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Article Abstract
Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection.
Materials And Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations.
Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of non-TCMR categories (Banff categories 2, 5, and 6) (=0.026) and showed equal or higher AUC values than multiple machine learning classifiers.
Conclusion: Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955394 | PMC |
| http://dx.doi.org/10.3349/ymj.2024.0082 | DOI Listing |
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