Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG
Journal:
arXiv
Published Date:
May 27, 2025
Abstract
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.
