Objective To identify the relations of secondary cerebral edema volume with aquaporin 4 (AQP4) and reactive oxygen species (ROS) levels in patients with spontaneous deep supratentorial intracerebral hemorrhage. Methods Forty-seven patients with spontaneous deep supratentorial intracerebral hemorrhage, admitted to our hospital from December 2016 to January 2018, were chosen in our study; on the 1st, 3rd, 14th and 28th d of onset, the hematoma volume and secondary cerebral edema volume were measured by CT scan. Serum AQP4 and ROS levels were measured by ELESA. The relations of perihematomal edema volume with AQP4 and ROS levels in patients with spontaneous deep supratentorial intracerebral hemorrhage were analyzed. Results The hematoma volumes on 14th and 28th d of onset were significantly decreased as compared with those on 1st and 3rd d of onset (P<0.05); the serum AQP4 and ROS levels gradually increased on 1st, 3rd, and 14th d of onset, with significant differences (P<0.05). The cerebral edema volume, and serum AQP4 and ROS levels on 28th d of onset were significantly decreased as compared with those on 3rd and 14th d of onset (P<0.05). Serum AQP4 and ROS levels were positively correlated with cerebral edema volume (r=0.331, P=0.000;r=0.541, P=0.000); serum ROS level was positively correlated with AQP4 level (r=0.298, P=0.000). Conclusion The changes of brain edema volume, and serum AQP4 and ROS levels in patients with spontaneous supratentorial intracerebral hemorrhage are consistent and positively correlated, which suggests that the antioxidant may reduce the AQP4 protein expression, reduce the degree of brain edema, and alleviate the deterioration of patients with spontaneous intracerebral hemorrhage.

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.