Assessing the reproducibility of a subject-specific finite element modelling pipeline for the human metastatic vertebrae

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Authors: Robert Roger, Rajdeep Ghosh, Yuanrui Cai, Fiona G. Gibson, Áron Lazáry, Lingzhong Guo, Damien Jerome Lacroix, Enrico Dall’Ara
Cited by: 1
References: 35

Abstract

Abstract Computed tomography (CT)-based subject-specific finite element (SS-FE) models offer a quantitative approach to assessing the stability of metastatic vertebrae. A critical step in developing SS-FE models is image segmentation, however automatic techniques fail to accurately capture the altered contours of metastatic vertebrae, making manual image segmentation the preferred approach. This introduces operator-dependent steps into the CT-to-FE modelling pipeline that may influence the model’s mechanical properties. To assess these dependencies, this study evaluated intra- and inter-operator reproducibility of a CT-to-FE pipeline applied to six lumbar vertebrae, three radiologically healthy and three lytic metastases. All models were developed using a standardised pipeline incorporating densitometric calibration, 1mm quadratic tetrahedral meshing, and heterogeneous isotropic elasto-plastic material properties, with vertebrae loaded in uniaxial compression to 1.9% apparent strain. Geometrical/volumetrical metrics from segmented images and mechanical metrics from the SS-FE models were assessed. Intra-operator reproducibility was high across geometric and mechanical metrics, whereas inter-operator reproducibility showed modest variability, particularly in local mechanical metrics of metastatic vertebrae. Consistent volumetric inconsistencies between segmentations significantly correlated with variation in predicted failure load of SS-FE models. Overall reproducibility was high, demonstrating the reliability of the CT-based SS-FE modelling pipeline for estimating vertebral strength.

Fields of Study & Subjects

Medical Imaging and Analysis Bone health and osteoporosis research Spinal Fractures and Fixation Techniques

Publication details

Journal / source
Research Square
Publisher
Springer Science and Business Media LLC
Publication date
23 February 2026
Language
EN
Document type
Preprint