No abstract

This study aimed to explore the application value of computed tomography (CT) imaging features based on the deep learning batch normalization (batch normalization, BN) U-net-W network image segmentation algorithm in evaluating and diagnosing glioma surgery. 72 patients with glioma who were admitted to hospital were selected as the research subjects. They were divided into a low-grade group (grades I-II, N = 27 cases) and high-grade group (grades III-IV, N = 45 cases) according to postoperative pathological examination results. The CT perfusion imaging (CTPI) images of patients were processed by using the deep learning-based BN-U-net-W network image segmentation algorithm. The application value of the algorithm was comprehensively evaluated by comparing the average Dice coefficient, average recall rate, and average precision of the BN-U-net-W network image segmentation algorithm with the U-net and BN-U-net network algorithms. The results showed that the Dice coefficient, recall, and precision of the BN-U-net-W network were 86.31%, 88.43%, and 87.63% respectively, which were higher than those of the U-net and BN-U-net networks, and the differences were statistically significant ( P < 0.05 ). Cerebral blood flow (CBF), cerebral blood volume (CBV), and capillary permeability (PMB) in the glioma area were 56.85 mL/(min·100 g), 18.03 mL/(min·100 g), and 8.57 mL/100 g, respectively, which were significantly higher than those of normal brain tissue, showing statistically significant differences ( P < 0.05 ). The mean transit time (MTT) difference between the two was not statistically significant ( P > 0.05 ). The receiver operating characteristic (ROC) curves of CBF, CBV, and PMB in CTPI parameters of glioma had area under the curve (AUC) of 0.685, 0.724, and 0.921, respectively. PMB parameters were significantly higher than those of CBF and CVB, and the differences were statistically obvious ( P < 0.05 ). It showed that the BN-U-net-W network model had a better image segmentation effect, and CBF, CBV, and PMB showed better sensitivity in diagnosing glioma tissue and normal brain tissue and high-grade and low-grade gliomas, among which PBM showed the highest predictability.

BACKGROUND Background: Glioma are tumors derived from neuroectodermal stromal cells and one of the most common malignant tumors of the brain. OBJECTIVE Objective：The objective is to understand the application of CT angiography in the proliferation of glioma cells more clearly and get more effective diagnosis and treatment of glioma. METHODS Methods： The adult male healthy Sprague Dawley (SD) rats were selected as the research objects to construct the animal models. In addition, respective on the 7th day, 14th day, and the 21st day, the rats were submitted to CT scans. According to parameters and indicators such as cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and time to peak (TTP), the stained cerebral tissues of rats at different time nodes were further analyzed. RESULTS Results: The results of rat CT manifestation analysis and clinical analysis showed that with the time increased, the volume of the tumors in rats increased apparently, with symptoms such as listlessness appeared at the initial stage and hemiplegia appeared at the later stage. The comparative analysis results of CT parameters at different times nodes showed that in the same tumor area, before the 14th day, CBF, CBV, and TTP showed the increasing trends, which gradually decreased after the 14th day, with significant statistical difference (P<0.01). However, MTT showed a decreasing trend in general, which is not apparent. The results of Vascular endothelial growth factor (VEGF) staining of the tumor area, the peritumoral area, and the contralateral mirror area at different time nodes showed that the vascular endothelial cells, the smooth muscle cells of vessel walls, and the tumor cells were strongly positive. In addition, with the time increased, in the tumor area, the peripheral area, and the contralateral mirror area, the cells increased in both volume and quantity, showing significant differences. CONCLUSIONS Conclusion: Therefore, through the research in this study, it is found that the computed tomography angiography data monitoring can reflect the angiogenesis of rat cerebral glioma. CLINICALTRIAL