Objective
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.
Fields of science
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Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
40126 Bologna
Italy
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Participants (11)
50009 Zaragoza
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40136 Bologna
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1126 Budapest
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
3584 CX Utrecht
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10117 Berlin
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Participation ended
22335 Hamburg
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6720 Szeged
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
34123 Trieste
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
92100 Boulogne Billancourt
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
66386 St Ingbert
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
10000 Zagreb
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
Partners (4)
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
8092 Zuerich
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
7270 Davos Platz
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
S10 2TN Sheffield
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
6720 Szeged
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