The primary aim of Gamma Knife (GK) radiosurgery is to deliver high-dose radiation precisely to a target while conforming to the target shape. In this study, the effects of tumor shape irregularity (TSI) on GK dose-plan quality and treatment outcomes were analyzed in 234 vestibular schwannomas. TSI was quantified using seven different metrics including volumetric index of sphericity (VioS). GK treatment plans were created on a single GK-Perfexion/ICON platform. The plan quality was measured using selectivity index (SI), gradient index (GI), Paddick’s conformity index (PCI), and efficiency index (EI). Correlation and linear regression analyses were conducted between shape irregularity features and dose plan indices. Machine learning was employed to identify the shape feature that predicted dose plan quality most effectively. The treatment outcome analysis including tumor growth control and serviceable hearing preservation at 2 years, were conducted using Cox regression analyses. All TSI features correlated significantly with the dose plan indices (P < 0.0012). With increasing tumor volume, vestibular schwannomas became more spherical (P < 0.05) and the dose plan indices varied significantly between tumor volume subgroups (P < 0.001 and P < 0.01). VioS was the most effective predictor of GK indices (P < 0.001) and we obtained 89.36% accuracy (79.17% sensitivity and 100% specificity) for predicting PCI. Our results indicated that TSI had significant effects on the plan quality however did not adversely affect treatment outcomes.

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Gamma knife radiosurgery (GKRS) delivers an unevenly distributed radiation dose to a tumor, with a sharp falloff outside the target. Although the dose inhomogeneity within a tumor is strongly influenced by its shape, routine GKRS dose planning does not account for it. We hypothesized that shape irregularity measures were correlated with treatment planning indices, and might provide insight during treatment planning. The aims of this study were to quantify the shape irregularity measures in vestibular schwannomas, estimate their correlations with core radiosurgical planning measures, and define the most predictive shape feature for dose effectiveness. METHODS: Four dose plan indices, which were the selectivity index (SI), gradient index (GI), efficiency index (EI), and Paddick’s conformity index (PCI) were estimated from the GKRS plans of 234 vestibular schwannomas. All dose plans were prepared using Gamma Plan 10.0 and above and all treatments were delivered using a perfexion/ICON platform. Three-dimensional (3D) tumor models were rendered using 3D Slicer Software from segmented T1-weighted MR images. Sixteen irregularity measures were calculated for each tumor using Radiomics in MATLAB. Spearman correlation coefficients (r) were computed to find associations of the dose plan indices with the irregularity descriptors. The most predictive shape feature for dose efficiency was identified using the least absolute shrinkage and selection operator (Lasso). RESULTS: The shape irregularity measures were negatively correlated with SI, EI, and PCI, and positively correlated with GI. Volumetric index of sphericity (VioS) had the highest correlations with SI (r = 0.63, p= 3.27E-23), GI (r= -0.58, p= 1.10E-19), EI (r = 0.69, p= 0.00), and PCI(r= 0.68, p = 6.73E-28), and Lasso feature selection identified VioS as the most important feature for predicting all dose plan indices. CONCLUSION: VioS provides a numerical quantification of tumor shape irregularity, and it is highly correlated with the GKRS dose planning indices. *indicates co-senior authors

Meningioma is the most common primary intracranial tumor in adults, and loss of NF2 function in meningiomas has been associated with a more aggressive biology, shorter time to recurrence and shorter overall survival. In this work, radiomics based biomarkers of NF2 copy number loss (NF2-L) have been assessed. Lower grey-level co-occurrence matrix cluster shade with wavelet high-high-low pass filter, lower original image first order minimum, and higher first order skewness with wavelet low-low-high pass filter were observed in tumors with NF2-L compared to tumors with no copy number loss. The classification accuracy of NF2 molecular subsets was 0.80&plusmn;0.03 (precision=0.85&plusmn;0.04, recall=0.73&plusmn;0.05).