Angelo Barra

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, and early diagnosis and accurate disease stratification are still major clinical challenges. Radiomics has emerged as a quantitative imaging approach that extracts high-dimensional features from radiological imaging, with applications in diagnosis, prognosis, radio genomics, and assessment of treatment response. However, its clinical translation is still limited by methodological heterogeneity and a lack of standardization. Aim: This narrative review synthesizes evidence from systematic reviews and meta-analyses on radiomics in thoracic imaging for lung cancer, focusing on clinical applications, methodological limitations, and translational challenges. Methods: A structured search was conducted in PubMed and Scopus using predefined keywords related to radiomics, lung cancer, and imaging modalities. Only peer-reviewed systematic reviews and meta-analyses published in English were included. In total, 27 studies were selected and synthesized using a structured narrative approach guided by the ANDJ checklist. A differential integrative framework was adopted to connect evidence from systematic reviews and meta-analyses with primary empirical studies and policy documents through an intermediate layer of translational recommendations, ensuring a multi-level and interpretation-driven synthesis. Results: Radiomics demonstrated consistent potential across multiple clinical domains, including lesion classification, histological differentiation, molecular profiling, prognostic stratification, and prediction of treatment response. Machine learning and deep learning approaches frequently improved predictive performance. However, key limitations were identified, including heterogeneity in imaging protocols, lack of external validation, small single-centre datasets, and limited reproducibility of radiomic features. Conclusions: Radiomics in lung cancer imaging shows strong clinical potential but remains constrained by methodological and translational barriers. Future progress will depend on standardization, external validation, multimodal data integration, and improved interpretability, alongside alignment with regulatory and clinical implementation frameworks.

Background: Deep medullary vein (DMV) drainage has been suggested as a new biomarker for predicting clinical outcomes in patients with acute ischemic stroke (AIS). We evaluated this hypothesis in patients who received endovascular treatment (EVT) within 24 h of symptom onset. Methods: We performed a retrospective study of consecutive AIS patients at a single institution treated with EVT achieving successful recanalization (final mTICI score ≥2b). DMV drainage was graded on a three-point scale (0-1-2) during the second peak venous phase of mCTA by assessing contrast filling, with grade 2 indicating a favorable DMV profile. Our primary outcomes were functional independence, defined as a modified Rankin Scale (mRS) score of 0–2 at 90 days, and ordinal mRS shift at 90 days. Secondary outcomes were excellent clinical status (mRS 0–1 at 90 days), hemorrhagic transformation, and symptomatic intracranial hemorrhage. We investigated independent associations using multivariable logistic and ordinal regression analyses as appropriate, adjusting for age, sex, baseline mRS, NIHSS at onset, occlusion site, intravenous thrombolysis, onset-to-CT time, and ASPECTS. Results: We included 506 patients; the mean age was 76 years. A favorable DMV profile was present in 394 (78%) patients. We found that DMV doubled the odds of achieving functional independence (OR = 2.22; 95% CI = 1.28–3.85) and was associated with a shift towards better functional outcomes in ordinal regression analysis (cOR = 1.93; 95% CI = 1.24–3.02), whereas we did not find any association between a favorable DMV profile and secondary outcomes. Conclusions: In AIS patients successfully recanalized with EVT, a favorable DMV profile was associated with better functional outcomes. Further investigations may clarify the clinical use and predictive ability of this novel radiological marker.

Background: Breast cancer is still the most common type of cancer worldwide. Advances and the global use of artificial intelligence (AI) have opened up new opportunities to improve diagnostic accuracy and optimize breast cancer screening. This review summarizes the findings from systematic reviews to assess the current situation of AI integration in mammography. Methods: A total of 28 systematic reviews were included and analyzed using a standardized narrative checklist to assess the impact of AI on mammography imaging. Bibliometric analysis and thematic synthesis were used to assess trends, evaluate the performance of AI in different modalities and identify challenges and opportunities for clinical implementation. Results and Discussion: AI technologies show an overall performance comparable to radiologists in terms of sensitivity and specificity, especially when integrated with human interpretation to detect breast cancer in mammography. However, most studies are retrospective, which raises concerns about their generalizability to real-world clinical settings. Key limitations include potential dataset bias—often stemming from the over-representation of specific imaging equipment or clinical environments—limited ethnic and demographic diversity, the lack of model explainability that hinders clinical trust, and an unclear or evolving legal and regulatory framework that complicates integration into standard practice. Conclusions: AI has the potential to transform mammography screening, but its integration into the real world requires prospective validation, ethical safeguards and robust regulatory oversight. Coordinated international efforts are essential to ensure that AI is used safely, fairly and effectively in breast cancer diagnostics.

Background/Objectives: Pain, anxiety, and distress are common yet frequently insufficiently managed issues for patients undergoing radiology and radiotherapy procedures. Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), are emerging as innovative non-pharmacological approaches to alleviate such burdens through engaging interventions. This review, combining scoping and narrative methodologies, seeks to examine the current application, efficacy, and integration of these technologies to enhance patient care and wellbeing within diagnostic and oncological environments. Methods: Employing a mixed scoping and narrative review approach, this study conducted a systematic search of PubMed, EMBASE, Scopus, and Web of Science databases (no date restrictions—search included studies up to May 2025) to identify relevant studies utilizing VR, AR, MR, or XR for mitigating pain, anxiety, or distress in patients undergoing radiology or radiotherapy. Two independent reviewers selected eligible papers, with data extracted systematically. The narrative analysis supplemented the scoping review by providing contextual insights into clinical relevance and technological challenges. Results: The screening process identified 76 articles, of which 27 were assessed for eligibility and 14 met the inclusion criteria. Most studies focused on oncology and primarily employed VR as the immersive technology. VR has shown promising effects in reducing anxiety and pain—particularly during radiotherapy sessions and invasive procedures—and in supporting patient education through engaging, immersive experiences, making it a valuable approach meriting further investigation. Patient acceptance was notably high, especially among those with elevated distress levels. However, findings in radiology were less consistent, likely due to shorter procedure durations limiting the effectiveness of VR. The variability in outcomes highlights the importance of tailoring immersive interventions to specific procedures and patient needs. The narrative component identified key barriers, such as regulatory hurdles, standardization issues, and implementation challenges, that need addressing for broader clinical adoption. Conclusions: Immersive digital therapeutics are evolving from preliminary research tools toward more structured incorporation into clinical practice. Their future success relies on harmonizing technological advancements with patient-focused design and robust clinical evidence. Achieving this will require collaborative efforts among researchers, industry stakeholders, and healthcare providers. The integration of scoping and narrative review methods in this study offers a comprehensive perspective on the current landscape and informs strategic directions for advancing immersive technologies in radiology and radiotherapy.

The integration of artificial intelligence in interventional radiology is an emerging field with transformative potential, aiming to make a great contribution to the health domain. This overview of reviews seeks to identify prevailing themes, opportunities, challenges, and recommendations related to the process of integration. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements in, and future implications of, this domain. In total, 27 review studies were selected through the systematic process. Based on the overview, the integration of artificial intelligence (AI) in interventional radiology (IR) presents significant opportunities to enhance precision, efficiency, and personalization of procedures. AI automates tasks like catheter manipulation and needle placement, improving accuracy and reducing variability. It also integrates multiple imaging modalities, optimizing treatment planning and outcomes. AI aids intra-procedural guidance with advanced needle tracking and real-time image fusion. Robotics and automation in IR are advancing, though full autonomy in AI-guided systems has not been achieved. Despite these advancements, the integration of AI in IR is complex, involving imaging systems, robotics, and other technologies. This complexity requires a comprehensive certification and integration process. The role of regulatory bodies, scientific societies, and clinicians is essential to address these challenges. Standardized guidelines, clinician education, and careful AI assessment are necessary for safe integration. The future of AI in IR depends on developing standardized guidelines for medical devices and AI applications. Collaboration between certifying bodies, scientific societies, and legislative entities, as seen in the EU AI Act, will be crucial to tackling AI-specific challenges. Focusing on transparency, data governance, human oversight, and post-market monitoring will ensure AI integration in IR proceeds with safeguards, benefiting patient outcomes and advancing the field.

The application of chatbots and NLP in radiology is an emerging field, currently characterized by a growing body of research. An umbrella review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This review explores the early developments and potential future impact of these technologies in radiology. The current literature, comprising 15 systematic reviews, highlights potentialities, opportunities, areas needing improvements, and recommendations. This umbrella review offers a comprehensive overview of the current landscape of natural language processing (NLP) and natural language models (NLMs), including chatbots, in healthcare. These technologies show potential for improving clinical decision-making, patient engagement, and communication across various medical fields. However, significant challenges remain, particularly the lack of standardized protocols, which raises concerns about the reliability and consistency of these tools in different clinical contexts. Without uniform guidelines, variability in outcomes may hinder the broader adoption of NLP/NLM technologies by healthcare providers. Moreover, the limited research on how these technologies intersect with medical devices (MDs) is a notable gap in the literature. Future research must address these challenges to fully realize the potential of NLP/NLM applications in healthcare. Key future research directions include the development of standardized protocols to ensure the consistent and safe deployment of NLP/NLM tools, particularly in high-stake areas like radiology. Investigating the integration of these technologies with MD workflows will be crucial to enhance clinical decision-making and patient care. Ethical concerns, such as data privacy, informed consent, and algorithmic bias, must also be explored to ensure responsible use in clinical settings. Longitudinal studies are needed to evaluate the long-term impact of these technologies on patient outcomes, while interdisciplinary collaboration between healthcare professionals, data scientists, and ethicists is essential for driving innovation in an ethically sound manner. Addressing these areas will advance the application of NLP/NLM technologies and improve patient care in this emerging field.

(Background) Over the years, there has been increasing interest in adopting a quality approach in radiology, leading to the strategic pursuit of specific and key performance indicators (KPIs). These indicators in radiology can have significant impacts ranging from radiation protection to integration into digital healthcare. (Purpose) This study aimed to conduct a narrative review on the integration of key performance indicators (KPIs) in radiology with specific key questions. (Methods) This review utilized a standardized checklist for narrative reviews, including the ANDJ Narrative Checklist, to ensure thoroughness and consistency. Searches were performed on PubMed, Scopus, and Google Scholar using a combination of keywords related to radiology and KPIs, with Boolean logic to refine results. From an initial yield of 211 studies, 127 were excluded due to a lack of focus on KPIs. The remaining 84 studies were assessed for clarity, design, and methodology, with 26 ultimately selected for detailed review. The evaluation process involved multiple assessors to minimize bias and ensure a rigorous analysis. (Results and Discussion) This overview highlights the following: KPIs are crucial for advancing radiology by supporting the evolution of imaging technologies (e.g., CT, MRI) and integrating emerging technologies like AI and AR/VR. They ensure high standards in diagnostic accuracy, image quality, and operational efficiency, enhancing diagnostic capabilities and streamlining workflows. KPIs are vital for radiological safety, measuring adherence to protocols that minimize radiation exposure and protect patients. The effective integration of KPIs into healthcare systems requires systematic development, validation, and standardization, supported by national and international initiatives. Addressing challenges like CAD-CAM technology and home-based radiology is essential. Developing specialized KPIs for new technologies will be key to continuous improvement in patient care and radiological practices. (Conclusions) In conclusion, KPIs are essential for advancing radiology, while future research should focus on improving data access and developing specialized KPIs to address emerging challenges. Future research should focus on expanding documentation sources, improving web search methods, and establishing direct connections with scientific associations.

Objectives: Three-dimensional (3D) planning and Patient Specific Instrumentation (PSI) can help the surgeon to obtain more predictable results in Medial Opening Wedge High Tibial Osteotomy (mOW-HTO) than the conventional techniques. We compared the accuracy of the PSI and standard techniques and measured the learning curve for surgery time and number of fluoroscopic shots. Methods: We included the first 12 consecutive cases of mOW-HTO performed with 3D planning and PSI cutting guides and the first 12 non-supervised mOW-HTO performed with the standard technique. We recorded surgery time and fluoroscopic time. We calculated the variation (Δ delta) between the planned target and the postoperative result for Hip Knee Ankle Angle (HKA), mechanical medial Proximal Tibia Angle (MPTA), Joint Line Convergence Angle (JLCA) and tibial slope (TS) and compared it both groups. We also recorded the complication rate. We then calculated the learning curves for surgery time, number of fluoroscopic shots, Δ from target in both groups. CUSUM analysis charts for learning curves were applied between the two groups. Results: Mean surgical time and mean number of fluoroscopic shots were lower in PSI group (48.58±7.87 vs. 58.75±6.86 min; p=0.034 and 10.75±3.93 vs. 18.16±4.93 shots; p<0.001). The postoperative ΔHKA was 0.42±0.51° in PSI vs. 1.25±0.87° in conventional, p=0.005. ΔMPTA was 0.50±0.67° in PSI vs. 3.75±1.48° in conventional, p<0.001; ΔTS was 1.00±0.82° in PSI vs. 3.50±1.57° in conventional, p<0.001. ΔJLCA was 1.83±1.11° in PSI vs. 4±1.41° in conventional, p<0.001. The CUSUM analysis favoured PSI group regarding surgery time (p=0.034) and number of shots (p<0.001) with no learning curve effect for ΔHKA, ΔMPTA, ΔJLCA and ΔTS. Conclusions: PSI cutting guides and 3D planning for HTO are effective in reducing the learning curves for operation time and number of fluoroscopic shots. Accuracy of the procedure has been elevated since the first cases.

This study delves into the transformative potential of integrating augmented reality (AR) within imaging technologies, shedding light on this evolving landscape. Through a comprehensive narrative review, this research uncovers a wealth of literature exploring the intersection between AR and medical imaging, highlighting its growing prominence in healthcare. AR’s integration offers a host of potential opportunities to enhance surgical precision, bolster patient engagement, and customize medical interventions. Moreover, when combined with technologies like virtual reality (VR), artificial intelligence (AI), and robotics, AR opens up new avenues for innovation in clinical practice, education, and training. However, amidst these promising prospects lie numerous unanswered questions and areas ripe for exploration. This study emphasizes the need for rigorous research to elucidate the clinical efficacy of AR-integrated interventions, optimize surgical workflows, and address technological challenges. As the healthcare landscape continues to evolve, sustained research efforts are crucial to fully realizing AR’s transformative impact in medical imaging. Systematic reviews on AR in healthcare also overlook regulatory and developmental factors, particularly in regard to medical devices. These include compliance with standards, safety regulations, risk management, clinical validation, and developmental processes. Addressing these aspects will provide a comprehensive understanding of the challenges and opportunities in integrating AR into clinical settings, informing stakeholders about crucial regulatory and developmental considerations for successful implementation. Moreover, navigating the regulatory approval process requires substantial financial resources and expertise, presenting barriers to entry for smaller innovators. Collaboration across disciplines and concerted efforts to overcome barriers will be essential in navigating this frontier and harnessing the potential of AR to revolutionize healthcare delivery.

Optimizing work shifts in healthcare is crucial for maintaining high standards of service delivery and fostering professional development. This study delves into the emerging field of skill-oriented work shift optimization, focusing specifically on radiographers within the healthcare sector. Through the development of Skills Retention Monitoring (SRH), this research aims to enhance skill monitoring, workload management, and organizational performance. In this study, several key highlights emerged: (a) Introduction of the SRH tool: The SRH tool represents a resource-efficient solution that harnesses existing software infrastructure. A preliminary version, focusing on the radiographers’ professional profile, was released, and after several months of use, it demonstrated effectiveness in optimizing work based on competency monitoring. (b) The SRH tool has thus demonstrated the capacity to generate actionable insights in the organizational context of radiographers. By generating weekly reports, the SRH tool streamlines activity management and optimizes resource allocation within healthcare settings. (c) Application of a Computer-Assisted Web Interviewing (CAWI) tool for pre-release feedback during a training event. (d) Strategic importance of a maintenance and monitoring plan: This plan, rooted in a continuous quality improvement approach and key performance indicators, ensures the sustained effectiveness of the SRH tool. (e) Strategic importance of a transfer plan: Involving professional associations and employing targeted questionnaires, this plan ensures the customization of the tool from the perspective of each profession involved. This is a crucial point, as it will enable the release of tool versions tailored to various professions operating within the hospital sector. As a side result, the tool could allow for a more tailored and personalized medicine both by connecting the insights gathered through the SRH tool with the right competencies for healthcare professionals and with individual patient data. This integration could lead to better-informed decision making, optimizing treatment strategies based on both patient needs and the specific expertise of the healthcare provider. Future directions include deploying the SRH tool within the Pisa hospital network and exploring integration with AI algorithms for further optimization. Overall, this research contributes to advancing work shift optimization strategies and promoting excellence in healthcare service delivery.