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.
|Title of host publication||Medical Imaging 2019|
|Subtitle of host publication||Computer-Aided Diagnosis|
|Editors||Kensaku Mori, Horst K. Hahn|
|Publication status||Published - 13 Mar 2019|
|Event||Medical Imaging 2019: Computer-Aided Diagnosis - San Diego, United States|
Duration: 17 Feb 2019 → 20 Feb 2019
|Series||Progress in Biomedical Optics and Imaging - Proceedings of SPIE|
|Conference||Medical Imaging 2019: Computer-Aided Diagnosis|
|Period||17/02/19 → 20/02/19|
Bibliographical notePublisher Copyright:
© 2019 SPIE.