An intelligent decision support system for personalized cancer treatment

Cancer is one of the biggest killers in the western world; every two minutes someone is diagnosed with cancer in the UK. Personalized treatment of cancer, which simply means selecting a treatment best suited to an individual involving the integration and translation of several new technologies in clinical care of patients. Conventional cancer treatments include surgery, radiotherapy and chemotherapy. Among these, therapeutically treatment requires optimal control of radiation/drug to minimize toxic effect and in turn to minimize side effect. We propose a hybrid prediction model consist of avascular tumour growth model from a tumour image and intelligent drug scheduling schema for drug penetration. Our main aim is to develop an intelligent decision support system which helps to analyze the tumour microenvironment constraints like cell-cell adhesion, cell movement, extra-cellular matrix (ECM) and optimal solutions of drug scheduling problem. Hypoxia and drug resistance are also incorporated in the model to achieve the predictive results for every patient as both of them considered as the main reason for chemotherapy and radiotherapy treatment failure. Finally, our goal is to provide a dynamic and effective personalized cancer treatment model to support the oncologist for making right decisions to the right patient at the right time.