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. A special emphasis is placed on the utilization of the SPIKES protocol, a structured method for conveying sensitive health information, and the deployment of strategies for navigating challenging conversations. Furthermore, the work encompasses the significance of communication with caregivers, the integration of artificial intelligence, and the acknowledgement of patients' psychological needs. By adopting empathetic communication methodologies and fostering multidisciplinary collaboration, healthcare practitioners have the potential to enhance patient satisfaction, promote treatment adherence, and augment the overall outcomes within breast cancer diagnosis. This paper advocates for the implementation of guidelines pertaining to psychological support and the allocation of sufficient resources to ensure the provision of holistic and patient-centered cancer care. The article stresses the need for a holistic approach that addresses patients' emotional and psychological well-being alongside medical treatment. Through thoughtful and empathetic communication practices, healthcare providers can profoundly impact patient experiences and breast cancer journeys in a positive manner.

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. This narrative review synthesizes literature from 2000 to 2023 to examine breast cancer patients' attitudes towards AI in breast imaging, focusing on trust, acceptance, and demographic influences on these views. Methodologically, we employed a systematic literature search across databases such as PubMed, Embase, Medline, and Scopus, selecting studies that provided insights into patients' perceptions of AI in diagnostics. Our review included a sample of seven key studies after rigorous screening, reflecting varied patient trust and acceptance levels towards AI. Overall, we found a clear preference among patients for AI to augment rather than replace the diagnostic process, emphasizing the necessity of radiologists' expertise in conjunction with AI to enhance decision-making accuracy. This paper highlights the importance of aligning AI implementation in clinical settings with patient needs and expectations, emphasizing the need for human interaction in healthcare. Our findings advocate for a model where AI augments the diagnostic process, underlining the necessity for educational efforts to mitigate concerns and enhance patient trust in AI-enhanced diagnostics.

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%). Treatment efficacy was obtained through reviewing the follow-up imaging for evidence of response in target lesion(s), according to modified response criteria in solid tumours (mRECIST) criteria with the following outcomes: complete response (CR), partial response (PR), stable disease (SD) and progressive disease (PD). Safety assessment was based on the quantitative and qualitative recording of the adverse events, classified according to CIRSE classification. RESULTS: Seventy-two patients were enrolled, and 95 procedures were carried out. We observed a target tumour response rate at 1 month with CR in 68%, PR in 10.3% 11.8%, SD in 13%, PD in 7.2%, and an overall tumour(s) (whole liver) response at 1 month with CR in 58.9%, PR in 12.6%, SD in 10.5% and PD in 18%. We found a significant association (p < 0.01) between tumour response CR or CR + PR and the number of the target lesion(s). CIRSE classification grade I and grade II complications were recorded, respectively, in 11 (11.6%) and 6 (6.3%) procedures. No grade III-IV-V complications occurred. CONCLUSION: TACE using DC Beads LUMI is a safe and effective treatment option for patients with HCC.

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. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.