METASTRA

Start date: 1 July 2023

End date: 30 June 2028

Grant agreement ID: 101080135

Initiative aimed at transforming the way clinicians assess fracture risk in cancer patients with vertebral metastases. It promises to provide personalised treatment recommendations based on robust computational models and improved patient stratification techniques. With an avid and visionary roadmap, the international, multidisciplinary research team is poised to make a substantial impact on the lives of cancer patients and the healthcare system as a whole.

While early diagnosis and improved screening are improving the life expectancy of cancer patients across Europe, approximately 2.7 million people face an alarmingly high incidence of secondary tumours, affecting almost 1 million people. Among these cases, bone metastases spread to the spine in 30-70% of cases, causing a significant reduction in the load-bearing capacity of the vertebrae and leading to fractures in approximately 30% of patients. Currently, physicians have two subjective choices: perform surgery to stabilise the spine or leave the patient vulnerable to a high risk of fracture. These decisions often result in unnecessary surgery or fractures that severely affect both quality of life and ongoing cancer treatment.

The existing standard of care relies on scoring systems based solely on radiographic images, with limited consideration of local biomechanics. As a consequence, these systems fail to provide accurate indications for surgery in around 60% of cases, leaving a critical need for improved methods of risk quantification and patient stratification.

METASTRA will address this unmet need by developing innovative mechanistic computational models based on Artificial Intelligence (AI) and Physiology (HPV). These models will accurately stratify patients with spinal metastases who are at high risk of fractures and identify personalised surgical treatments. The project will extensively train the models using a comprehensive dataset comprising clinical data from 2,000 retrospective cases and biomechanical data from 120 ex vivo samples. Subsequently, the efficacy of the new approach will be evaluated in a multicentre prospective observational study involving 200 patients.

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