Original articlePredicting bone strength from CT data: Clinical applications
Introduction
The bones forming our skeleton fracture when they are exposed to abnormal loads, or when their biomechanical competence is compromised. 77% of all unintentional injuries that occur annually in the United States are to the musculoskeletal system [1]. In total, 8.9 millions of bone fractures are associated every year to osteoporosis, worldwide [2]. Because the increased propensity to fall and overload, as well as the reduction of biomechanical competence of the skeleton are both associated with ageing, projections of prevalence are all quite concerning, because of the ageing population [3].
It is thus very important to develop reliable methods that can estimate the biomechanical strength of specific bones in the skeleton to defined loading conditions, and from those derive the risk of bone fracture associated to the reduced biomechanical competence.
In this review paper we provide a description of the most advanced technologies used for the non-invasive prediction of bone strength, and of the clinical applications that such technologies are finding.
Section snippets
Predicting bone strength
The intensity of the force required to fracture a human bone, when such bone is loaded in a given direction, is function of three biophysical determinants: the bone geometry, the biomechanical properties of the tissues forming the bone, and the loading condition.
Accuracy of bone strength predictions
Bone fracture due to overloading can occur at any anatomical site. Those associated to a reduced biomechanical competence (fragility fractures) occur most commonly at wrist, followed by the ankle, spine and hip. The hip fracture is the one involving the most severe effects, and thus it is the most studied; here below we provide quantifications of accuracy for such fracture.
The standard of care accepted in most countries uses as predictor of the risk of hip fracture the areal bone mineral
Use of QCT-FE in clinical research
On the basis of the results summarised in the previous sections, QCT-FE can predict whole bone strength as measured on cadaver bones with an accuracy around 85%, while DXA-aBMD shows an accuracy of only 77%–78%, 7 points less. On the Sheffield Cohort QCT-FE strength shows a stratification accuracy of 82%, DXA-aBMD only 75%.
Thus, any time it is clinically justified to perform a clinical CT, QCT-FE can provide a fairly accurate estimate of the whole bone biomechanical strength. This has enabled
Use of QCT-FE in clinical trials
Clinical trials of antiresorptive drugs are particularly challenging: the primary endpoint, bone fractures occur relatively rarely, and the observational time frame must be at least five years. Thus, it is commonly accepted as the use of a biomarker as a surrogate of such long-term clinical endpoint: the most common is DXA-aBMD, but some studies now use instead bone strength predicted with QCT-FE.
When the stratification accuracy of QCT-FE models as measured on the Sheffield cohort was used to
Use of QCT-FE in the clinical practice
The only routine use for QCT-FE which has been explored in term of cost-benefit is that of prognostic biomarker in the treatment planning of primary osteoporosis [29]. Here the analysis concluded that the method could become cost-effective, when compared to DXA-aBMD, only when it was used on a subset of “difficult” cases, and when the price-point for a QCT-FE was sufficiently low (US$ 100). This study, based on the UK healthcare costs, substantially confirmed the findings of another similar
Discussion
From this brief review of some of the recent literature, we can conclude that it is possible to predict with excellent accuracy the biomechanical strength of bones using finite element models informed by Quantitative Computer Tomography. These patient-specific models are now being used for clinical research, to improve clinical trials of new treatments, and in a few cases also in the routine clinical practice.
The simplicity, robustness, and reliability of this patient-specific modelling method
Disclosure of interest
This study was partially supported by the European Union H2020 grant STriTuVaD: “In Silico Trial for Tuberculosis Vaccine Development (grant ID 777123). The authors declare that they do not have any financial or personal relationships with other people or organisations that could have inappropriately influenced this study.
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