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Lovisa Engberg, Thesis preview

Title: Automated radiation therapy treatment planning by increased accuracy of optimization tools

Tid: Fr 2018-11-16 kl 11.00 - 12.00

Plats: F11

Medverkande: Lovisa Engberg

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Abstract: Every radiation therapy treatment is preceded by a treatment planning phase. In this phase, a treatment plan that specifies exactly how to irradiate the patient is designed by the treatment planner. Since the introduction of intensity-modulated radiation therapy into clinical practice in the 1990's, treatment planning involves, and requires, the use of advanced optimization tools due to the largely increased degrees of freedom in treatment specifications compared to earlier radiation therapy techniques.

The aim of treatment planning is to create a plan that results in the, in some sense, best treatment---a treatment that at the same time reflects the patient-specific clinical goals, achieves the best possible quality, and adheres to other possible preferences of the oncologist or of the clinic. Despite dedicated treatment planning systems available with advanced optimization tools, treatment planning is often referred to as a complicated process involving many iterations with successively adjusted parameters. Over the years, a request has emerged from the clinical and treatment planners' side to make treatment planning less time-consuming and more straightforward, and the methods subsequently developed as a response have come to be referred to as methods for automated treatment planning.

In this thesis, a framework for automated treatment planning is proposed and its potential and flexibility investigated. The focus is placed on increasing the accuracy of the optimization tools, aiming at achieving a less complicated treatment planning process that is driven by intuition rather than, as currently, trial and error. The suggested framework is contrasted to a class of methods dominating in the literature, which applies a more classical view of automation to treatment planning and strives towards reducing any type of human interaction. To increase the accuracy of the optimization tools, the underlying so-called objective functions are reformulated to better correlate with measures of treatment plan quality while possessing mathematical properties favorable for optimization. An important step is to show that the suggested framework not only is theoretically desirable, but also useful in practice. An interior-point method is therefore tailored to the specific structure of the novel optimization formulation, and is applied throughout the thesis, to demonstrate tractability. Numerical studies support the idea of the suggested framework equipping the treatment planner with more accurate and thereby less complicated tools to more straightforwardly handle the intrinsically complex process that constitutes treatment planning.