Img-12. CBTN Brain Tumor Segmentation Initiative: Updates on Model Release, HGG Segmentation, and Survival Analysis

Abstract BACKGROUND Assessment of treatment responses in pediatric brain tumors requires accurate tumor segmentation, particularly important for nonresectable tumors like diffuse midline glioma (DMG), including diffuse intrinsic pontine glioma (DIPGs). Evaluating tumor progression in these tumors relies on monitoring tumor size changes in longitudinal MRI exams which is challenged by their infiltrative growth patterns. Our team developed a comprehensive multi-institutional, multi-histology autosegmentation deep learning (DL) model leveraging data from the Children’s Brain Tumor Network (CBTN), involving continuous enhancements and enriched by an expanding dataset including post-surgical/post-treatment studies. METHODS Recently, we trained and validated this DL model (publicly available) on a multi-institutional dataset of 644 PBTs, including 273 pediatric-type diffuse high-grade gliomas (HGGs, including DMG/DIPGs), using pre-operative and post-operative/post-treatment multiparametric MRI scans. We tested the model in 23 longitudinal MRI exams of 8 DMGs from the PNOC003 and PNOC007 clinical trials and applied Cox regression for predicting overall survival (OS) using tumor volume, computed with our autosegmentation model, as a time-varying variable in 18 patients. RESULTS For HGGs in treatment-naïve sessions, our model demonstrated strong performance with median Dice scores of 0.89 for whole tumor and tumor core (encompassing all regions, except for edema), and 0.73 for enhancing tumor segmentation. In longitudinal MRI exams of DMGs from the aforementioned clinical trials, the model achieved median Dice scores of 0.79 for whole tumor, and 0.78 for enhancing tumor and tumor core segmentation. Cox regression showed the significance of longitudinal volumetric measurements (p=0.08) for predicting OS in DMGs. CONCLUSIONS Our approach provides reliable tumor segmentation in HGGs across diverse datasets, crucial for treatment response assessment. Looking ahead, we plan to validate our model on a larger set of longitudinal exams and use volumetric measurements to predict tumor progression. This ongoing effort aims to improve model performance, extend its generalizability, and mitigate potential model decay.