Data-Driven Neural Differential Equation Model and Stochastic Dynamics for Glioma Prediction
Low-grade gliomas are infiltrative, incurable primary brain tumors that usually grow slowly and cause death. This study presents a unique low-grade glioma mathematical model and predicts the parameters of the model through real data using deep learning. We combine the advantages of mathematical mode...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11124840/ |
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| author | Mohammed Salman Sanjay Kumar Mohanty |
| author_facet | Mohammed Salman Sanjay Kumar Mohanty |
| author_sort | Mohammed Salman |
| collection | DOAJ |
| description | Low-grade gliomas are infiltrative, incurable primary brain tumors that usually grow slowly and cause death. This study presents a unique low-grade glioma mathematical model and predicts the parameters of the model through real data using deep learning. We combine the advantages of mathematical models with deep learning features to provide results with precise solutions and high-performance prediction. The global stability of treatment success and failure equilibrium is effectively analysed using the Lyapunov method. Next, we estimate the parameters involved in the mathematical model by fitting them into a set of clinical data and employing a neural ordinary differential equations algorithm. Compared to baseline mechanistic tumor models, our approach reduces prediction root mean squared error to 3.37, demonstrating improved data alignment and forecasting accuracy. We also investigate the impact of stochastic perturbation in the model and the effect of time delay parameter on the rate of drug concentration. As an alternative to conducting clinical trials on patients, practitioners can make more informed decisions about patient treatment by studying the numerous models mentioned above that are appropriate for the patient’s condition. |
| format | Article |
| id | doaj-art-d2e2384b502949198f243abc8afe36c2 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d2e2384b502949198f243abc8afe36c22025-08-25T23:12:33ZengIEEEIEEE Access2169-35362025-01-011314521114522210.1109/ACCESS.2025.359893211124840Data-Driven Neural Differential Equation Model and Stochastic Dynamics for Glioma PredictionMohammed Salman0https://orcid.org/0009-0007-6078-7502Sanjay Kumar Mohanty1https://orcid.org/0000-0001-7095-2806Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, IndiaDepartment of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, IndiaLow-grade gliomas are infiltrative, incurable primary brain tumors that usually grow slowly and cause death. This study presents a unique low-grade glioma mathematical model and predicts the parameters of the model through real data using deep learning. We combine the advantages of mathematical models with deep learning features to provide results with precise solutions and high-performance prediction. The global stability of treatment success and failure equilibrium is effectively analysed using the Lyapunov method. Next, we estimate the parameters involved in the mathematical model by fitting them into a set of clinical data and employing a neural ordinary differential equations algorithm. Compared to baseline mechanistic tumor models, our approach reduces prediction root mean squared error to 3.37, demonstrating improved data alignment and forecasting accuracy. We also investigate the impact of stochastic perturbation in the model and the effect of time delay parameter on the rate of drug concentration. As an alternative to conducting clinical trials on patients, practitioners can make more informed decisions about patient treatment by studying the numerous models mentioned above that are appropriate for the patient’s condition.https://ieeexplore.ieee.org/document/11124840/Gliomachemotherapystochastic perturbationneural ordinary differential equationstime delay |
| spellingShingle | Mohammed Salman Sanjay Kumar Mohanty Data-Driven Neural Differential Equation Model and Stochastic Dynamics for Glioma Prediction IEEE Access Glioma chemotherapy stochastic perturbation neural ordinary differential equations time delay |
| title | Data-Driven Neural Differential Equation Model and Stochastic Dynamics for Glioma Prediction |
| title_full | Data-Driven Neural Differential Equation Model and Stochastic Dynamics for Glioma Prediction |
| title_fullStr | Data-Driven Neural Differential Equation Model and Stochastic Dynamics for Glioma Prediction |
| title_full_unstemmed | Data-Driven Neural Differential Equation Model and Stochastic Dynamics for Glioma Prediction |
| title_short | Data-Driven Neural Differential Equation Model and Stochastic Dynamics for Glioma Prediction |
| title_sort | data driven neural differential equation model and stochastic dynamics for glioma prediction |
| topic | Glioma chemotherapy stochastic perturbation neural ordinary differential equations time delay |
| url | https://ieeexplore.ieee.org/document/11124840/ |
| work_keys_str_mv | AT mohammedsalman datadrivenneuraldifferentialequationmodelandstochasticdynamicsforgliomaprediction AT sanjaykumarmohanty datadrivenneuraldifferentialequationmodelandstochasticdynamicsforgliomaprediction |