A Modified Genetic Algorithm for the Beam Angle Optimization Problem in Intensity-Modulated Radiotherapy Planning

A self-adaptive case-based reasoning system for dose planning in prostate cancer radiotherapy

PURPOSE

Prostate cancer is the most common cancer in the male population. Radiotherapy is often used in the treatment for prostate cancer. In radiotherapy treatment, the oncologist makes a trade-off between the risk and benefit of the radiation, i.e., the task is to deliver a high dose to the prostate cancer cells and minimize side effects of the treatment. The aim of our research is to develop a software system that will assist the oncologist in planning new treatments.

METHODS

A nonlinear case-based reasoning system is developed to capture the expertise and experience of oncologists in treating previous patients. Importance (weights) of different clinical parameters in the dose planning is determined by the oncologist based on their past experience, and is highly subjective. The weights are usually fixed in the system. In this research, the weights are updated automatically each time after generating a treatment plan for a new patient using a group based simulated annealing approach.

RESULTS

The developed approach is analyzed on the real data set collected from the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. Extensive experiments show that the dose plan suggested by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better.

CONCLUSIONS

The developed case-based reasoning system enables the use of knowledge and experience gained by the oncologist in treating new patients. This system may play a vital role to assist the oncologist in making a better decision in less computational time; it utilizes the success rate of the previously treated patients and it can also be used in teaching and training processes.

An approaching genetic algorithm for automatic beam angle selection in IMRT planning

A method named approaching genetic algorithm (AGA) is introduced to automatically select the beam angles for intensity-modulated radiotherapy (IMRT) planning. In AGA, the best individual of the current population is found at first, and the rest of the normal individuals approach the current best one according to some specially designed rules. In the course of approaching, some better individuals may be obtained. Then, the current best individual is updated to try to approach the real best one. The approaching and updating operations of AGA replace the selection, crossover and mutation operations of the genetic algorithm (GA) completely. Using the specially designed updating strategies, AGA can recover the varieties of the population to a certain extent and retain the powerful ability of evolution, compared to GA. The beam angles are selected using AGA, followed by a beam intensity map optimization using conjugate gradient (CG). A simulated case and a clinical case with nasopharynx cancer are employed to demonstrate the feasibility of AGA. For the case investigated, AGA was feasible for the beam angle optimization (BAO) problem in IMRT planning and converged faster than GA.