Dose-Volume Histogram Prediction using KernelDensity Estimation

Sammanfattning: Dose plans developed for stereotactic radiosurgery are assessed by studying so called Dose-Volume Histograms. Since it is hard to compare an individual dose plan with doseplans created for other patients, much experience and knowledge is lost. This thesis therefore investigates a machine learning approach to predicting such Dose-Volume Histograms for a new patient, by learning from previous dose plans.The training set is chosen based on similarity in terms of tumour size. The signed distances between voxels in the considered volume and the tumour boundary decide the probability of receiving a certain dose in the volume. By using a method based on Kernel Density Estimation, the intrinsic probabilistic properties of a Dose-Volume Histogramare exploited.Dose-Volume Histograms for the brainstem of 22 Acoustic Schwannoma patients, treated with the Gamma Knife,have been predicted, solely based on each patient’s individual anatomical disposition. The method has proved higher prediction accuracy than a “quick-and-dirty” approach implemented for comparison. Analysis of the bias and variance of the method also indicate that it captures the main underlying factors behind individual variations. However,the degree of variability in dose planning results for the Gamma Knife has turned out to be very limited. Therefore, the usefulness of a data driven dose planning tool for the Gamma Knife has to be further investigated.