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Researchers developed an automated framework using brain atlas registration to map the precise spatial distribution of pediatric low-grade gliomas across 349 patients. The study found that tumor location patterns varied by patient age, molecular subtype, and extent of surgical resection, with younger children showing more optic pathway involvement and KIAA1549-BRAF fusion tumors appearing more frequently in the cerebellum. The automated spatial features successfully predicted surgical resection outcomes and performed comparably to manual location coding in clinical risk models.
Why it matters
This standardized approach enables more precise, reproducible characterization of tumor locations than traditional single-category assignments, which could improve surgical planning and patient risk stratification. The publicly available framework allows researchers to quantitatively compare tumor distributions across studies and better understand how location relates to treatment outcomes.
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⚠️ Preprint – Noch nicht peer-reviewed
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Pediatric low-grade gliomas (pLGGs) arise throughout the developing brain, and their anatomic distribution is related to age, molecular subtype, and extent of resection (EOR). In clinical studies, pLGG location is commonly represented by a single manually assigned anatomic category, which does not quantify the full spatial extent of tumors spanning multiple brain regions. We developed and evaluated a reproducible, publicly available pediatric atlas-based framework integrating voxel-wise probabilistic mapping with patient-level atlas-derived spatial features. In this retrospective study, preoperative MRI from 349 patients with pLGG was registered to the Haskins pediatric template using EasyReg. Probabilistic maps showed differences in tumor distribution by age, molecular subtype, and EOR, including greater optic pathway and suprasellar involvement at 0-2 years and greater cerebellar involvement in KIAA1549-BRAF fusion tumors. Atlas-derived spatial, tumor morphometric, and clinical features distinguished gross total resection/near total resection from subtotal resection or biopsy/no-surgery (Replication area under the curve, 0.864). Substituting 107 automatically computed regional overlap features for the manually assigned location variable in previously published clinico-imaging risk stratification models did not significantly alter discrimination (smallest P = 0.12). Together, these group- and patient-level analyses demonstrate the utility of the framework for quantitative spatial phenotyping in imaging and clinical studies of pLGG.
Source: Atlas-Based MRI Spatial Phenotyping in Pediatric Low-Grade Glioma