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

P. P. J. H. Langenhuizen; H. van Gorp; S. Zinger; H. B. Verheul; S. Leenstra; P. H. N. de With

doi:10.1117/12.2512472

13 March 2019 Dose distribution as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma

P. P. J. H. Langenhuizen, H. van Gorp, S. Zinger, H. B. Verheul, S. Leenstra, P. H. N. de With

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109504C (2019) https://doi.org/10.1117/12.2512472
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

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.

Citation Download Citation

P. P. J. H. Langenhuizen, H. van Gorp, S. Zinger, H. B. Verheul, S. Leenstra, and P. H. N. de With "Dose distribution as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109504C (13 March 2019); https://doi.org/10.1117/12.2512472

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available