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.

翻译：准确识别非强化高细胞密度（NEH）肿瘤区域是神经肿瘤影像学中尚未满足的关键需求，对患者管理和治疗规划具有重要影响。本文提出一种稳健的计算框架，该框架利用常规MRI数据生成NEH区域的概率图，通过整合多种网络架构以应对固有的影像异质性和清晰边界缺失的挑战。我们的方法通过独立临床标志物——相对脑血容量（rCBV）与强化肿瘤复发位置（ETRL）——进行了双重验证，既证明了方法的鲁棒性，也确认了其生物学相关性。该框架实现了NEH肿瘤区域的可靠无创定位，支持其作为影像生物标志物整合到临床工作流程中，为脑肿瘤患者的精准肿瘤治疗提供了技术支撑。