Early assessment of acute kidney injury in severe acute pancreatitis with multimodal DWI: an animal model

Assessment of Acute Kidney Injury using MRI

There has been growing interest in using quantitative magnetic resonance imaging (MRI) to describe and understand the pathophysiology of acute kidney injury (AKI). The ability to assess kidney blood flow, perfusion, oxygenation, and changes in tissue microstructure at repeated timepoints is hugely appealing, as this offers new possibilities to describe nature and severity of AKI, track the time-course to recovery or progression to chronic kidney disease (CKD), and may ultimately provide a method to noninvasively assess response to new therapies. This could have significant clinical implications considering that AKI is common (affecting more than 13 million people globally every year), harmful (associated with short and long-term morbidity and mortality), and currently lacks specific treatments. However, this is also a challenging area to study. After the kidney has been affected by an initial insult that leads to AKI, complex coexisting processes ensue, which may recover or can progress to CKD. There are various preclinical models of AKI (from which most of our current understanding derives), and these differ from each other but more importantly from clinical AKI. These aspects are fundamental to interpreting the results of the different AKI studies in which renal MRI has been used, which encompass different settings of AKI and a variety of MRI measures acquired at different timepoints. This review aims to provide a comprehensive description and interpretation of current studies (both preclinical and clinical) in which MRI has been used to assess AKI, and discuss future directions in the field. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

Diffusion Tensor and Kurtosis MRI-Based Radiomics Analysis of Kidney Injury in Type 2 Diabetes

BACKGROUND

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) can provide quantitative parameters that show promise for evaluation of diabetic kidney disease (DKD). The combination of radiomics with DTI and DKI may hold potential clinical value in detecting DKD.

PURPOSE

To investigate radiomics models of DKI and DTI for predicting DKD in type 2 diabetes mellitus (T2DM) and evaluate their performance in automated renal parenchyma segmentation.

STUDY TYPE

Prospective.

POPULATION

One hundred and sixty-three T2DM patients (87 DKD; 63 females; 27-80 years), randomly divided into training cohort (N = 114) and validation cohort (N = 49).

FIELD STRENGTH/SEQUENCE

1.5-T, diffusion spectrum imaging (DSI) with 9 different b-values.

ASSESSMENT

The images of DSI were processed to generate DKI and DTI parameter maps, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). The Swin UNETR model was trained with 5-fold cross-validation using 100 samples for renal parenchyma segmentation. Subsequently, radiomics features were automatically extracted from each parameter map. The performance of the radiomics models on the validation cohort was evaluated by utilizing the receiver operating characteristic (ROC) curve.

STATISTICAL TESTS

Mann-Whitney U test, Chi-squared test, Pearson correlation coefficient, least absolute shrinkage and selection operator (LASSO), dice similarity coefficient (DSC), decision curve analysis (DCA), area under the curve (AUC), and DeLong's test. The threshold for statistical significance was set at P < 0.05.

RESULTS

The DKI_MD achieved the best segmentation performance (DSC, 0.925 ± 0.011). A combined radiomics model (DTI_FA, DTI_MD, DKI_FA, DKI_MD, and DKI_RD) showed the best performance (AUC, 0.918; 95% confidence interval [CI]: 0.820-0.991). When the threshold probability was greater than 20%, the combined model provided the greatest net benefit. Among the single parameter maps, the DTI_FA exhibited superior diagnostic performance (AUC, 887; 95% CI: 0.779-0.972).

DATA CONCLUSION

The radiomics signature constructed based on DKI and DTI may be used as an accurate and non-invasive tool to identify T2DM and DKD.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.