The EURO-ENDO registry aimed to study the management and outcomes of patients with infective endocarditis (IE). Prospective cohort of 3116 adult patients (2470 from Europe, 646 from non-ESC countries), admitted to 156 hospitals in 40 countries between January 2016 and March 2018 with a diagnosis of IE based on ESC 2015 diagnostic criteria. Clinical, biological, microbiological, and imaging [echocardiography, computed tomography (CT) scan, ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT)] data were collected. Infective endocarditis was native (NVE) in 1764 (56.6%) patients, prosthetic (PVIE) in 939 (30.1%), and device-related (CDRIE) in 308 (9.9%). Infective endocarditis was community-acquired in 2046 (65.66%) patients. Microorganisms involved were staphylococci in 1085 (44.1%) patients, oral streptococci in 304 (12.3%), enterococci in 390 (15.8%), and Streptococcus gallolyticus in 162 (6.6%). ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography was performed in 518 (16.6%) patients and presented with cardiac uptake (major criterion) in 222 (42.9%) patients, with a better sensitivity in PVIE (66.8%) than in NVE (28.0%) and CDRIE (16.3%). Embolic events occurred in 20.6% of patients, and were significantly associated with tricuspid or pulmonary IE, presence of a vegetation and Staphylococcus aureus IE. According to ESC guidelines, cardiac surgery was indicated in 2160 (69.3%) patients, but finally performed in only 1596 (73.9%) of them. In-hospital death occurred in 532 (17.1%) patients and was more frequent in PVIE. Independent predictors of mortality were Charlson index, creatinine > 2 mg/dL, congestive heart failure, vegetation length > 10 mm, cerebral complications, abscess, and failure to undertake surgery when indicated. Infective endocarditis is still a life-threatening disease with frequent lethal outcome despite profound changes in its clinical, microbiological, imaging, and therapeutic profiles.

Aims: Cardiac implantable electronic device (CIED) infection rates are increasing. Worldwide compliance and disparities to published guidelines for the prevention, diagnosis and management of these conditions are not well elucidated. The purpose of this survey, therefore, was to clarify these issues through an inquiry to arrhythmia-related associations and societies worldwide. Methods and results: A questionnaire comprising 15 questions related to CIED infections was distributed among members of seven arrhythmia societies worldwide. A total of 234 centres in 62 countries reported implantation rates of which 159 (68.0%) performed more than 200 device implantations per year and 14 (6.0%) performed fewer than 50 implantations per year. The reported rates of CIED infections for 2017 were ≤2% in 78.7% of the centres, while the infection rates exceeded 5% in 7.8% of the centres. Preventive measures for CIED infection differed from published recommendations and varied among different regions mainly in terms of pocket irrigation and administering post-operative antimicrobial therapy the use of which was reported by 39.9% and 44% of the respondents, respectively. Antibacterial envelopes were used by 37.7% of the respondents in selected circumstances. In terms of pocket infection management, 62% of the respondents applied complete system removal as an initial step. Diagnostic pocket needle aspiration and pocket surgical debridement were reported by 15.8% and 11.8% of centres, respectively. Conclusion: Clinical practices for prevention and management of CIED do not fully comply with current recommendations and demonstrate considerable regional disparities. Further education and programmes for improved implementation of guidelines are mandatory.

Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer—being one of the most extensively malignancy studied by hybrid medical imaging—has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.

Background: Prostate-specific membrane antigen (PSMA) is overexpressed on the endothelial cells of tumor neo-vessels of several solid malignancies, including differentiated thyroid cancer (DTC). We aimed to test the potential role of PSMA as a biomarker for DTC aggressiveness and outcome prediction. We retrospectively screened all patients who underwent thyroidectomy between 1 January 2010 and 31 December 2017 in our institution. Applying the inclusion (histological diagnosis of thyroid cancer and tissue availability) and exclusion criteria (no clinical or follow-up data or diagnosis of medullary thyroid cancer), a cohort of 59 patients was selected. The monoclonal mouse anti-human PSMA antibody was used to stain tissue sections. A 3-point scale was used to score PSMA positivity: 0–5% expression was considered as negative (score 0), 6–50% as moderately positive (score 1), and 51–100% as highly positive (score 2). A cumulative score (0–10%, 11–79%, and 80–100%) was also explored. Univariate and multivariate logistic regression analyses were performed to predict the presence of distant metastases, chosen as endpoint of aggressiveness. The area under the curve (AUC) was calculated. Cox models were built to predict patient outcome in terms of recurrence, iodine refractoriness, and status at last follow-up, which were calculated using the Kaplan-Meier failure function. Results: At immunostaining, 12, 25, and 22 patients had scores of 0, 1, and 2, respectively. According to the cumulative score, PSMA expression was ≤ 10% in 17 cases, 11–79% in 31 cases, and ≥ 80% in 11 cases. At multivariate analysis, age, sex, histotype, vascular invasion, T and N parameters, and PSMA positivity were significant predictors of distant metastases. The AUC was 0.92. Recurrence or progression occurred in 19/59 patients. Twelve patients developed radioiodine (RAI) refractoriness, after a median time of 17 months (range 2–32). One patient died of DTC; 46 of the 58 patients alive at last follow-up were disease free. Median DFS was 23 months (range 3–82). The final multivariate model to predict RAI refractoriness included as covariates the stage, high PSMA expression (≥ 80%), and the interaction between moderate PSMA expression (11–79%) and stage. Conclusions: PSMA, a marker of neovasculature formation expressed by DTC, contributes in the prediction of tumor aggressiveness and patient outcome.