METASTRA: CoMputer-aided EffecTive frActure risk STRAtification of patients with vertebral metastases for personalised treatment through robust computational models validated in clinical settings

预后良好的癌症患者（欧洲有270万）继发性肿瘤的发病率很高（100万）。在30-70%的病例中，骨转移扩散到脊柱，降低了椎骨的承载能力，并在30%的病例中引发骨折。临床医生只有两种选择：要么手术稳定脊柱，要么让病人暴露在高骨折风险中。这一决定是高度主观的，可能导致不必要的手术，或骨折严重影响生活质量和癌症治疗。对脊椎转移患者进行分类的标准治疗是基于放射学图像的评分系统，很少考虑局部生物力学。目前的评分系统无法在约60%的病例中确定手术指征。因此，需要准确及时地量化骨折风险，以改善患者分层并确定最佳个性化治疗。该跨学科项目将开发基于人工智能（AI）和生理学（VPH）的生物力学计算模型，以分层具有骨折高风险的脊柱转移患者，并确定最佳个性化手术治疗。在使用临床（2000例回顾性病例）和生物力学（120例离体标本）数据进行严格的模型训练后，将在一项多中心前瞻性观察性研究（200例患者）中对新方法进行测试。这些模型将结合在一个决策支持系统（DSS）中，使临床医生能够成功地对转移性患者进行分层。模型和决策支持系统的设计将适合监管要求和未来的开发。METASTRA将为转移性患者的分层和管理提出新的指南。METASTRA方法有望将不确定的诊断从目前的60%降至20%。这将减少患者的痛苦，并允许每年削减24亿欧元的开支。

Cancer patients (2.7M in Europe) with a positive prognosis are exposed to a high incidence of secondary tumours (˜1M). Bone metastases spread to the spine in 30-70% cases, reducing the load bearing capacity of the vertebrae and triggering fracture in 30% cases. Clinicians have only two options: either operate to stabilise the spine, or leave the patient exposed to a high fracture risk. The decision is highly subjective and can either lead to unnecessary surgery, or a fracture significantly affecting the quality of life and cancer treatment. The standard-of-care to classify patients with vertebral metastasis are scoring systems based on radiographic images, with little consideration of the local biomechanics. Current scoring systems are unable to establish an indication for surgery in around 60% of cases. Thus, there is an unmet need to accurately and timely quantify the risk of fracture to improve patient stratification and identify the best personalised treatment. This interdisciplinary project will develop Artificial Intelligence (AI)- and Physiology-based (VPH) biomechanical computational models to stratify patients with spine metastasis who are at high risk of fracture and to identify the best personalised surgical treatment. After rigorous model training with clinical (2000 retrospective cases) and biomechanical (120 ex vivo specimens) data, the new approach will be tested in a multicentric prospective observational study (200 patients). The models will be combined in a decision support system (DSS) enabling clinicians to successfully stratify metastatic patients. The models and the DSS will be designed so as to be suitable for regulatory requirements and future exploitation. METASTRA will propose new guidelines for the stratification and management of metastatic patients. METASTRA approach is expected to cut the uncertain diagnoses from the current 60% down to 20% of cases. This will reduce patient suffering, and allow cutting expenditure by 2.4B€/year.