Cancer Studies

This book’s aim is to study the mathematical and computational models to analyze the progress, prognosis, prevention, and panacea of breast cancer. The book discusses application of Markov chains and transient mappings, Charlie–Simpson numerical algorithm, models represented by nonlinear reaction–diffusion-type partial differential equations, and related techniques. The book also attempts to design mathematical model of targeted strategic treatments by using Skilled Killer Drugs (SKD1 and SKD2) to suggest the improvisation of future cancer treatments. Both graduate students and researchers of computational biology and oncologists will benefit by studying this book. Researchers of cancer studies and biological sciences will also find this work helpful.

Despite improvements in strategies for early detection and cancer treatment, racial and ethnic groups and individuals from other medically underserved populations continue to experience disparities in cancer morbidity and mortality. Research in cancer health disparities has evolved from first generation studies that described racial differences in morbidity and mortality to research that examines the efforts of interventions that focus on increasing access to early detection and treatment. As a result of these efforts, racial background, socioeconomic characteristics, access to high quality cancer care, and psychological and social factors have been documented as important determinants of cancer health disparities; these factors provide the context within which cancer is detected, treated, and prevented.