Brain tumor growth is unique to each patient and extends beyond what is visible in imaging scans, infiltrating surrounding brain tissue. Understanding these hidden patient-specific progressions is essential for effective therapies. Current treatment plans for brain tumors, such as radiotherapy, typically involve delineating a uniform margin around the visible tumor on pre-treatment scans to target this invisible tumor growth. This "one size fits all" approach is derived from population studies and often fails to account for the nuances of individual patient conditions. We present the framework GliODIL which infers the full spatial distribution of tumor cell concentration from available multi-modal imaging. This is achieved through the newly introduced method of Optimizing the Discrete Loss (ODIL), where both data and physics-based constraints are softly assimilated into the solution. Our test dataset comprises 152 glioblastoma patients with pre-treatment imaging and post-treatment follow-ups for tumor recurrence monitoring. By blending data-driven techniques with physics-based constraints adapted for complex cases, GliODIL enhances recurrence prediction in radiotherapy planning, offering a superior alternative to traditional uniform margins and strict PDE adherence.

翻译：脑肿瘤生长具有患者特异性，且超出影像可见范围，向周围脑组织浸润。理解这些隐匿的患者特异性进展对有效治疗至关重要。当前脑肿瘤治疗方案（如放射治疗）通常通过在治疗前影像中围绕可见肿瘤划定均匀边界来定位不可见的肿瘤生长。这种“一刀切”方法源于群体研究，往往无法考虑个体患者情况的细微差异。我们提出GliODIL框架，从可用多模态影像中推断肿瘤细胞浓度的全空间分布。这通过新引入的离散损失优化（ODIL）方法实现，其中数据约束和物理约束被软性同化纳入解中。测试数据集包含152例胶质母细胞瘤患者，包含治疗前影像及治疗后随访以监测肿瘤复发。通过将数据驱动技术与适应复杂病例的物理约束相融合，GliODIL增强了放射治疗计划中的复发预测，为传统均匀边界和严格PDE约束提供了更优的替代方案。