Conventional finite element models estimate the strength of metastatic human vertebrae despite alterations of the bone's tissue and structure

Stadelmann, Marc A.; Schenk, Denis; Maquer, Ghislain Bernard; Lenherr, Christopher; Buck, Florian M.; Bosshardt, Dieter D.; Hoppe, Sven; Theumann, Nicolas; Alkalay, Ron N.; Zysset, Philippe

doi:10.1016/j.bone.2020.115598

Cited by 30 publications

(68 citation statements)

References 96 publications

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“…The vertebra with blastic metastases showed, instead, a completely different behaviour [44]. As reported in other studies, the strength of vertebrae with blastic lesion is higher than the vertebrae with lytic lesions [16,45]. However, the control vertebrae were subjected to localized strain concentrations in the location corresponding to the adjacent blastic metastasis, which seemed to act as stress concentrators.…”

Section: Discussionmentioning

confidence: 50%

“…In general, the metastasis type, which is associated with the mineral content [16], the mineral distribution and the crystal size [3] of the bone, determines the behaviour of the vertebra.…”

Section: Discussionmentioning

confidence: 99%

“…1). The manual procedure was required because it was not possible to automatically segment the lesion [16] as the grey-scale value of the voxels corresponding to metastatic blastic or mixed tissues was similar to the surrounding bone. The protocol developed here was an extension of the procedure used in [35]: for each specimen, each slice of the qCT scans was inspected and the entire vertebral body and the metastasis were separately segmented and their volumes were calculated.…”

Section: Assessment Of the Metastasismentioning

confidence: 99%

“…Biomechanical evidences of the metastasis effect were explored with experimental [7][8][9][10][11][12] and computational [13][14][15][16][17][18][19] studies but their outcomes are still not unanimous [20] and they only partially explain the complexity of the problem. While it has been ascertained [21] that the metastases can weaken the bone, the relationship between metastatic features (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Type, size, and position of metastatic lesions explain the deformation of the vertebrae under complex loading conditions

Palanca

Barbanti-Bròdano

Marras

et al. 2021

Bone

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Background: Bone metastases may lead to spine instability and increase the risk of fracture. Scoring systems are available to assess critical metastases, but they lack specificity, and provide uncertain indications over a wide range, where most cases fall. The aim of this work was to use a novel biomechanical approach to evaluate the effect of lesion type, size, and location on the deformation of the metastatic vertebra. Method: Vertebrae with metastases were identified from 16 human spines from a donation programme. The size and position of the metastases, and the Spine Instability Neoplastic Score (SINS) were evaluated from clinical Quantitative Computed Tomography images. Thirty-five spine segments consisting of metastatic vertebrae and adjacent healthy controls were biomechanically tested in four different loading conditions. The strain distribution over the entire vertebral bodies was measured with Digital Image Correlation. Correlations between the features of the metastasis (type, size, position and SINS) and the deformation of the metastatic vertebrae were statistically explored. Results: The metastatic type (lytic, blastic, mixed) characterizes the vertebral behaviour (Kruskal-Wallis, p = 0.04). In fact, the lytic metastases showed more critical deformation compared to the control vertebrae (average: 2-fold increase, with peaks of 14-fold increase). By contrast, the vertebrae with mixed or blastic metastases did not show a clear trend, with deformations similar or lower than the controls. Once the position of the lytic lesion with respect to the loading direction was taken into account, the size of the lesion was significantly correlated with the perturbation to the strain distribution (r 2 = 0.72, p < 0.001). Conversely, the SINS poorly correlated with the mechanical evidence, and only in case of lytic lesions (r 2 = 0.25, p < 0.0001). Conclusion: These results highlight the relevance of the size and location of the lytic lesion, which are marginally considered in the current clinical scoring systems, in driving the spinal biomechanical instability. The strong correlation with the biomechanical evidence indicates that these parameters are representative of the mechanical competence of the vertebra. The improved explanatory power compared to the SINS suggests including them in future guidelines for the clinical practice.

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Section: Discussionmentioning

confidence: 50%

Section: Discussionmentioning

confidence: 99%

Section: Assessment Of the Metastasismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Type, size, and position of metastatic lesions explain the deformation of the vertebrae under complex loading conditions

Palanca

Barbanti-Bròdano

Marras

et al. 2021

Bone

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“…Although the FEA-based assessment of vertebral failure load is considered the gold standard (29)(30)(31), FEA is a sophisticated and methodologically demanding approach, which requires the input of assumptions for force direction and spread as well as for modeling of IVDs. Furthermore, sufficient computational power is needed, entailing a rather long duration for FEA computation that would hamper seamless application in the direct clinical setting.…”

Section: Introductionmentioning

confidence: 99%

Multi-detector computed tomography (MDCT) imaging: association of bone texture parameters with finite element analysis (FEA)-based failure load of single vertebrae and functional spinal units

Sollmann

Rayudu

Lim

et al. 2021

Quant Imaging Med Surg

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Background: Osteoporosis is a systemic skeletal disease that is characterized by low bone mass and microarchitectural deterioration, predisposing affected individuals to fragility fractures. Yet, standard measurement of areal bone mineral density (BMD) in dual-energy X-ray absorptiometry (DXA) as the current reference standard has limitations for correctly detecting osteoporosis and fracture risk, with opportunistic osteoporosis screening using computed tomography (CT) showing increasing importance. This study's objective is to compare finite element analysis (FEA)-based vertebral failure load with parameters of texture analysis (TA) derived from multi-detector CT (MDCT). Methods: MDCT data of seven subjects (mean age: 71.9±7.4 years) were included for FEA and TA. Manual segmentation was performed for the vertebral bodies T11, T12, L1, and L2 and the intervertebral discs (IVDs) T11/12, T12/L1, L1/2, and L2/3. Correlation analyses between FEA-derived failure loads and parameters of TA for the single vertebrae and two functional spinal units (FSUs) were calculated, defining FSU-1 as T11-IVD-T12-IVD-L1 and FSU-2 as T12-IVD-L1-IVD-L2. Furthermore, multivariate regressions were performed to identify the texture parameters that predicted the failure load best. Results: For single vertebrae, the strongest correlations were observed for skewnessglobal, kurtosisglobal, and gray level variance (rho =−0.7668 to −0.7362; P<0.001), while for FSUs, SumAverage, long-run emphasis, long-run low gray-level emphasis, homogeneity, and energy showed the strongest correlations (rho =−0.8187 to 0.8407; P<0.05) to failure loads. SumAverage best predicted the failure load for single vertebrae (R 2 adj =0.523, P<0.001). For the two FSUs, kurtosisglobal (FSU-1: R 2 adj =0.611, P=0.001) and skewnessglobal (FSU-2: R 2 adj =0.579, P=0.002) were the best predictors. Conclusions: TA using MDCT data of the spine was significantly associated with FEA-derived failure loads of both, single vertebrae and FSUs. Texture parameters predicted failure loads of FSUs as a more realistic in-vivo scenario equally well as compared to single vertebrae analyses. TA may reflect a less complex and time-consuming approach to accurately and non-invasively evaluate vertebral bone strength.

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Toward an artificial intelligence‐assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response

Ahmadian

Mageswaran

Walter

et al. 2022

Numer Methods Biomed Eng

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This article presents an effort toward building an artificial intelligence (AI) assisted framework, coined ReconGAN, for creating a realistic digital twin of the human vertebra and predicting the risk of vertebral fracture (VF). ReconGAN consists of a deep convolutional generative adversarial network (DCGAN), image-processing steps, and finite element (FE) based shape optimization to reconstruct the vertebra model. This DCGAN model is trained using a set of quantitative micro-computed tomography (micro-QCT) images of the trabecular bone obtained from cadaveric samples. The quality of synthetic trabecular models generated using DCGAN are verified by comparing a set of its statistical microstructural descriptors with those of the imaging data. The synthesized trabecular microstructure is then infused into the vertebra cortical shell extracted from the patient's diagnostic CT scans using an FE-based shape optimization approach to achieve a smooth transition between trabecular to cortical regions. The final geometrical model of the vertebra is converted into a high-fidelity FE model to simulate the VF response using a continuum damage model under compression and flexion loading conditions.A feasibility study is presented to demonstrate the applicability of digital twins generated using this AI-assisted framework to predict the risk of VF in a cancer patient with spinal metastasis.

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Conventional finite element models estimate the strength of metastatic human vertebrae despite alterations of the bone's tissue and structure

Cited by 30 publications

References 96 publications

Type, size, and position of metastatic lesions explain the deformation of the vertebrae under complex loading conditions

Type, size, and position of metastatic lesions explain the deformation of the vertebrae under complex loading conditions

Multi-detector computed tomography (MDCT) imaging: association of bone texture parameters with finite element analysis (FEA)-based failure load of single vertebrae and functional spinal units

Toward an artificial intelligence‐assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response

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