Defining tumor growth in vestibular schwannomas: a volumetric inter-observer variability study in contrast-enhanced T1-weighted MRI.

PURPOSE: For patients with vestibular schwannomas (VS), a conservative observational approach is increasingly used. Therefore, the need for accurate and reliable volumetric tumor monitoring is important. Currently, a volumetric cutoff of 20% increase in tumor volume is widely used to define tumor growth in VS. The study investigates the tumor volume dependency on the limits of agreement (LoA) for volumetric measurements of VS by means of an inter-observer study. METHODS: This retrospective study included 100 VS patients who underwent contrast-enhanced T1-weighted MRI. Five observers volumetrically annotated the images. Observer agreement and reliability was measured using the LoA, estimated using the limits of agreement with the mean (LOAM) method, and the intraclass correlation coefficient (ICC). RESULTS: The 100 patients had a median average tumor volume of 903 mm3 (IQR: 193-3101). Patients were divided into four volumetric size categories based on tumor volume quartile. The smallest tumor volume quartile showed a LOAM relative to the mean of 26.8% (95% CI: 23.7-33.6), whereas for the largest tumor volume quartile this figure was found to be 7.3% (95% CI: 6.5-9.7) and when excluding peritumoral cysts: 4.8% (95% CI: 4.2-6.2). CONCLUSION: Agreement limits within volumetric annotation of VS are affected by tumor volume, since the LoA improves with increasing tumor volume. As a result, for tumors larger than 200 mm3, growth can reliably be detected at an earlier stage, compared to the currently widely used cutoff of 20%. However, for very small tumors, growth should be assessed with higher agreement limits than previously thought.

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 Gamma Knife center. The data was collected prospectively and included patient- and treatment-related characteristics and MRI scans obtained at day of treatment and at follow-up visits, 6, 12, 24 and 36 months after treatment. The correlations of the patient- and treatment-related characteristics to TTE were investigated using statistical tests. From the treatment scans, we extracted the following MRI image features: first-order statistics, Minkowski functionals (MFs), and three-dimensional gray-level co-occurrence matrices (GLCMs). These features were applied in a machine learning environment for classification of TTE, using support vector machines. RESULTS: In a clinical data set, containing 61 patients presenting obvious non-TTE and 38 patients presenting obvious TTE, we determined that patient- and treatment-related characteristics do not show any correlation to TTE. Furthermore, first-order statistical MRI features and MFs did not significantly show prognostic values using support vector machine classification. However, utilizing a set of 4 GLCM features, we achieved a sensitivity of 0.82 and a specificity of 0.69, showing their prognostic value of TTE. Moreover, these results increased for larger tumor volumes obtaining a sensitivity of 0.77 and a specificity of 0.89 for tumors larger than 6 cm3 . CONCLUSIONS: The results found in this research clearly show that MRI tumor texture provides information that can be employed for predicting TTE. This can form a basis for individual VS treatment selection, further improving overall treatment results. Particularly in patients with large VSs, where the phenomenon of TTE is most relevant and our predictive model performs best, these findings can be implemented in a clinical workflow such that for each patient, the most optimal treatment strategy can be determined.

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.

Influence of pretreatment growth rate on Gamma Knife treatment response for vestibular schwannoma: a volumetric analysis.

OBJECTIVEThe aim of this study was to gain insight into the influence of the pretreatment growth rate on the volumetric tumor response and tumor control rates after Gamma Knife radiosurgery (GKRS) for incidental vestibular schwannoma (VS).METHODSAll patients treated with GKRS at the Gamma Knife Center, ETZ Hospital, who exhibited a confirmed radiological progression of their VS after an initial observation period were included. Pre- and posttreatment MRI scans were volumetrically evaluated, and the volume doubling times (VDTs) prior to treatment were calculated. Posttreatment volumes were used to create an objective mathematical failure definition: 2 consecutive significant increases in tumor volume among 3 consecutive follow-up MRI scans. Spearman correlation, Kaplan-Meier survival analysis, and Cox proportional hazards regression analysis were used to determine the influence of the VDT on the volumetric treatment response.RESULTSThe resulting patient cohort contained 311 patients in whom the VDT was calculated. This cohort had a median follow-up time of 60 months after GKRS. Of these 311 patients, 35 experienced loss of tumor control after GKRS. The pretreatment growth rate and the relative volume changes, calculated at 6 months and 1, 2, and 3 years following treatment, showed no statistically significant correlation. Kaplan-Meier analysis revealed that slow-growing tumors, with a VDT equal to or longer than the median VDT of 15 months, had calculated 5- and 10-year control rates of 97.3% and 86.0%, respectively, whereas fast-growing tumors, with a VDT less than the median growth rate, had control rates of 85.5% and 67.6%, respectively (log-rank, p = 0.001). The influence of the VDT on tumor control was also determined by employing the Cox regression analysis. The resulting model presented a significant (p = 0.045) effect of the VDT on the hazard rates of loss of tumor control.CONCLUSIONSBy employing a unique, large database with long follow-up times, the authors were able to accurately investigate the influence of the pretreatment VS growth rate on the volumetric GKRS treatment response. The authors have found a predictive model that illustrates the negative influence of the pretreatment VS growth rate on the efficacy of radiosurgery treatment. The resulting tumor control rates confirm the high efficacy of GKRS for slow-growing VS. However, fast-growing tumors showed significantly lower control rates. For these cases, different treatment strategies may be considered.