Influence of Breast Density and Menopausal Status on Background Parenchymal Enhancement in Contrast-Enhanced Mammography: Insights from a Retrospective Analysis.

Contrast-enhanced mammography (CEM) has recently gained recognition as an effective alternative to breast magnetic resonance imaging (MRI) for assessing breast lesions, offering both morphological and functional imaging capabilities. However, the phenomenon of background parenchymal enhancement (BPE) remains a critical consideration, as it can affect the interpretation of images by obscuring or mimicking lesions. While the impact of BPE has been well-documented in MRI, limited data are available regarding the factors influencing BPE in CEM and its relationship with breast cancer (BC) characteristics.

Errors in Radiology: A Standard Review.

Radiological interpretations, while essential, are not infallible and are best understood as expert opinions formed through the evaluation of available evidence. Acknowledging the inherent possibility of error is crucial, as it frames the discussion on improving diagnostic accuracy and patient care. A comprehensive review of error classifications highlights the complexity of diagnostic errors, drawing on recent frameworks to categorize them into perceptual and cognitive errors, among others.

Radiologists' Communicative Role in Breast Cancer Patient Management: Beyond Diagnosis.

In the landscape of cancer treatment, particularly in the realm of breast cancer management, effective communication emerges as a pivotal factor influencing patient outcomes. This article delves into the nuanced intricacies of communication skills, specifically spotlighting the strategies embraced by breast radiologists. By examining the ramifications of communication on patient experience, interdisciplinary collaboration, and legal ramifications, this study underscores the paramount importance of empathetic and comprehensive communication approaches.

Patients' Perceptions and Attitudes to the Use of Artificial Intelligence in Breast Cancer Diagnosis: A Narrative Review.

Breast cancer remains the most prevalent cancer among women worldwide, necessitating advancements in diagnostic methods. The integration of artificial intelligence (AI) into mammography has shown promise in enhancing diagnostic accuracy. However, understanding patient perspectives, particularly considering the psychological impact of breast cancer diagnoses, is crucial.

Efficacy and Safety of Transarterial Chemoembolization with DC Beads LUMI in the Treatment of HCC: Experience from a Tertiary Centre.

Purpose: The aim of this study was to describe the safety and efficacy profiles of TACE using DC Beads LUMI.

Materials And Methods: We retrospectively analyzed 90 patients with HCC who underwent TACE with DC Bead LUMI™ between November 2018 and November 2020 at Fondazione IRCCS Cà Granda Policlinico Hospital in Milan, Italy. Patient- and tumour-related factors were registered, including the number of treated lesions, dose of DC Beads LUMI™, dose of Epirubicin, DC Beads LUMI™ target tumour coverage (LC) according to the percentage of target nodule involvement (LC1-0%-25%, LC2-25%-50%, LC3-50%-75%, LC4 75%-100%).

Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review.

The advent of artificial intelligence (AI) represents a real game changer in today's landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems.