Pancreatic CT perfusion

• Verfasst von: Skornitzke, Stephan [VerfasserIn]
• Verfasst von: Vats, Neha [VerfasserIn]
• Verfasst von: Mayer, Philipp [VerfasserIn]
• Verfasst von: Kauczor, Hans-Ulrich [VerfasserIn]
• Verfasst von: Stiller, Wolfram [VerfasserIn]
• Titel: Pancreatic CT perfusion
• Titelzusatz: quantitative meta-analysis of disease discrimination, protocol development, and effect of CT parameters
• Verf.angabe: Stephan Skornitzke, Neha Vats, Philipp Mayer, Hans-Ulrich Kauczor and Wolfram Stiller
• Jahr: 2023
• Umfang: 14 S.
• Fussnoten: Veröffentlicht: 21. Juli 2023 ; Gesehen am 26.09.2023
• Titel Quelle: Enthalten in: Insights into imaging
• Ort Quelle: Berlin : Springer, 2010
• Jahr Quelle: 2023
• Band/Heft Quelle: 14(2023), Artikel-ID 132, Seite 1-14
• ISSN Quelle: 1869-4101
• DOI: doi:10.1186/s13244-023-01471-0
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Pancreas
• Sach-SW: Perfusion imaging
• Sach-SW: X-ray computed tomography
• K10plus-PPN: 1860270964

Abstract

Background: This study provides a quantitative meta-analysis of pancreatic CT perfusion studies, investigating choice of study parameters, ability for quantitative discrimination of pancreatic diseases, and influence of acquisition and reconstruction parameters on reported results. Methods: Based on a PubMed search with key terms ‘pancreas’ or ‘pancreatic,’ ‘dynamic’ or ‘perfusion,’ and ‘computed tomography’ or ‘CT,’ 491 articles published between 1982 and 2020 were screened for inclusion in the study. Inclusion criteria were: reported original data, human subjects, five or more datasets, measurements of pancreas or pancreatic pathologies, and reported quantitative perfusion parameters. Study parameters and reported quantitative measurements were extracted, and heterogeneity of study parameters and trends over time are analyzed. Pooled data were tested with weighted ANOVA and ANCOVA models for differences in perfusion results between normal pancreas, pancreatitis, PDAC (pancreatic ductal adenocarcinoma), and non-PDAC (e.g., neuroendocrine tumors, insulinomas) and based on study parameters. Results: Reported acquisition parameters were heterogeneous, except for contrast agent amount and injection rate. Tube potential and slice thickness decreased, whereas tube current time product and scan coverage increased over time. Blood flow and blood volume showed significant differences between pathologies (both p < 0.001), unlike permeability (p = 0.11). Study parameters showed a significant effect on reported quantitative measurements (p < 0.05). Conclusions: Significant differences in perfusion measurements between pathologies could be shown for pooled data despite observed heterogeneity in study parameters. Statistical analysis indicates most influential parameters for future optimization and standardization of acquisition protocols.