Development of a Neurotensin-Derived 68Ga-Labeled PET Ligand with High In Vivo Stability for Imaging of NTS1 Receptor-Expressing Tumors

Overexpression of the neurotensin receptor type 1 (NTS1R), a peptide receptor located at the plasma membrane, has been reported for a variety of malignant tumors. Thus, targeting the NTS1R with 18F- or 68Ga-labeled ligands is considered a straightforward approach towards in vivo imaging of NTS1R-expressing tumors via positron emission tomography (PET). The development of suitable peptidic NTS1R PET ligands derived from neurotensin is challenging due to proteolytic degradation. In this study, we prepared a series of NTS1R PET ligands based on the C-terminal fragment of neurotensin (NT(8–13), Arg8-Arg9-Pro10-Tyr11-Ile12-Leu13) by attachment of the chelator 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) via an Nω-carbamoylated arginine side chain. Insertion of Ga3+ in the DOTA chelator gave potential PET ligands that were evaluated concerning NTS1R affinity (range of Ki values: 1.2–21 nM) and plasma stability. Four candidates were labeled with 68Ga3+ and used for biodistribution studies in HT-29 tumor-bearing mice. [68Ga]UR-LS130 ([68Ga]56), containing an N-terminal methyl group and a β,β-dimethylated tyrosine instead of Tyr11, showed the highest in vivo stability and afforded a tumor-to-muscle ratio of 16 at 45 min p.i. Likewise, dynamic PET scans enabled a clear tumor visualization. The accumulation of [68Ga]56 in the tumor was NTS1R-mediated, as proven by blocking studies

Thrombosis in vasculitis: from pathogenesis to treatment

In recent years, the relationship between inflammation and thrombosis has been deeply investigated and it is now clear that immune and coagulation systems are functionally interconnected. Inflammation-induced thrombosis is by now considered a feature not only of autoimmune rheumatic diseases, but also of systemic vasculitides such as Behçet’s syndrome, ANCA-associated vasculitis or giant cells arteritis, especially during active disease. These findings have important consequences in terms of management and treatment. Indeed, Behçet’syndrome requires immunosuppressive agents for vascular involvement rather than anticoagulation or antiplatelet therapy, and it is conceivable that also in ANCA-associated vasculitis or large vessel-vasculitis an aggressive anti-inflammatory treatment during active disease could reduce the risk of thrombotic events in early stages. In this review we discuss thrombosis in vasculitides, especially in Behçet’s syndrome, ANCA-associated vasculitis and large-vessel vasculitis, and provide pathogenetic and clinical clues for the different specialists involved in the care of these patients

Faster and Better: How Anomaly Detection Can Accelerate and Improve Reporting of Head Computed Tomography

Background: Most artificial intelligence (AI) systems are restricted to solving a pre-defined task, thus limiting their generalizability to unselected datasets. Anomaly detection relieves this shortfall by flagging all pathologies as deviations from a learned norm. Here, we investigate whether diagnostic accuracy and reporting times can be improved by an anomaly detection tool for head computed tomography (CT), tailored to provide patient-level triage and voxel-based highlighting of pathologies. Methods: Four neuroradiologists with 1–10 years of experience each investigated a set of 80 routinely acquired head CTs containing 40 normal scans and 40 scans with common pathologies. In a random order, scans were investigated with and without AI-predictions. A 4-week wash-out period between runs was included to prevent a reminiscence effect. Performance metrics for identifying pathologies, reporting times, and subjectively assessed diagnostic confidence were determined for both runs. Results: AI-support significantly increased the share of correctly classified scans (normal/pathological) from 309/320 scans to 317/320 scans (p = 0.0045), with a corresponding sensitivity, specificity, negative- and positive- predictive value of 100%, 98.1%, 98.2% and 100%, respectively. Further, reporting was significantly accelerated with AI-support, as evidenced by the 15.7% reduction in reporting times (65.1 ± 8.9 s vs. 54.9 ± 7.1 s; p < 0.0001). Diagnostic confidence was similar in both runs. Conclusion: Our study shows that AI-based triage of CTs can improve the diagnostic accuracy and accelerate reporting for experienced and inexperienced radiologists alike. Through ad hoc identification of normal CTs, anomaly detection promises to guide clinicians towards scans requiring urgent attention