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.
Vestibular schwannomas (VS) are benign brain tumors that can be treated with high-precision focused radiation with the Gamma Knife in order to stop tumor growth. Outcome prediction of Gamma Knife radiosurgery (GKRS) treatment can help in determining whether GKRS will be effective on an individual patient basis. However, at present, prognostic factors of tumor control after GKRS for VS are largely unknown, and only clinical factors, such as size of the tumor at treatment and pre-treatment growth rate of the tumor, have been considered thus far. This research aims at outcome prediction of GKRS by means of quantitative texture feature analysis on conventional MRI scans. We compute first-order statistics and features based on gray-level co- occurrence (GLCM) and run-length matrices (RLM), and employ support vector machines and decision trees for classification. In a clinical dataset, consisting of 20 tumors showing treatment failure and 20 tumors exhibiting treatment success, we have discovered that the second-order statistical metrics distilled from GLCM and RLM are suitable for describing texture, but are slightly outperformed by simple first-order statistics, like mean, standard deviation and median. The obtained prediction accuracy is about 85%, but a final choice of the best feature can only be made after performing more extensive analyses on larger datasets. In any case, this work provides suitable texture measures for successful prediction of GKRS treatment outcome for VS.