.05; R² = 0.144). BPE distribution: Minimal (57%), Light (31%), Moderate (10%), Marked (2%). The BCSS showed excellent reproducibility (κ = 0.85; 95% CI: 0.78–0.92). Conclusion BD is a key determinant of BPE in CEM. The BCSS provides a reproducible framework for standardized assessment, reducing interpretive variability. This obser-vational groundwork enables computational automation in Parte 2