Objective: To use a Likert scale method to optimize image quality (IQ) for cone beam CT (CBCT) soft-tissue matching for image-guided radiotherapy of the prostate. Methods: 23 males with local/locally advanced prostate cancer had the CBCT IQ assessed using a 4-point Likert scale (4 5 excellent, no artefacts; 3 5 good, few artefacts; 2 5 poor, just able to match; 1 5 unsatisfactory, not able to match) at three levels of exposure. The lateral separations of the subjects were also measured. The Friedman test and Wilcoxon signed-rank tests were used to determine if the IQ was associated with the exposure level. We used the point-biserial correlation and a x 2 test to investigate the relationship between the separation and IQ.Results: The Friedman test showed that the IQ was related to exposure (p 5 2 3 10 27) and the Wilcoxon signed-rank test demonstrated that the IQ decreased as exposure decreased (all p-values ,0.005). We did not find a correlation between the IQ and the separation (correlation coefficient 0.045), but for separations ,35 cm, it was possible to use the lowest exposure parameters studied. Conclusion:We can reduce exposure factors to 80% of those supplied with the system without hindering the matching process for all patients. For patients with lateral separations ,35 cm, the exposure factors can be reduced further to 64% of the original values. Advances in knowledge: Likert scales are a useful tool for measuring IQ in the optimization of CBCT IQ for soft-tissue matching in radiotherapy image guidance applications.

This paper presents a decision support system for radiotherapy treatment planning for head, neck and brain cancer. The aim of a treatment plan is to apply radiation to kill tumor cells, while minimizing the damage to healthy tissue and critical organs. Since treatment planning is a complex decision making process that relies heavily on the subjective experience of clinicians, we propose the use of case-based reasoning (CBR), in which problems are solved based on the solutions of similar past problems. This paper focuses on the case retrieval process of a CBR system. The attributes, which describe the cases, are selected by assessing their effect on the performance of the CBR system. We have developed a context sensitive local weighting scheme that assigns weights to attributes based on their value and the values of other attributes in the target case. A novel two phase retrieval mechanism is developed, in which each phase is optimized to retrieve a particular part of the solution. We also present an original use of fuzzy logic in order to represent nonlinearity in the similarity measure. Experiments, which evaluate the similarity measure using real brain cancer patient cases, show promising results.

Abstract. This paper presents a decision support system for treatment planning in brain cancer radiotherapy. The aim of a radiotherapy treatment plan is to apply radiation in a way that destroys tumour cells but minimizes the damage to healthy tissue and organs at risk. Treatment planning for brain cancer patients is a complex decision-making process that relies heavily on the subjective experience and expert domain knowledge of clinicians. We propose to capture this experience by using case-based reasoning. Central to the working of our case-based reasoning system is a novel similarity measure that takes into account the non-linear effect of the individual case attributes on the similarity measure. The similarity measure employs fuzzy sets. Experiments, which were carried out to evaluate the similarity measure using real brain cancer patient cases show promising results.