A rat model of acute kidney injury through systemic hypoperfusion evaluated by micro-US, color and PW-Doppler.

AIM: To create an animal model of acute renal ischemia induced by systemic hypoperfusion, controllable and reproducible to study, in real time, hemorrhagic shock changes with micro-imaging. ANIMALS AND METHODS: Hemorrhagic shock was induced in rats activating a syringe pump setup to remove 1 mL/min of blood, through the femoral artery catheter. The withdrawal was continued until the mean arterial pressure (MAP) dropped to 25-30 mmHg. For the next 60 min, the MAP was maintained at a constant pressure value, by automatic pump infusion and withdrawal. Micro-ultrasound imaging was performed using the Vevo 2100 system with the MS250 transducer (13-24 MHz). Renal size, morphology and echogenicity were evaluated in B-mode. Renal blood flow was evaluated using color and PW-Doppler. RESULTS: After 1 h of ischemia, B-mode images documented slight changes in kidney echogenicity. Color and PW-Doppler analysis showed a reduction in renal blood flow in kidneys during the hypoperfusion with a progressive and significant change from baseline values of resistive index (RI). At the histological evaluation, 60 min of hypoperfusion resulted in ischemic changes in the kidneys. CONCLUSIONS: The results of this experimental study encourage the use of the described model to study acute renal ischemia trough severe hypoperfusion. The histological data confirmed that the model was able to produce injury in renal parenchyma. It can be used to assess acute ischemic damage not only in the kidney but also in other organs by using all available dedicated small animals imaging techniques.

Can microvascular invasion in hepatocellular carcinoma be predicted by diagnostic imaging? A critical review.

Imaging still has a limited capacity to detect microvascular invasion (mVI). The objective of this critical review is the evaluation of the most significant predictors of mVI in hepatocellular carcinoma (HCC) detectable by computed tomography, PET/computed tomography and MRI using a mathematical model. We systematically reviewed 15 observational studies from 2008 to 2018 to analyze factors with most impact on mVI detection. The most significant predictors of mVI correlating with imaging techniques were considered. From 1902 patients considered, we individuated 30 total predictors of mVI in a multivariate analysis. The most frequent predictors related to the highest presence with mVI in HCC were: α-fetoprotein (p < 0.0001), tumor size (p < 0.0001) and number of HCC nodules (p = 0.0020).