Ensemble Models for Predicting Treatment Response in Pediatric Low-Grade Glioma Managed with Chemotherapy

TL;DR

This study presents an ensemble approach combining MRI segmentation, radiomics, and clinical data to predict chemotherapy response in pediatric low-grade glioma, aiming for personalized treatment planning.

Contribution

The paper introduces a novel ensemble pipeline integrating segmentation, radiomics, and clinical data for non-invasive prediction of chemotherapy response in challenging pediatric brain tumor cases.

Findings

Swin-Ensemble achieved the highest accuracy of 0.69.

The model outperformed other approaches like Mamba-FeatureFuse.

Ensemble method shows promise for personalized therapy prediction.

Abstract

In this paper, we introduce a novel pipeline for predicting chemotherapy response in pediatric brain tumors that are not amenable to complete surgical resection, using pre-treatment magnetic resonance imaging combined with clinical information. Our method integrates a state-of-the-art pediatric brain tumor segmentation framework with radiomic feature extraction and clinical data through an ensemble of a Swin UNETR encoder and XGBoost classifier. The segmentation model delineates four tumor subregions enhancing tumor, non-enhancing tumor, cystic component and edema which are used to extract imaging biomarkers and generate predictive features. The Swin UNETR network classifies the response to treatment directly from these segmented MRI scans, while XGBoost predicts response using radiomics and clinical variables including legal sex, ethnicity, race, age at event (in days), molecular subtype, tumor locations, initial surgery status, metastatic status, metastasis location, chemotherapy type, protocol name and chemotherapy agents. The ensemble output provides a non-invasive estimate of chemotherapy response in this historically challenging population characterized by lower progression-free survival. Among compared approaches, our Swin-Ensemble achieved the best performance (precision for non effective cases=0.68, recall for non effective cases=0.85, precision for chemotherapy effective cases=0.64 and overall accuracy=0.69), outperforming Mamba-FeatureFuse, Swin UNETR encoder, and Swin-FeatureFuse models. Our findings suggest that this ensemble framework represents a promising step toward personalized therapy response prediction for pediatric low-grade glioma patients in need of chemotherapy treatment who are not suitable for complete surgical resection, a population with significantly lower progression free survival and for whom chemotherapy remains the primary treatment option.