Prediction of transient tumor enlargement using MRI tumor texture after radiosurgery on vestibular schwannoma

Purpose: Vestibular schwannomas (VSs) are uncommon benign brain tumors, generally treated using Gamma Knife radiosurgery (GKRS). However, due to the possible adverse effect of transient tumor enlargement (TTE), large VS tumors are often surgically removed instead of treated radiosurgically. Since microsurgery is highly invasive and results in a significant increased risk of complications, GKRS is generally preferred. Therefore, prediction of TTE for large VS tumors can improve overall VS treatment and enable physicians to select the most optimal treatment strategy on an individual basis. Currently, there are no clinical factors known to be predictive for TTE. In this research, we aim at predicting TTE following GKRS using texture features extracted from MRI scans. Methods: We analyzed clinical data of patients with VSs treated at our

Radiomics-Based Prediction of Long-Term Treatment Response of Vestibular Schwannomas Following Stereotactic Radiosurgery

OBJECTIVE: Stereotactic radiosurgery (SRS) is one of the treatment modalities for vestibular schwannomas (VSs). However, tumor progression can still occur after treatment. Currently, it remains unknown how to predict long-term SRS treatment outcome. This study investigates possible magnetic resonance imaging (MRI)-based predictors of long-term tumor control following SRS. STUDY DESIGN: Retrospective cohort study. SETTING: Tertiary referral center. PATIENTS: Analysis was performed on a database containing 735 patients with unilateral VS, treated with SRS between June 2002 and December 2014. Using strict volumetric criteria for long-term tumor control and tumor progression, a total of 85 patients were included for tumor texture analysis. INTERVENTION(S): All patients underwent SRS and had at least 2 years of follow-up. MAIN OUTCOME MEASURE(S): Quantitative tumor texture features were extracted from conventional MRI scans. These features were supplied to a machine learning stage to train prediction models. Prediction accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC) are evaluated. RESULTS: Gray-level co-occurrence matrices, which capture statistics from specific MRI tumor texture features, obtained the best prediction scores: 0.77 accuracy, 0.71 sensitivity, 0.83 specificity, and 0.93 AUC. These prediction scores further improved to 0.83, 0.83, 0.82, and 0.99, respectively, for tumors larger than 5 cm. CONCLUSIONS: Results of this study show the feasibility of predicting the long-term SRS treatment response of VS tumors on an individual basis, using MRI-based tumor texture features. These results can be exploited for further research into creating a clinical decision support system, facilitating physicians, and patients to select a personalized optimal treatment strategy

Invloed van groeisnelheid op de effectiviteit van Gamma Knife-behandeling van vestibulair schwannomen

Deze studie is gericht op het bepalen van de invloed van de groeisnelheid van vestibulair schwannomen (VS) op de effectiviteit van een Gamma Knife (GK)-behandeling

Influence of growth rate on effectiveness of Gamma Knife treatment on vestibular schwannoma

Deze studie is gericht op het bepalen van de invloed van de groeisnelheid van vestibulair schwannomen (VS) op de effectiviteit van een Gamma Knife (GK)-behandeling.Goal: This study focused on determining the influence of the growth rate of vestibular schwannoma (VS) on the effectiveness of a Gamma Knife (GK) treatment.Methods: All patients treated with GK after a wait-and-scan policy were included. MRI scans before and after treatment were volumetrically assessed and the pre-treatment volume doubling time (VDT) was calculated. Statistical analyses were applied to analyse the influence.Results: The resulting patient cohort contained 311 patients with a median follow-up of 60 months, including 35 patients for which the treatment was not effective. Kaplan-Meier analyses resulted in significant differences between slow- and fast-growing VS in the 5- and 10-year success percentages (log-rank, p=0.001): 97.3% and 86.0% for the slow-growing tumors and 85.5% and 67.6% for the fast-growing tumors, respectively. Influence of the VDT on the treatment result was also determined in a Cox regression. The resulting model showed a significant (p=0.045), but small effect of the VDT on the hazard ratios. Conclusions: By employing our unique, large database with long follow-up times, we were able to show the influence of the growth rate of VS on the GK treatment results.<br/

Invloed van groeisnelheid op de effectiviteit van Gamma Knife-behandeling van vestibulair schwannomen

Deze studie is gericht op het bepalen van de invloed van de groeisnelheid van vestibulair schwannomen (VS) op de effectiviteit van een Gamma Knife (GK)-behandeling. Goal: This study focused on determining the influence of the growth rate of vestibular schwannoma (VS) on the effectiveness of a Gamma Knife (GK) treatment.\u3cbr/\u3eMethods: All patients treated with GK after a wait-and-scan policy were included. MRI scans before and after treatment were volumetrically assessed and the pre-treatment volume doubling time (VDT) was calculated. Statistical analyses were applied to analyse the influence.\u3cbr/\u3eResults: The resulting patient cohort contained 311 patients with a median follow-up of 60 months, including 35 patients for which the treatment was not effective. Kaplan-Meier analyses resulted in significant differences between slow- and fast-growing VS in the 5- and 10-year success percentages (log-rank, p=0.001): 97.3% and 86.0% for the slow-growing tumors and 85.5% and 67.6% for the fast-growing tumors, respectively. Influence of the VDT on the treatment result was also determined in a Cox regression. The resulting model showed a significant (p=0.045), but small effect of the VDT on the hazard ratios. \u3cbr/\u3eConclusions: By employing our unique, large database with long follow-up times, we were able to show the influence of the growth rate of VS on the GK treatment results.\u3cbr/\u3

Outcome prediction of Gamma Knife radiosurgery on vestibular schwannoma using contour-based shape descriptors

Improving care and the transition to personalized medicine is a subject of active research. For many diseases, such as vestibular schwannomas (VS), different treatment strategies are available, each having their own benefits and drawbacks. From a personalized medicine point of view, the ability to predict results for these different treatment strategies would be very useful. In this paper we concentrate on the prediction of the outcome of one of these treatment strategies: Gamma Knife Stereotactic Radiosurgery (GKRS). In some cases, treatment is unsuccessful and the tumor continues to grow. It remains unclear as to why this happens. Some reported influencing factors for a successful GKRS outcome are size and pretreatment growth rate. However, there is conflicting evidence with regard to the predictive value of these factors. Therefore, we have investigated to what extend shape-based binary prediction of GKRS on VS is possible. Twenty-five shape descriptors were computed for training Support Vector Machine (SVM) classifiers and Decision Tree (DT) classifiers. Using these classifiers, 40 tumors of which the treatment outcome is known, were classified. Feature vectors were constructed with 18 descriptors and used for training the classifiers. The best SVM showed a sensitivity, specificity and AUC of 55%, 70% and 0.67, respectively. The best DT classifier resulted in a sensitivity, specificity and AUC of 70%, 60% and 0.71, respectively. These results show that, when using shape-based descriptors, the shape of VS is a weak predictor for GKRS to result into a successful treatment outcome

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

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