Prognosis of Cancer Progression — Mathematical Model for Prediction and Potential Therapeutic Evaluation

Abstract

The onset of cancerous growth is one of the intriguing aspects of medical sciences with the concomitant risks of delayed diagnosis aggravating the prognosis significantly and eroding the probability of regression of the disease.

Mathematical modeling of vital parameters influencing the pathological states, the changes in the CNS – central nervous system and the triggers in the changes metabolism of the blood, muscle, and glucose that have the potential to adversely impact the end organs is the critical focus of the paper. Conventional approaches are founded on differential diagnostic pathways whilst the mathematical modeling is designed to home in on a higher level of clinical precision through deep learning on the lead indicators of multiple domains of interests than are normally possible on two-dimensional matrices.

The fundamental approaches hinge on defining data entropy for clusters of influences of metabolism, CNS triggers of probable depletion in the strength of electro-signal conductivity through the nerve endings, electroencephalograph changes in the wave dynamics, the perfusion of the blood in the CNS, the relative onset of the rudiments of the dielectric field in the cerebral, hypothalamus or mitochondrial zones and finally the functions of end organs in the cavity.

Changes in data entropy are normalized and compared in real-time to evaluate the onset of cellular changes in the cytoplasm and potential onset of a mutating environment. The measurements of entropy are strongly correlated to the milieu that promotes mutation and consequently the prognostic progression of cancerous growth. Similarly, a reduction in entropy in response to therapeutic interventions during the prognosis can be well correlated.

About the Presenter

Dr. Debasish Banerjee, Ph.D. CEO, Blackstone Synergy Consulting Group Limited, Nairobi, Kenya