In this paper we propose a classification scheme to isolate truly benign tumors from those
that initially start off as benign but subsequently show metastases. A non-parametric
artificial neural network methodology has been chosen because of the analytical
difficulties associated with extraction of closed-form stochastic-likelihood parameters
given the extremely complicated and possibly non-linear behavior of the state variables.
This is intended as the first of a three-part research output. In this paper, we have
proposed and justified the computational schema. In the second part we shall set up a
working model of our schema and pilot-test it with clinical data while in the concluding
part we shall give an in-depth analysis of the numerical output and model findings and
compare it to existing methods of tumor growth modeling and malignancy prediction.
Category: Quantitative Biology