Self-adaptive Case Based Reasoning for Dose Planning in Radiotherapy

Prostate cancer is the most common cancer in the male population. Radiotherapy is often used to control the abnormal growth of cancer cells. In radiotherapy treatment the oncologist makes a trade-off between the risk and benefit of the radiation i.e. the task is to deliver the high dose to the cancer cells and minimize the side effects of the treatment. A case based reasoning is developed to capture the expertise and experience of oncologists in treating previous patients. The proposed retrieval process makes a trade-off between the benefit and risk of the radiation. Importance (weights) of different clinical parameters in the dose planning is highly subjective and is generally fixed in the system with the input from the oncologist based on their past experience. In this research, the weights corresponding to each feature used in the retrieval process are updated automatically each time after generating a treatment plan for a new patient using a group based Simulated Annealing approach. The developed approach is analyzed on the real data sets collected from the Nottingham University Hospitals NHS, City Hospital Campus, UK. Extensive experiments show that in most of the cases, the dose plan suggested by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better.