Dual-energy computed tomography (DECT) represents a high-precision tool for opportunistic bone mineral density (BMD) assessment, yet optimal algorithmic standardization across anatomical sites remains debated. This opportunistic study evaluated 60 postmenopausal female patients who underwent both dual-energy X-ray absorptiometry (DXA) and contrast-enhanced spectral DECT (80/140 kVp) within a 6-month interval. Quantitative trabecular measurements were obtained at the femoral neck and lumbar spine (L1–L4) across five distinct Base Material Pairs (BMPs) and Effective Atomic Number (Z-Effective). Material decomposition revealed significant anatomical site dependency: fat-based pairs demonstrated superior correlation with DXA BMD at the femoral neck (Calcium–Fat: ρ = 0.703; p ≤ 0.001), whereas water-based pairs performed best in the cellular, red-marrow-rich lumbar spine (CaOxMono–Water: ρ = 0.525; p ≤ 0.001). In contrast, Z-Effective functioned as a robust biomarker, maintaining significant positive linear correlations across both the proximal femur (ρ = 0.652; p ≤ 0.001) and lumbar spine (ρ = 0.489; p ≤ 0.001). Multivariable linear regression demonstrated severe multicollinearity between site-matched BMPs and Z-Effective (VIF > 5.0), confirming they capture identical physical mineral depletion. In conclusion, opportunistic DECT screening requires anatomically tailored BMP selection or, alternatively, Z-Effective alone as a unified spectral parameter to achieve reliable, accurate BMD evaluation during routine CT acquisitions.

Objectives: This study evaluates the impact of neoadjuvant chemotherapy (NAT) on breast tissue density and examines the correlation between density changes and pathological response, while also assessing inter-reader agreement among radiologists and artificial intelligence (AI) tools. Methods: The study included 135 women with triple-negative and/or HER2+ invasive ductal breast cancer who underwent NAT. Digital mammography was performed pre- and post-NAT, with evaluations by radiologists of varying experience levels and comparison using an in-house developed AI tool based on AlexNet CNN architecture. Statistical analyses included Wilcoxon signed-rank test, linear regression, McNemar test, Spearman’s rank correlation, and intraclass correlation coefficients (ICC). Results: Post-NAT assessments showed a significant decrease in breast tissue density, with initial heterogeneously dense and extremely dense breasts decreasing from 32% and 15% to 18% and 7%, respectively (P < .003). Significant relationships were found between density reduction and age, menopausal status, and baseline density, and the reduction correlated moderately with pathological response to NAT. Intraclass correlation coefficient values reflected good agreement among experienced radiologists (0.68), fair agreement between expert and resident (0.55), poor agreement between expert and non-radiologist (0.39), and substantial agreement between the expert and AI tool (0.78). Conclusions: Neoadjuvant chemotherapy significantly reduces breast tissue density, correlating with pathological response, and AI tools provide consistent density evaluations. Artificial intelligence tools show promise in improving assessment consistency, with substantial agreement with expert radiologists. The findings highlight the need for standardized protocols and training to optimize breast density evaluations and suggest further refinement and validation of AI tools for clinical use. Advances in knowledge: This study evaluates the impact of NAT on breast tissue density while investigating its correlation with pathological response and inter-reader agreement among radiologists and AI tools. It contributes to the field by demonstrating the potential of AI to improve consistency in density evaluations and emphasizing the importance of standardized protocols and training for optimized clinical assessments.

Erdheim–Chester disease (ECD) is a rare, clonal non-Langerhans cell histiocytic neoplasm driven by activating mutations in the mitogen-activated protein kinase (MAPK) pathway, most notably the BRAF-V600E mutation. The discovery of these molecular alterations has shifted the therapeutic paradigm from non-specific immunosuppression to personalized targeted therapies, such as BRAF and MEK inhibitors, dramatically improving patient outcomes. Given its protean and insidious systemic manifestations, imaging plays an indispensable role in the personalized diagnostic and therapeutic pathway of ECD. Metadiaphyseal symmetrical osteosclerosis represents the skeletal hallmark, while extraskeletal involvement typically affects the cardiovascular, retroperitoneal, pulmonary, and central nervous systems, yielding pathognomonic imaging signatures such as the “coated aorta” and “hairy kidney.” Advanced cross-sectional techniques—including CT, MRI, and organ-specific mapping protocols—are crucial for baseline phenotype characterization, risk stratification, and guiding biopsies toward hypermetabolic sites for biomarker detection. Furthermore, molecular imaging with ¹⁸F-FDG PET/CT has emerged as the cornerstone for baseline systemic staging and quantitative metabolic response monitoring via PERCIST criteria, enabling early assessment of targeted treatment efficacy long before morphological changes occur. This review illustrates the multimodality imaging spectrum of ECD based on our institutional experience, emphasizing how precision imaging integrates with molecular profiling to guide personalized patient management.