Background: Portal venous phase (PVP) contrast-enhanced CT is a cornerstone for response assessment in early-phase oncology trials, yet contrast-enhancement phase timing remains insufficiently monitored across sites. We assessed PVP timing using a validated artificial intelligence (AI) quality control tool (CECT-QC). Methods: In this retrospective multicenter study, 402 CT examinations from 402 patients enrolled in phase I oncology trials across three European institutions were analyzed using CECT-QC, which quantifies aortic and portal venous enhancement to classify contrast-enhancement phase. The primary analysis included 335 standard CT examinations. Optimal PVP classification was compared (i) across institutions, (ii) before vs. after French regulatory changes impacting contrast delivery, and (iii) across scanner technologies (photon-counting CT [PCCT] vs. standard CT). Results: Among the 335 standard CT examinations included in the primary analysis, 53.1% (178/335) were classified by CECT-QC as optimal PVP. Optimal PVP rates were 39.0% (32/82) at Institution A, 56.9% (87/153) at Institution B, and 59.0% (59/100) at Institution C, with a significant adjusted institutional association (p = 0.039). No significant temporal association was observed before versus after the legislative change (p = 0.716). At Institution A, standard CT was associated with higher odds of optimal PVP classification than PCCT (OR 2.24, 95% CI 1.07–4.68; p = 0.031), while PCCT showed higher aortic and portal venous attenuation (both p < 0.001). Conclusions: Approximately half of standard CT examinations in phase I oncology trials were classified as optimal PVP. Classification varied significantly across institutions and by scanner technology, whereas no significant temporal association was observed before versus after the legislative change. These findings support continued quality-control monitoring, protocol harmonization, and adaptation of classification tools to emerging CT platforms.

Background: Accurate identification of macro-reentrant atrial tachycardia (AT) circuits is critical for successful ablation but remains challenging with conventional mapping techniques. The aim of this study was to automatically detect macro-reentrant AT loops from high-density local activation time (LAT) maps. Methods: We developed an algorithm for automated detection of macro-reentrant AT circuits using LAT-derived directed graphs. Compared to previous graph-based approaches, the algorithm is designed to identify the fastest-conducting reentrant pathways and cluster them by rotational orientation (clockwise vs. counterclockwise) to distinguish single- from dual-loop circuits. The algorithm was applied retrospectively to 60 macro-reentrant scar-related AT cases mapped with CARTO or Ensite from two institutions. The results were compared with blinded expert electrophysiologist annotations of loop location and single- vs. dual-loop classification. Results: The 60 cases included 16 right atrial and 44 left atrial ATs from 51 patients. Expert review identified 57% single-loop and 43% dual-loop circuits. Compared with expert annotation, the algorithm correctly identified anatomical loop locations with 88% accuracy and correctly distinguished single- vs. dual-loop ATs in 93% of cases. Conclusion: Our LAT graph-based algorithm automatically identified single- and dual-loop macro-reentrant AT circuits. Localizing these pathways may provide insight into circuit mechanisms and help guide ablation. CLINICAL PERSPECTIVE: What is Known: Accurate characterization of macro-reentrant atrial tachycardias (ATs) is essential for effective catheter ablation, yet identifying these circuits from local activation time (LAT) maps remains challenging and operator dependent. What the Study Adds: We developed and validated a graph-based algorithm that automatically identifies macro-reentrant AT circuits from LAT maps with high agreement compared to expert electrophysiologist annotation. The algorithm localizes reentrant loop pathways and distinguishes single- from dual-loop circuits. Localizing these pathways may provide insight into circuit mechanisms and help guide ablation.