Personalized Finite Element Modeling of the L1-Sacrum Spine in Multiple Myeloma: A Proof-of-Concept Evaluation of Presurgical and Postsurgical Biomechanical Stability
Alireza Gholipour, Ali Kiapour, Ganesh M. Shankar, Joseph H. Schwab, Daniel G. TobertBackground/Objectives:
Multiple myeloma (MM) frequently involves the axial skeleton and can produce osteolytic spinal lesions associated with pain, instability, and fracture risk. This proof-of-concept case study developed and applied a patient-specific finite element (FE) workflow to explore whether CT-based biomechanical modeling can quantify preoperative instability and postoperative load redistribution in a representative MM spine case.
Materials and Methods:
High-resolution CT scans from a 63-year-old patient with MM-related lumbar involvement were used to reconstruct an L1-sacrum FE model. Vertebrae, discs, ligaments, and the L3 lytic lesion were segmented and assigned material properties using CT-derived estimates and published constitutive parameters. Preoperative and postoperative models were evaluated under standardized flexion, extension, lateral bending, and axial rotation loading conditions. Lesion-specific stress and strain were compared with the assigned local yield threshold, and model behavior was checked against published lumbar spine motion benchmarks.
Results:
In the preoperative model, the L3 lesion showed motion-dependent stress concentrations that exceeded the assigned local yield threshold, particularly during flexion and axial rotation. After simulated posterior fixation and cement augmentation, lesion-region stresses decreased below the failure threshold across the tested loading modes, while higher global stresses were concentrated primarily in the instrumentation.
Conclusion:
This single-patient study demonstrates the technical feasibility of a CT-based, patient-specific FE workflow for visualizing motion-specific and lesion-specific biomechanical risk in MM-related spinal disease. The findings should be interpreted as exploratory and hypothesis-generating, supporting future multipatient validation before clinical decision-making use.