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Patient Specific Finite Element Modeling Outputs Outperform Clinical Metrics in Predicting Fusion Cage Subsidence.

Francis Lali¹, Kay Raftery¹, Hannah Levy²

¹Department of Bioengineering, Imperial College London, UK.

|March 26, 2026

Summary

This summary is machine-generated.

Finite element (FE) analysis shows that preoperative CT-derived FE models can predict cage subsidence in transforaminal lumbar interbody fusion (TLIF) patients. These models offer improved accuracy over traditional clinical measurements for predicting subsidence.

Keywords:

TLIF biomechanics finite element modelling fusion cage fusion cage subsidence interbody fusion spine surgery

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Area of Science:

Spinal surgery
Biomechanical engineering
Medical imaging analysis

Background:

Cage subsidence is a common complication after spinal fusion, potentially leading to reoperation.
Existing methods lack a validated tool integrating patient anatomy, bone quality, and implant characteristics for subsidence prediction.
Finite element (FE) models show promise for predicting subsidence but require clinical validation.

Purpose of the Study:

To evaluate if preoperative CT-derived FE model outputs enhance subsidence prediction in transforaminal lumbar interbody fusion (TLIF) patients compared to conventional clinical measurements.
To assess the clinical utility of FE models in predicting cage subsidence.

Main Methods:

Patient-specific FE models were constructed from preoperative CT scans of 42 TLIF patients (22 severe subsidence, 20 non-severe).
Vertebral geometries were segmented, and bone material properties were assigned based on Hounsfield units (HU).
FE model outputs (stresses and strains) were compared to clinical metrics using receiver operating characteristic analysis.

Main Results:

FE simulations revealed significantly higher principal stresses and strains in patients with severe subsidence.
Average trabecular intermediate strain (AUC=0.809) and peak endplate minimum principal stress (AUC=0.775) outperformed traditional clinical metrics.
Traditional metrics like cage length (AUC=0.797) showed lower discriminative ability.

Conclusions:

Patient-specific FE model outputs significantly correlate with clinical subsidence outcomes.
FE models outperform traditional metrics in classifying severe subsidence, highlighting the importance of endplate and trabecular stresses/strains.
Integrating FE models into clinical workflows could establish a comprehensive preoperative subsidence prediction tool.