Preoperative prognosis of Ependymoma is critical for treatment planning but challenging due to the lack of semantic insights in MRI compared to post-operative surgical reports. Existing multimodal methods fail to leverage this privileged text data when it is unavailable during inference. To bridge this gap, we propose HyperPriv-EPN, a hypergraph-based Learning Using Privileged Information (LUPI) framework. We introduce a Severed Graph Strategy, utilizing a shared encoder to process both a Teacher graph (enriched with privileged post-surgery information) and a Student graph (restricted to pre-operation data). Through dual-stream distillation, the Student learns to hallucinate semantic community structures from visual features alone. Validated on a multi-center cohort of 311 patients, HyperPriv-EPN achieves state-of-the-art diagnostic accuracy and survival stratification. This effectively transfers expert knowledge to the preoperative setting, unlocking the value of historical post-operative data to guide the diagnosis of new patients without requiring text at inference.

翻译：室管膜瘤的术前预后对于治疗规划至关重要，但由于MRI图像相较于术后手术报告缺乏语义层面的洞察，这一任务极具挑战。现有的多模态方法在推理阶段无法获取此类特权文本数据时，难以有效利用其信息。为弥补这一差距，我们提出了HyperPriv-EPN，一个基于超图的特权信息学习框架。我们引入了一种割裂图策略，利用一个共享编码器同时处理教师图（富含术后特权信息）和学生图（仅限术前数据）。通过双流蒸馏，学生网络学习仅从视觉特征中幻觉出语义社群结构。在一个包含311名患者的多中心队列上进行验证，HyperPriv-EPN实现了最先进的诊断准确率和生存分层能力。该方法有效地将专家知识迁移至术前场景，释放了历史术后数据的价值，从而能够在无需推理阶段文本输入的情况下指导新患者的诊断。