Decision Support System for Stereotactic Radiosurgery

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The Gamma Knife (GK) system uses 192 small radiation beams that converge to deliver a high radiation dose within a brain tumour. This technique, called "stereotactic radiosurgery" (SRS) is an effective way of treating brain cancer. The point at which the beams intersect is called an "isocentre" and the treatment involves multiple isocentres to ensure the entire tumour receives a curative dose. Determination of the best isocentre positions and corresponding dwell times is a complex task, since there is a direct conflict between maximizing tumour dose and minimizing dose in surrounding healthy brain. Furthermore, as the number of patients requiring treatment is rapidly increasing due to an aging population and improved drug therapies, we need efficient computerized methods for automating this process. The present research program involves developing models of automation in order to obtain a deeper understanding of the complex interplay between the various objectives. The present program involves two main themes of research as follows. In the first theme, we will explore novel "inverse" automation models. In inverse methods we define the desired output and the computer infers the input. In SRS the desired output is a high radiation dose to the tumour and low dose to normal tissue and the input is the radiation dwell time at each isocentre. The mathematical formulation involves minimizing the numerical difference between the ideal and model-predicted radiation dose. Since the goals conflict (high tumour dose vs. low normal brain dose) and computers do not know what tradeoffs are acceptable, the user sets priorities in the algorithm via weighting factors. However since the relationship of such weight parameters to the solution is not known in advance, one needs to repeat the computation with different weight factors, and by doing so we generate a reasonable model of the problem. The mathematical models will ultimately feed back to end users to decide how weighting factors should be allocated, which will lead to completely automated planning in the future. In the second theme, our group will explore novel artificial intelligence (AI) based automation models. In AI, thousands of cases are used to define features such as tumour volume, and tumour histology. Novel mathematical approaches are then used to establish linkages between all of the features and the outcome, which can be a treatment plan quality metric. The result is a model that we can use to better understand the complex interplay between all of the features and outcome. It is possible to validate a given model by testing its predictive power for an unknown case, not included in the modeling itself. However the emphasis of the current program is in the engineering of the models to understand the problem. The successful completion of this program will lead to future clinically-oriented studies that can use such prediction models for informing plan quality outcome.