Brain tumor growth is unique to each glioma 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 GliODIL framework, which infers the full spatial distribution of tumor cell concentration from available multi-modal imaging, leveraging a Fisher-Kolmogorov type physics model to describe tumor growth. 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, GliODIL enhances recurrence prediction in radiotherapy planning, challenging traditional uniform margins and strict adherence to the Fisher-Kolmogorov partial differential equation (PDE) model, which is adapted for complex cases.

翻译：脑肿瘤生长对每位胶质瘤患者具有特异性，其浸润范围常超出影像学可观测区域，深入周围脑组织。理解这些隐性的个体化进展对于制定有效治疗方案至关重要。当前脑肿瘤（如放疗）治疗计划通常基于治疗前影像在可见肿瘤周围划定统一边界以覆盖不可见肿瘤生长。这种"一刀切"方法来自群体研究，往往无法体现患者个体病情的细微差异。我们提出GliODIL框架，通过利用Fisher-Kolmogorov型物理模型描述肿瘤生长，从多模态影像中推断肿瘤细胞浓度的完整空间分布。该框架通过新引入的离散损失优化（ODIL）方法实现，将数据约束与物理约束软性融合到解中。测试数据集包含152例胶质母细胞瘤患者的治疗前影像及治疗后复发监测随访数据。通过将数据驱动技术与物理约束相结合，GliODIL在放疗计划中提升了复发预测能力，挑战了传统统一边界方法及对Fisher-Kolmogorov偏微分方程模型的严格遵循，使其适应复杂病例。