A multi-objective multi-drug model for cancer chemotherapy treatment planning: A cost-effective approach to designing clinical trials

The MOEO algorithm for multi-objective optimization of the cancer immuno-chemotherapy

In cancer treatment, chemotherapy has the disadvantage of killing both healthy and cancerous cells. Hence, the mixed-treatment of cancer such as chemo-immunotherapy is recommended. However, deriving the optimal dosage of each drug is a challenging issue. Although metaheuristic algorithms have received more attention in solving engineering problems due to their simplicity and flexibility, they have not consistently produced the best results for every problem. Thus, the need to introduce novel algorithms or extend the previous ones is felt for important optimization problems. Hence, in this paper, the multi-objective Equilibrium Optimizer algorithm, as an extension of the single-objective Equilibrium Optimizer algorithm, is recommended for cancer treatment problems. Then, the performance of the proposed algorithm is considered in both chemotherapy and mixed chemo-immunotherapy of cancer, considering the constraints of the tumor-immune dynamic system and the health level of the patients. For this purpose, two different patients with real clinical data are considered. The Pareto front obtained from the multi-objective optimization algorithm shows the points that can be selected for treatment under different criteria. Using the proposed multi-objective algorithm has reduced the total chemo-drug dose to 138.92 and 5.84 in the first patient, and 16.9 and 0.4384 in the second patient, for chemotherapy and chemo-immunotherapy, respectively. Comparing the results with previous studies demonstrates MOEO's superior performance in both chemotherapy and chemo-immunotherapy. However, it is shown that the proposed algorithm is more suitable for mixed-treatment approaches.

Tackling the non-communicable disease epidemic: a framework for policy action in low- and middle-income countries

Health policy frameworks for the prevention and control of non-communicable diseases have largely been developed for application in high-income countries. Limited attention has been given to the policy exigencies in lower- and middle-income countries where the impacts of these conditions have been most severe, and further clarification of the policy requirements for effective prevention is needed. This paper presents a policy approach to prevention that, although relevant to high-income countries, recognizes the peculiar situation of low-and middle-income countries. Rather than a narrow emphasis on the implementation of piecemeal interventions, this paper encourages policymakers to utilize a framework of four embedded policy levels, namely health services, risk factors, environmental, and global policies. For a better understanding of the non-communicable disease challenge from a policy standpoint, it is proposed that a policy framework that recognizes responsible health services, addresses key risk factors, tackles underlying health determinants, and implements global non-communicable disease conventions, offers the best leverage for prevention.

Robust controller for cancer chemotherapy dosage using nonlinear kernel-based error function

It is well-known that chemotherapy is the most significant method on curing the most death-causing disease like cancer. These days, the use of controller-based approach for finding the optimal rate of drug injection throughout the treatment has increased a lot. Under these circumstances, this paper establishes a novel robust controller that influences the drug dosage along with parameter estimation. A new nonlinear error function-based extended Kalman filter (EKF) with improved scaling factor (NEF-EKF-ISF) is introduced in this research work. In fact, in the traditional schemes, the error is computed using the conventional difference function and it is deployed for the updating process of EKF. In our previous work, it has been converted to the nonlinear error function. Here, the updating process is based on the prior error function, though scaled to a nonlinear environment. In addition, a scaling factor is introduced here, which considers the historical error improvement, for the updating process. Finally, the performance of the proposed controller is evaluated over other traditional approaches, which implies the appropriate impact of drug dosage injection on normal, immune and tumor cells. Moreover, it is observed that the proposed NEF-EKF-ISF has the ability to evaluate the tumor cells with a better accuracy rate.

Simulation analysis for tumor radiotherapy based on three-component mathematical models

OBJECTIVE

To setup a three-component tumor growth mathematical model and discuss its basic application in tumor fractional radiotherapy with computer simulation.

METHOD

First, our three-component tumor growth model extended from the classical Gompertz tumor model was formulated and applied to a fractional radiotherapy with a series of proper parameters. With the computer simulation of our model, the impact of some parameters such as fractional dose, amount of quiescent tumor cells, and α/β value to the effect of radiotherapy was also analyzed, respectively.

RESULTS

With several optimal technologies, the model could run stably and output a series of convergent results. The simulation results showed that the fractional radiotherapy dose could impact the effect of radiotherapy significantly, while the amount of quiescent tumor cells and α/β value did that to a certain extent.

CONCLUSIONS

Supported with some proper parameters, our model can simulate and analyze the tumor radiotherapy program as well as give some theoretical instruction to radiotherapy personalized optimization.