Simulating glioblastoma growth consisting both visible and invisible parts of the tumor using a diffusion-reaction model followed by resection and radiotherapy

Heterogeneous biomechanical/mathematical modeling of spatial prediction of glioblastoma progression using magnetic resonance imaging-based finite element method

BACKGROUND AND OBJECTIVE

Brain tumors are one of the most common diseases and causes of death in humans. Since the growth of brain tumors has irreparable risks for the patient, predicting the growth of the tumor and knowing its effect on the brain tissue will increase the efficiency of treatment strategies.

METHODS

This study examines brain tumor growth using mathematical modeling based on the Reaction-Diffusion equation and the biomechanical model based on continuum mechanics principles. With the help of the image threshold technique of magnetic resonance images, a heterogeneous and close-to-reality environment of the brain has been modeled and experimental data validated the results to achieve maximum accuracy in predicting growth.

RESULTS

The obtained results have been compared with the reported conventional models to evaluate the presented model. In addition to incorporating the chemotherapy effects in governing equations, the real-time finite element analysis of the stress tensors of the surrounding tissue of tumor cells and considering its role in changing the shape and growth of the tumor has added to the importance and accuracy of the current model.

CONCLUSIONS

The comparison of the obtained results with conventional models shows that the heterogeneous model has higher reliability due to the consideration of the appropriate properties for the different regions of the brain. The presented model can contribute to personalized medicine, aid in understanding the dynamics of tumor growth, optimize treatment regimens, and develop adaptive therapy strategies.

Exploration of CT Images Based on the BN-U-net-W Network Segmentation Algorithm in Glioma Surgery

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.

Spatio-temporal investigation of doxorubicin in a 3D heterogeneous tumor microenvironment

Doxorubicin (Adriamycin) is a type of chemotherapy drugs using to treat diseases such as breast cancer, bladder cancer, Kaposi's sarcoma, and lymphoma. Additionally, it can be first prescribed to reduce tumor size. The ratio of killed cells is varied depending on the clinical dosage regimen. Hence, the exact dosage of the drug must be administered to prevent the toxicity that could impair the immune system or leading to heart failure. In the present study, a 3D heterogeneous geometry with a solid tumor and healthy tissue is modeled for the drug delivery investigation. At the first stage, the physical properties of tumor microenvironment are obtained. Then, a five-compartmental model is used to evaluate the free, bound and internalized drug via the convection-diffusion-reaction (CDR) equation. Results are shown that a percent increase of 37.5% and 47.1% for the 75 mg m^-2 to 50 mg m^-2 in the AUC of bound drug and free drug concentration, respectively. The free and bound drugs have the same trend in time showing an apex at the earliest time of injection and then drops to the lowest amount about 9 hours after treatment. Moreover, the internalized drug has a different trend in time. It increases and reaches a constant amount of drug concentration in the cells. Besides, the fraction of surviving cells is also evaluated for both tumor and healthy tissues showing a 88.62% and 97.76% of surviving cells with 50 mg m^-2 of doxorubicin after the treatment, respectively. This model is developed to predict the heterogenous distribution of doxorubicin in three different drug concentrations for patient-specific drug treatment. This model could be used for different drugs to show the rate of perfusion and the ability to kill cancerous cells regarding their different doses and toxicity effects.