Dose distribution as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma

TitleDose distribution as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma
Publication TypeConference Paper
Year of Publication2019
AuthorsLangenhuizen, PPJH, van Gorp, H, Zinger, S, Verheul, J, Leenstra, S, de With, PHN
Conference NameSPIE Medical Imaging 2019: Computer-Aided Diagnosis
Date Published03/2019
Conference LocationSan Diego, USA

Vestibular schwannomas are benign brain tumors that can be treated radiosurgically with the Gamma Knife in order to stop tumor progression. However, in some cases tumor progression is not stopped and treatment is deemed a failure. At present, the reason for these failed treatments is unknown. Clinical factors and MRI characteristics have been considered as prognostic factors. Another confounder in the success of treatment is the treatment planning itself. It is thought to be very uniformly planned, even though dose distributions among treatment plans are highly inhomogeneous. This paper explores the predictive value of these dose distributions for the treatment outcome. We compute homogeneity indices (HI) and three-dimensional histogram-of-oriented gradients (3D-HOG) and employ support vector machine (SVM) paired with principal component analysis (PCA) for classification. In a clinical dataset, consisting of 20 tumors that showed treatment failure and 20 tumors showing treatment success, we discover that the correlation of the HI values with the treatment outcome presents no statistical evidence of an association (52:5% accuracy employing linear SVM and no statistical significant difference with t-tests), whereas the 3D-HOG features concerning the dose distribution do present correlations to the treatment outcome, suggesting the influence of the treatment on the outcome itself (77:5% accuracy employing linear SVM and PCA). These findings can provide a basis for refining towards personalized treatments and prediction of treatment efficiency. However, larger datasets are needed for more extensive analysis.

Refereed DesignationDoes Not Apply