Using 1/2 Descending Time in CEUS to Identify Renal Allograft Rejection.

Rationale And Objectives: This study investigates the potential of quantitative Contrast-Enhanced Ultrasound (CEUS) parameters to distinguish between graft dysfunction due to rejection and non-rejection in kidney transplant recipients.

Methods: In this retrospective study, 50 kidney transplant patients who presented elevated serum creatinine or proteinuria were analyzed. They were categorized as rejection or non-rejection based on biopsy outcomes. These classifications were applied in both derivation (n = 33) and validation cohorts (n = 17). Prior to the biopsy, all patients underwent a CEUS. Quantitative parameters derived from the CEUS were further analyzed for their consistency and reliability. Additionally, the relationship between the Banff scores, a standard for diagnosing transplant rejections, and these CEUS parameters was explored.

Results: Significant differences between rejection and non-rejection groups were observed in the CEUS parameters of derivation cohorts. Specifically, Peak Intensity (PI), 1/2 Descending Time (DT/2), Area Under Curve (AUC), and Mean Transit Time (MTT) stood out. Sensitivity and specificity for these parameters were 76.5% and 87.5% for PI, 76.5% and 81.2% for DT/2, 76.5% and 87.5% for AUC, and 68.8% and 94.1% for MTT, respectively. DT/2 and MTT showed superior interobserver agreement compared to PI and AUC. When extrapolating the cutoff values from the derivation cohort to the validation group, DT/2 and AUC exhibited optimal diagnostic precision with positive and negative predictive values being 91.7% vs. 100% and 100% vs. 85.7%, respectively. Additionally, DT/2 effectively differentiated between mild and moderate to severe microvascular inflammation, pivotal in diagnosing antibody-mediated renal transplant rejection.

Conclusion: DT/2 from CEUS parameters presents as a reliable tool to differentiate rejection from non-rejection causes in renal transplant dysfunction. Yet, large-scale, multi-center studies are essential for further validation.