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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2505.21094 |
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| _version_ | 1866910970629062656 |
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| author | Chattopadhyay, Amit K Unkundiye, Aimee Pascaline N Pearce, Gillian |
| author_facet | Chattopadhyay, Amit K Unkundiye, Aimee Pascaline N Pearce, Gillian |
| contents | This is a Machine Learning guided study towards zone-specific ray therapy. Combining Machine Learning (Extreme Gradient Boosting) with continuum modeling (exponential and logistic growth), we find that while fluorodeoxyglucose-coated (mNP-FDG) can control cancerous tumor progression within 2 days compared to 18 days by Superparamagnetic Iron Oxide Nanoparticles (SPIONs), for complete termination of the tumor, SPIONS (20 days) are superior compared to mNP-FDG (more than 40 days). We also provide an interactive graphical user interface (GUI) developed with Tkinter/Python that allows users to input relevant data, such as treatment type and time, to receive real-time tumor volume predictions. Our ML-guided prediction indicates joint therapy as the optimum choice, with mNP-FDG ideal for taming the tumor spread, followed by SPIONs for complete eradication, facilitating personalized cancer treatment in clinical practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21094 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG Chattopadhyay, Amit K Unkundiye, Aimee Pascaline N Pearce, Gillian Quantitative Methods Soft Condensed Matter Medical Physics This is a Machine Learning guided study towards zone-specific ray therapy. Combining Machine Learning (Extreme Gradient Boosting) with continuum modeling (exponential and logistic growth), we find that while fluorodeoxyglucose-coated (mNP-FDG) can control cancerous tumor progression within 2 days compared to 18 days by Superparamagnetic Iron Oxide Nanoparticles (SPIONs), for complete termination of the tumor, SPIONS (20 days) are superior compared to mNP-FDG (more than 40 days). We also provide an interactive graphical user interface (GUI) developed with Tkinter/Python that allows users to input relevant data, such as treatment type and time, to receive real-time tumor volume predictions. Our ML-guided prediction indicates joint therapy as the optimum choice, with mNP-FDG ideal for taming the tumor spread, followed by SPIONs for complete eradication, facilitating personalized cancer treatment in clinical practice. |
| title | Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG |
| topic | Quantitative Methods Soft Condensed Matter Medical Physics |
| url | https://arxiv.org/abs/2505.21094 |