Stage IV cancer is characterized by a cancer’s ability to metastasize, or spread throughout the body. Metastatic disease in bone is a devastating condition affecting hundreds of thousands of people each year. Stage IV cancer patients suffering from metastatic disease in the proximal femur are at high risk of catastrophic pathologic fracture, an event which severely impacts patient health. Although metrics have been created to assess the risk of impending fracture, they lack specificity in the proximal femoral region. Shortcomings of these metrics further complicate clinical decision making related to prophylactic fixation in these medically compromised individuals.
Fortunately, by using computational modeling to study this at-risk patient population, the likelihood of fracture due to metastatic lesions in the proximal femur can be more accurately assessed to improve clinical decision making. Finite element analysis (FEA) is a computational modeling technique that can non-invasively provide mechanics information to better assess true fracture risk of a given metastatic lesion. Although FEA has previously been utilized to study metastatic disease, lesions were always modeled as spheres or ellipsoids, while true lesion shapes are far more amorphous. It was the focus of this study to validate FEA’s ability to predict fracture location in cadaveric femora with realistically shaped experimental metastatic lesions. Off-set torsion, or load applied off-set from the fixed long bone axis, was applied to cadaveric specimens with mechanically induced metastatic lesions, and the resultant fracture location was compared to specimen-specific FEA models replicating the mechanical test. FEA was able to correctly predict fracture locations in five models. Determining fracture risk based on objective mechanical data may more accurate and effective in this patient population.
| Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-7579 |
| Date | 01 May 2018 |
| Creators | Permeswaran, Palani Taver |
| Contributors | Goetz, Jessica |
| Publisher | University of Iowa |
| Source Sets | University of Iowa |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | Copyright © 2018 Palani Taver Permeswaran |
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