  The study explores Artificial Intelligence (AI) powered modeling to predict
the evolution of cancer tumor cells in mice under different forms of treatment.
The AI models are analyzed against varying ambient and systemic parameters,
e.g. drug dosage, volume of the cancer cell mass, and time taken to destroy the
cancer cell mass. The data required for the analysis have been synthetically
extracted from plots available in both published and unpublished literature
(primarily using a Matlab architecture called "Grabit"), that are then
statistically standardized around the same baseline for comparison. Three forms
of treatment are considered - saline (multiple concentrations used), magnetic
nanoparticles (mNPs) and fluorodeoxyglycose iron oxide magnetic nanoparticles
(mNP-FDGs) - analyzed using three Machine Learning (ML) algorithms, Decision
Tree (DT), Random Forest (RF), Multilinear Regression (MLR), and a Deep
Learning (DL) module, the Adaptive Neural Network (ANN). The AI models are
trained on 60-80% data, the rest used for validation. Assessed over all three
forms of treatment, ANN consistently outperforms other predictive models. Our
models predict mNP-FDG as the most potent treatment regime that kills the
cancerous tumor completely in ca 13 days from the start of treatment. The
models can be generalized to other forms of cancer treatment regimens.
