• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• International Journal of Vascular Biomedical Engineering
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• v.2 no.1
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• pp.17-24
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• 2004

Tumor angiogenesis was simulated using a two-dimensional computational model. The equation that governed angiogenesis comprised a tumor angiogenesis factor (TAF) conservation equation in time and space, which was solved numerically using the Galerkin finite element method. The time derivative in the equation was approximated by a forward Euler scheme. A stochastic process model was used to simulate vessel formation and vessel elongation towards a paracrine site, i.e., tumor-secreted basic fibroblast growth factor (bFGF). In this study, we assumed a two-dimensional model that represented a thin (1.0 mm) slice of the tumor. The growth of the tumor over time was modeled according to the dynamic value of bFGF secreted within the tumor. The data used for the model were based on a previously reported model of a brain tumor in which four distinct stages (namely multicellular spherical, first detectable lesion, diagnosis, and death of the virtual patient) were modeled. In our study, computation was not continued beyond the 'diagnosis' time point to avoid the computational complexity of analyzing numerous vascular branches. The numerical solutions revealed that no bFGF remained within the region in which vessels developed, owing to the uptake of bFGF by endothelial cells. Consequently, a sharp, declining gradient of bFGF existed near the surface of the tumor. The vascular architecture developed numerous branches close to the tumor surface (the brush-border effect). Asymmetrical tumor growth was associated with a greater degree of branching at the tumor surface.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.711-712
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• 2002

A numerical study of a hemodynamical model for the tumor angiogenesis is carried out. The tumor angiogenesis process is comprised of a sequence of events; secretion of tumor angiogenesis factor(TAF) from the solid tumor, degradation of the basement membrane of nearby blood vessels, migration and proliferation of the endothelial cells. The model takes into account the effect of TAF concentration and endothelial cell density, and their conservation equations are represented as a set of one-dimensional initial boundary value problems. These equations are discretized by using a finite difference method in which the second order schemes both in time and in space are used. The effects of the parameters contained in the model are Investigated extensively through the numerical simulation of the discretized model. The result for the typical case compares very well with the known result.

• IMMUNE NETWORK
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• v.4 no.1
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• pp.44-52
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• 2004

Background: As a potent antigen presenting cell and a powerful inducer of antigen specific immunity, dendritic cells (DCs) are being considered as a promising anti-tumor therapeutic module. The expected therapeutic effect of DCs in renal cell carcinoma was tested in the mouse model. Established late-stage tumor therapeutic (E-T) and minimal residual disease (MRD) model was considered in the in vivo experiments. Methods: Syngeneic renal cell carcinoma cells (RENCA) were inoculated either subcutaneously (E-T) or intravenously (MRD) into the Balb/c mouse. Tumor cell lysate pulsed-DCs were injected twice in two weeks. Intraperitoneal DC injection was started 3 week (E-T model) or one day (MRD model) after tumor cell inoculation. Two weeks after the final DC injection, the tumor growth and the systemic immunity were observed. Therapeutic DCs were cultured from the bone marrow myeloid lineage cells with GM-CSF and IL-4 for 7 days and pulsed with RENCA cell lysate for 18 hrs. Results: Compared to the saline treated group, tumor growth (E-T model) or formation (MRD model) was suppressed in pulsed-DC treated group. RENCA specific lymphocyte proliferation was observed in the RENCA tumor-bearing mice treated with pulsed-DCs. Primary cytotoxic T cell activity against RENCA cells was increased in pulsed-DC treated group. Conclusion: The data suggest the possible anti-tumor effect of cultured DCs in established or minimal residual disease/metastasis state of renal cell carcinoma. Systemic tumor specific immunity including cytotoxic T cell activity was modulated also in pulsed-DC treated group.

• East Asian mathematical journal
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• v.36 no.1
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• pp.81-90
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• 2020

Many compartment models assume a kinetically homogeneous amount of materials that have well-stirred compartments. However, based on observations from such processes, they have been heuristically fitted by exponential or gamma distributions even though biological media are inhomogeneous in real environments. Fractional differential equations using a specific kernel in Pharmacokinetic/Pharmacodynamic (PKPD) model are recently introduced to account for abnormal drug disposition. We discuss a tumor growth inhibition (TGI) model using fractional-order derivative from it. This represents a tumor growth delay by cytotoxic agents and additionally show variations in the equilibrium points by the change of fractional order. The result indicates that the equilibrium depends on the tumor size as well as a change of the fractional order. We find that the smaller the fractional order, the smaller the equilibrium value. However, a difference of them is the number of concavities and this indicates that TGI over time profile for fitting or prediction should be determined properly either fractional order or tumor sizes according to the number of concavities shown in experimental data.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.143-148
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• 2022

Brain tumor classification is an important process that allows doctors to plan treatment for patients based on the stages of the tumor. To improve classification performance, various CNN-based architectures are used for brain tumor classification. Existing methods for brain tumor segmentation suffer from overfitting and poor efficiency when dealing with large datasets. The enhanced CNN architecture proposed in this study is based on U-Net for brain tumor segmentation, RefineNet for pattern analysis, and SegNet architecture for brain tumor classification. The brain tumor benchmark dataset was used to evaluate the enhanced CNN model's efficiency. Based on the local and context information of the MRI image, the U-Net provides good segmentation. SegNet selects the most important features for classification while also reducing the trainable parameters. In the classification of brain tumors, the enhanced CNN method outperforms the existing methods. The enhanced CNN model has an accuracy of 96.85 percent, while the existing CNN with transfer learning has an accuracy of 94.82 percent.

• IMMUNE NETWORK
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• v.5 no.1
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• pp.36-44
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• 2005

Background: The anti-tumor therapeutic effect of autologous tumor cell lysate pulseddendritic cells (DCs) was studied for non-immunogenic and immune suppressive lung cancer model. To test the possibility as an adjuvant therapy, minimal residual disease model was considered in mouse in vivo experiments. Methods: Syngeneic 3LL lung cancer cells were inoculated intravenously into the C57BL/6 mouse. Autologous tumor cell (3LL) or allogeneic leukemia cell (WEHI-3) lysate pulsed-DCs were injected twice in two weeks. Intraperitoneal DC injection was started one day (MRD model) after tumor cell inoculation. Two weeks after the final DC injection, tumor formation in the lung and the tumor-specific systemic immunity were observed. Tumor-specific lymphocyte proliferation and the IFN-${\gamma}$ secretion were analyzed for the immune monitoring. Therapeutic DCs were cultured from the bone marrow myeloid lineage cells with GM-CSF and IL-4 for 7 days and pulsed with tumor cell lysate for 18 hrs. Results: Compared to the saline treated group, tumor formation was suppressed in 3LL tumor cell lysate pulsed-DC treated group, while 3LL-specific immune stimulation was minimum. WEHI-3-specific immune stimulation occurred in WEHI-3 lysate-pulsed DC treated group, which had no correlation with tumor regression. Conclusion: The data suggest the possible anti-tumor effect of cultured DCs as an adjuvant therapy for minimal residual disease state of lung cancer. The significance of immune modulation in DC therapy including the possible involvement of NK cell as well as antigen-specific cytotoxic T cell activity induction was discussed.

• Biomedical Science Letters
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• v.27 no.4
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• pp.329-333
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• 2021

We previously identified that tumor cells genetically modified with a 4-1BBL co-stimulatory molecule had anticancer effects in a CT26 mouse colorectal tumor model. To identify the distinction between immune cells in a mouse tumor model treated with tumor cells genetically modified with 4-1BBL or β-gal, we examined the immune cells in CT26-WT, CT26-βgal, and CT26-4-1BBL tumor bearing mice 21 days after tumor cell administration. The CD8+ T cells population in mice treated with tumor cells genetically modified with 4-1BBL was significantly increased on day 21 compared to that of tumor cells genetically modified with β-gal in the spleen and tumor tissue. The CD4+ T cell population was not different between the two mice groups. The Foxp3+CD25^high CD4 T cell population decreased on day 21 in tumor tissues, but the decrease was not significant. We also found that CD8 T cells had pivotal roles in inhibiting tumor growth by treating mice with ant-CD4 and CD8 antibodies. These results suggest that tumor cells genetically modified with 4-1BBL could inhibit tumor growth by affecting on CD8 T lymphocytes.

• Genomics & Informatics
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• v.16 no.2
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• pp.30-35
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• 2018

Patient-derived xenograft (PDX) models are useful tools for tumor biology research and testing the efficacy of candidate anticancer drugs targeting the druggable mutations identified in tumor tissue. However, it is still unknown how much of the genetic alterations identified in primary tumors are consistently detected in tumor tissues in the PDX model. In this study, we analyzed the genetic alterations of three primary colorectal cancers (CRCs) and matched xenograft tissues in PDX models using a next-generation sequencing cancer panel. Of the 17 somatic mutations identified from the three CRCs, 14 (82.4%) were consistently identified in both primary and xenograft tumors. The other three mutations identified in the primary tumor were not detected in the xenograft tumor tissue. There was no newly identified mutation in the xenograft tumor tissues. In addition to the somatic mutations, the copy number alteration profiles were also largely consistent between the primary tumor and xenograft tissue. All of these data suggest that the PDX tumor model preserves the majority of the key mutations detected in the primary tumor site. This study provides evidence that the PDX model is useful for testing targeted therapies in the clinical field and research on precision medicine.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1225-1239
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• 2016

We investigate pediatric tumor incidence data collected by the Florida Association for Pediatric Tumor program using various models commonly used in disease mapping analysis. Particularly, we consider Poisson normal models with various conditional autoregressive structure for spatial dependence, a zero-in ated component to capture excess zero counts and a spatio-temporal model to capture spatial and temporal dependence, together. We found that intrinsic conditional autoregressive model provides the smallest Deviance Information Criterion (DIC) among the models when only spatial dependence is considered. On the other hand, adding an autoregressive structure over time decreases DIC over the model without time dependence component. We adopt weighted ranks squared error loss to identify high risk regions which provides similar results with other researchers who have worked on the same data set (e.g. Zhang et al., 2014; Wang and Rodriguez, 2014). Our results, thus, provide additional statistical support on those identied high risk regions discovered by the other researchers.