Brain tumor quantification equation: modeled on complete step response algorithm

In Image Guided neuro-Surgery (IGnS) protocol relating to tumor, the planning stage is the bottleneck where most times are spent reconstructing the slices in order to; quantify the tumor, get the tumor shape and location relative to adjacent cells, and determine best incursion route among others. This time consuming assignment is handled by a surgeon using any of the standardized IGnS software. It has been observed that the approach taken to quantify tumor in those software are simply replicating the surgeons’ experience-based brain tumor quantification technique fashionable in the pre-imaging era. The result is a quantification method that is time consuming, and at bests an approximation. What is presented here is a novel brain tumor quantification method based on step response algorithm utilizing a model which itself was based on step response model resulting in smart and rapid quantification of brain tumor volume

MOGA-Based Multi-drug Optimisation for Cancer Chemotherapy

This paper presents a novel method of multi-drug scheduling using multi-objective genetic algorithm (MOGA) that can find suitable/optimum dosages by trading-off between cell killing and toxic side-effects of chemotherapy treatment. A close-loop control method, namely Integral-Proportional-Derivative (I-PD) is designed to control dosages of drugs to be infused to the patient’s body and MOGA is used to find suitable parameters of the controller. A cell compartments model is developed and used to describe the effects of the drugs on different type of cells, plasma drug concentration and toxic side-effects. Results show that specific drug schedule obtained through the proposed method can reduce the tumour size nearly 100% with relatively lower toxic side-effects

Optimal Multi-Drug Chemotherapy Control Scheme for Cancer Treatment. Design and development of a multi-drug feedback control scheme for optimal chemotherapy treatment for cancer. Evolutionary multi-objective optimisation algorithms were used to achieve the optimal parameters of the controller for effective treatment of cancer with minimum side effects.

Cancer is a generic term for a large group of diseases where cells of the body lose their normal mechanisms for growth so that they grow in an uncontrolled way. One of the most common treatments of cancer is chemotherapy that aims to kill abnormal proliferating cells; however normal cells and other organs of the patients are also adversely affected. In practice, it¿s often difficult to maintain optimum chemotherapy doses that can maximise the abnormal cell killing as well as reducing side effects. The most chemotherapy drugs used in cancer treatment are toxic agents and usually have narrow therapeutic indices, dose levels in which these drugs significantly kill the cancerous cells are close to the levels which sometime cause harmful toxic side effects. To make the chemotherapeutic treatment effective, optimum drug scheduling is required to balance between the beneficial and toxic side effects of the cancer drugs. Conventional clinical methods very often fail to find drug doses that balance between these two due to their inherent conflicting nature. In this investigation, mathematical models for cancer chemotherapy are used to predict the number of tumour cells and control the tumour growth during treatment. A feedback control method is used so as to maintain certain level of drug concentrations at the tumour sites. Multi-objective Genetic Algorithm (MOGA) is then employed to find suitable solutions where drug resistances and drug concentrations are incorporated with cancer cell killing and toxic effects as design objectives. Several constraints and specific goal values were set for different design objectives in the optimisation process and a wide range of acceptable solutions were obtained trading off among different conflicting objectives. Abstract v In order to develop a multi-objective optimal control model, this study used proportional, integral and derivative (PID) and I-PD (modified PID with Integrator used as series) controllers based on Martin¿s growth model for optimum drug concentration to treat cancer. To the best of our knowledge, this is the first PID/I-PD based optimal chemotherapy control model used to investigate the cancer treatment. It has been observed that some solutions can reduce the cancer cells up to nearly 100% with much lower side effects and drug resistance during the whole period of treatment. The proposed strategy has been extended for more drugs and more design constraints and objectives.Libyan Ministry of Higher Educatio

MOGA-Based Multi-drug Optimisation for Cancer Chemotherapy

Abstract This paper presents a novel method of multi-drug scheduling using multi-objective genetic algorithm (MOGA) that can find suitable/optimum dosages by trading-off between cell killing and toxic side-effects of chemotherapy treatment. A close-loop control method, namely Integral-Proportional-Derivative (I-PD) is designed to control dosages of drugs to be infused to the patient's body and MOGA is used to find suitable parameters of the controller. A cell compartments model is developed and used to describe the effects of the drugs on different type of cells, plasma drug concentration and toxic side-effects. Results show that specific drug schedule obtained through the proposed method can reduce the tumour size nearly 100% with relatively lower toxic side-effects