通过MRI数据精准识别脑瘤中无增强高细胞区，助力个性化治疗
Robust Computational Extraction of Non-Enhancing Hypercellular Tumor Regions from Clinical Imaging Data
- 融合多种网络架构分析MRI，生成高细胞区概率图
- 验证结果与血流体积和复发位置高度一致，生物意义明确
- 适合神经肿瘤临床研究及精准诊疗团队参考
准确识别非增强高细胞（NEH）肿瘤区域是神经肿瘤影像学中的未满足需求，对患者管理与治疗方案制定具有重要意义。本文提出一种稳健的计算框架，基于常规MRI数据生成NEH区域的概率图，利用多种网络结构应对影像特征的内在变异与边界模糊问题。该方法通过独立临床指标——相对脑血容量（rCBV）与增强肿瘤复发位置（ETRL）进行验证，展现出方法稳健性与生物学相关性。该框架实现了对NEH肿瘤区的可靠、无创映射，支持其作为影像生物标志物融入临床流程，推动脑瘤患者的精准肿瘤学发展。
原文摘要 · Abstract (English)
Accurate identification of non-enhancing hypercellular (NEH) tumor regions is an unmet need in neuro-oncological imaging, with significant implications for patient management and treatment planning. We present a robust computational framework that generates probability maps of NEH regions from routine MRI data, leveraging multiple network architectures to address the inherent variability and lack of clear imaging boundaries. Our approach was validated against independent clinical markers -- relative cerebral blood volume (rCBV) and enhancing tumor recurrence location (ETRL) -- demonstrating both methodological robustness and biological relevance. This framework enables reliable, non-invasive mapping of NEH tumor compartments, supporting their integration as imaging biomarkers in clinical workflows and advancing precision oncology for brain tumor patients.
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