用于预测化疗反应的集成模型：在无法完全手术切除的儿童低级别胶质瘤中使用化疗
Ensemble Models for Predicting Treatment Response in Pediatric Low-Grade Glioma Managed with Chemotherapy
AI总结：
本文提出了一种集成模型，通过结合影像组学和临床数据，预测无法完全手术切除的儿童低级别胶质瘤患者对化疗的反应。
摘要：
本文介绍了一种新的管道，用于预测无法完全手术切除的儿童脑肿瘤对化疗的反应，结合治疗前的磁共振成像和临床信息。我们的方法整合了最先进的儿童脑肿瘤分割框架、影像组学特征提取和临床数据，通过集成Swin UNETR编码器和XGBoost分类器。分割模型确定四个肿瘤亚区：增强肿瘤、非增强肿瘤、囊性成分和水肿，用于提取影像生物标记物并生成预测特征。Swin UNETR网络直接从这些分割的MRI扫描中分类反应，而XGBoost使用影像组学和临床变量进行预测，包括性别、种族、年龄、分子亚型、肿瘤位置、初始手术状态、转移状态、转移位置、化疗类型、协议名称和化疗药物。集成输出提供了对这一历史上具有较低无进展生存率的群体的非侵入性化疗反应估计。在比较的方法中，我们的Swin-Ensemble在非有效病例的精确度为0.68、非有效病例的召回率为0.85、化疗有效病例的精确度为0.64和总体准确率为0.69，优于Mamba-FeatureFuse、Swin UNETR编码器和Swin-FeatureFuse模型。我们的发现表明，这种集成框架代表了向预测需要化疗治疗的儿童低级别胶质瘤患者个性化治疗反应的一步，这些患者不适合完全手术切除，是一个具有显著较低无进展生存率的群体，对于他们而言，化疗仍然是主要的治疗选择。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
