[Evaluation of brain age changes in patients with liver cirrhosis and hepatic encephalopathy with deep learning...

Objective: To investigate the brain aging in patients with cirrhosis and hepatic encephalopathy(HE), constructed a prediction model of brain age based on deep learning and T₁ high-resolution MRI, and try to reveal the specific regions where cirrhosis and HE accelerating brain aging. Methods: A cross-sectional study. A brain age prediction model based on the 3D full convolutional neural network was constructed through T₁ high-resolution MRI data from 3 609 healthy individuals across eight global public datasets. The mean absolute error (MAE) between actual age and predicted brain age, Pearson correlation coefficient (r) and determination coefficient (R²) were calculated to evaluate the accuracy of the model's predictions. A test set (n=555) from the Human Connectome Project was used to assess the accuracy of the model. A total of 136 patients with cirrhosis were recruited from Tianjin First Central Hospital as the case group (79 patients with cirrhosis without HE and 57 patients with cirrhosis with HE), and 70 healthy individuals were recruited from the society as the healthy control group during the same period. Brain-predicted age difference (Brain-PAD), digital connection-A (NCT-A) and digital-symbol test (DST) scores of all subjects were calculated for all subjects to assess brain aging and cognitive function in the healthy control group, the cirrhosis without HE group, and the cirrhosis with HE group. The network occlusion sensitivity analysis method was employed to assess the importance of each brain region in predicting brain age. Results: As for the prediction model, in the training set, MAE=2.85, r=0.98, R=0.96. In the test set, MAE=4.45, r=0.96, R=0.92. In the local data set of the healthy control group, MAE=3.77, r=0.85, R=0.73. The time of NCT-A in both cirrhosis groups was longer than healthy control group, while the DST scores were lower than healthy control group, and the differences were statistically significant (both P<0.001); the Brain-PAD of healthy control group was (0.8±4.5) years, the Brain-PAD of no-HE group was (6.9±8.1) years, and the HE group was (10.2±7.7) years. The differences between the three groups were statistically significant (P<0.001), and the differences between any two groups were statistically significant (all P<0.05). The importance ratio of visual network in predicting brain age increased in cirrhosis patients, and the HE group was higher than no-HE group. Conclusions: In patients with cirrhosis, the cognitive function is reduced, brain aging is accelerated, and these changes are more obvious in patients with HE. The importance differences of each brain network in predicting brain aging provide a new direction for identifying the specific regions where cirrhosis and HE accelerate brain aging.