Patients (2.7M in EU) with positive cancer prognosis frequently develop metastases (≈1M) in their remaining lifetime. In 30-70% cases, metastases affect the spine, reducing the strength of the affected vertebrae. Fractures occur in ≈30% patients. Clinicians must choose between leaving the patient exposed to a high fracture risk (with dramatic consequences) and operating to stabilise the spine (exposing patients to unnecessary surgeries). Currently, surgeons rely on their sole experience. This often results in to under- or over-treatment. The standard-of-care are scoring systems (e.g. Spine Instability Neoplastic Score) based on medical images, with little consideration of the spine biomechanics, and of the structure of the vertebrae involved. Such scoring systems fail to provide clear indications in ≈60% patients. The HEU-funded METASTRA project is implemented by biomechanicians, modellers, clinicians, experts in verification, validation, uncertainty quantification and certification from 15 partners across Europe. METASTRA aims to improve the stratification of patients with vertebral metastases evaluating their risk of fracture by developing dedicated reliable computational models based on Explainable Artificial Intelligence (AI) and on personalised Physiology-based biomechanical (VPH) models.Introduction
Method
Prediction of bone adaptation in response to mechanical loading is useful in the clinical management of osteoporosis. However, few studies have investigated the effect of repeated mechanical loading in the mouse tibia. Therefore, this study uses a combined experimental and computational approach to evaluate the effect of mechanical loading on bone adaptation in a mouse model of osteoporosis. Six female C57BL/6 mice were ovariectomised (OVX) at week 14 and scanned using in vivo micro computed tomography (10.4µm/voxel) at week 14, 16, 18, 20 and 22. The right tibiae were mechanically loaded in vivo at week 19 and 21 with a 12N peak load, 40 cycles/day, 3 days/week. Linear isotropic homogeneous finite element (microFE) models were created from the tissue mineral density calibrated microCT images. Changes in bone adaptation, densitometric and spatial analyses were measured by comparing the longitudinal images after image registration.Abstract
Objectives
Methods
