Optimizing Portal Venous Phase Acquisition Quality of Contrast-Enhanced CT in Oncology
Year: 2026
Type: article
Abstract: 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.
Source: Diagnostics
Institutions: Université Paris-Saclay, Institut Gustave Roussy, Columbia University Irving Medical Center, Centre National de la Recherche Scientifique, Inserm, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, Bordeaux Population Health, Hôpital Pellegrin, Université de Bretagne Occidentale, Memorial Sloan Kettering Cancer Center
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7222497883