ObjectiveEncephaloceles are considered to result from defects in the developing skull through which meninges, and potentially brain tissue, herniate. The pathological mechanism underlying this process is incompletely understood. We aimed to describe the location of encephaloceles through the generation of a group atlas to determine whether they occur at random sites or clusters within distinct anatomical regions. MethodsPatients diagnosed with cranial encephaloceles or meningoceles were identi ed from a prospectively maintained database between 1984 and 2021. Images were transformed to atlas space using non-linear registration. The bone defect, encephalocele and herniated brain contents were manually segmented allowing for a 3-dimensional heat map of encephalocele locations to be generated. The centroids of the bone defects were clustered utilising a K-mean clustering machine learning algorithm in which the elbow method was used to identify the optimal number of clusters. ResultsOf the 124 patients identi ed, 55 had volumetric imaging in the form of MRI (48/55) or CT (7/55) that could be used for atlas generation. Median encephalocele volume was 14704 [IQR 3655-86746] mm 3 and the median surface area of the skull defect was 679 [IQR 374-765] mm 2 . Brain herniation into the encephalocele was found in 45% (25/55) with a median volume of 7433 [IQR 3123-14237] mm 3 . Application of the elbow method revealed 3 discrete clusters: 1) Anterior skull base (22%; 12/55), 2) Parieto-occipital junction (45%; 25/55) and 3) Peri-torcular (33%; 18/55). Cluster analysis revealed no correlation between the location of the encephalocele with gender [χ 2 (2, n = 91) = 3.86, p = 0.15]. Compared to expected population frequencies, encephaloceles were relatively more common in Black, Asian and Other compared to White ethnicities. A falcine sinus was identi ed in 51% (28/55) of cases.Falcine sinuses were more common [χ 2 (2, n = 55) = 6.09, p = 0.05] whilst brain herniation was less common [χ 2 (2, n = 55) = .16.24, p < 0.0003] in the parieto-occipital location. ConclusionThis analysis revealed three predominant clusters for the location of encephaloceles, with the parietooccipital junction being the most common. The stereotypic location of encephaloceles into anatomically distinct clusters and the coexistence of distinct venous malformations at certain sites suggests that their
BACKGROUND Diffuse midline gliomas (DMG) are lethal pediatric brain tumors with dismal prognoses. Presently, MRI is the mainstay of disease diagnosis and surveillance. We aimed to identify prognostic image-based radiomics markers of DMG and compare its performance to clinical variables at presentation. METHODS 104 treatment-naïve DMG MRIs from five centers were used (median age=6.5yrs; 18 males, median OS=11mos). We isolated tumor volumes of T1-post-contrast (T1gad) and T2-weighted (T2) MRI for PyRadiomics high-dimensional feature extraction. 900 features were extracted on each image, including first order statistics, 2D/3D Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, Neighboring Gray tone Difference Matrix, and Gray Level Dependence Matrix, as defined by Imaging Biomarker Standardization Initiative. Overall survival (OS) served as outcome. 10-fold cross-validation of LASSO Cox regression was used to predict OS. We analyzed model performance using clinical variable (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variable. Concordance metric was used to assess the Cox model. RESULTS Nine radiomic features were selected from T1gad (2 texture wavelet) and T2 (5 first-order features (1 original, 4 wavelet), 2 texture features (1 wavelet, 1 log-sigma). This model demonstrated significantly higher performance than a clinical model alone (C: 0.68 vs 0.59, p<0.001). Adding clinical features to radiomic features slightly improved prediction, but was not significant (C=0.70, p=0.06). CONCLUSION Our pilot study shows a potential role for MRI-based radiomics and machine learning for DMG risk stratification and as image-based biomarkers for clinical therapy trials.
PURPOSE Posterior fossa ependymomas (PFE) are common pediatric brain tumors often assessed with MRI before surgery. Advanced radiomic analysis show promise in stratifying risk and outcome in other pediatric brain tumors. Here, we extracted high-dimensional MRI features to identify prognostic, image-based, radiomics markers of PFE and compared its performance to clinical variables. METHODS 93 children from five centers (median age=3.3yrs; 59 males; mean PFS=50mos) were included. Tumor volumes were manually contoured on T1-post contrast and T2-weighted MRI for PyRadiomics feature extraction. Features include first-order statistics, size, shape, and texture metrics calculated on the original, log-sigma, and wavelet transformed images. Progression free survival (PFS) served as outcome. 10-fold cross-validation of a LASSO Cox regression was used to predict PFS. Model performance was analyzed and concordance metric (C) was determined using clinical variable (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variable. RESULTS Six radiomic features were selected (all T1): 1 first-order kurtosis (log-sigma) and 5 texture features (3 wavelet, 2 original). This model demonstrated significantly higher performance than a clinical model alone (C: 0.69 vs 0.58, p<0.001). Adding clinical features to the radiomic features didn’t improve prediction (p=0.67). For patients with molecular subtyping (n=48), adding this feature to the clinical plus radiomics models significantly improved performance over clinical features alone (C = 0.79 vs. 0.66, p=0.02). Further validation and model refinement with additional datasets are ongoing. CONCLUSION Our pilot study shows potential role for MRI-based radiomics and machine learning for PFE risk stratification and as radiographic biomarkers.
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