Elsevier

Bone

Volume 69, December 2014, Pages 180-190
Bone

Predicting the stiffness and strength of human femurs with real metastatic tumors

https://doi.org/10.1016/j.bone.2014.09.022Get rights and content

Highlights

  • Patient specific finite element analyses of femurs with metastatic tumors are performed.

  • Stiffness and strength of these femurs with realistic tumors are predicted.

  • The FE analyses are validated by in-vitro experiments on 14 femurs with tumors.

  • Excellent predictions demonstrated for femurs' siffness and good for strength.

Abstract

Background

Predicting patient specific risk of fracture in femurs with metastatic tumors and the need for surgical intervention are of major clinical importance. Recent patient-specific high-order finite element methods (p-FEMs) based on CT-scans demonstrated accurate results for healthy femurs, so that their application to metastatic affected femurs is considered herein.

Methods

Radiographs of fresh frozen proximal femur specimens from donors that died of cancer were examined, and seven pairs with metastatic tumor were identified. These were CT-scanned, instrumented by strain-gauges and loaded in stance position at three inclination angles. Finally the femurs were loaded until fracture that usually occurred at the neck. Histopathology was performed to determine whether metastatic tumors are present at fractured surfaces. Following each experiment p-FE models were created based on the CT-scans mimicking the mechanical experiments. The predicted displacements, strains and yield loads were compared to experimental observations.

Results

The predicted strains and displacements showed an excellent agreement with the experimental observations with a linear regression slope of 0.95 and a coefficient of regression R2 = 0.967. A good correlation was obtained between the predicted yield load and the experimental observed yield, with a linear regression slope of 0.80 and a coefficient of regression R2 = 0.78.

Discussion

CT-based patient-specific p-FE models of femurs with real metastatic tumors were demonstrated to predict the mechanical response very well. A simplified yield criterion based on the computation of principal strains was also demonstrated to predict the yield force in most of the cases, especially for femurs that failed at small loads. In view of the limited capabilities to predict risk of fracture in femurs with metastatic tumors used nowadays, the p-FE methodology validated herein may be very valuable in making clinical decisions.

Introduction

One third to one half of all cancers (especially breast, prostate, renal, thyroid, and lung cancer) metastasize to bones [3], which in turn leads to pathological fractures or symptoms severe enough to require treatment in 30–50% of these cases [9]. Currently, to assess the fracture risk in patient with skeletal metastasis clinicians use the Mirels' criterion or rely on their past clinical experience. The Mirels' criterion is however not very specific (91% sensitive, 35% specific) [18], [4] and results in unnecessary internal fixation procedures in two thirds of the patients.

In recent years more accurate methods based on computed tomography (CT) have been suggested to predict the risk of fracture that take into consideration both the patient specific geometrical description and the spatial distribution of material properties in bones with metastases (especially lytic types). These include the CT based structural rigidly analysis (CTRA) that is mainly applicable to shaft regions [21], [19] and CT based finite element methods (FEMs) [13], [14], [22], [15], [23], [5]. A summary of past FE investigations for human femurs with real/simulated metastatic tumors is given in Table 1.

Most past studies that use FEMs for the assessment of fractures risk in femurs with metastases are limited because they are “validated” by healthy bones with artificially created defects that do not well represent actual metastatic tumors.

Metastases are associated with major trabecular bone loss before cortical bone loss and a considerable percentage of these tumors are mixed blastic-lytic ones. In addition, the borders between tumor and non-tumor affected areas usually do not have sharp boundaries. In this respect we cite [13], “…we found that femoral shafts with hemispheric burr holes do not accurately simulate the force versus displacement behavior of shafts with metastatic lesions.” To the best of the authors' knowledge, the only previous study that considers FEMs of fresh frozen proximal femurs with real metastases that are validated by experimental observations is Ref. [14]. In that pioneering study eight femurs with metastatic tumors, out of 44 femurs altogether, are considered for the determination of the fracture load. In Ref. [14] the authors had to artificially alter the material properties of the bone tissue in the FE analysis on a “calibration cohort” of 18 femurs, 4 of which are with metastases (by comparing FEM fracture loads to the ones in experiments) to enable a better prediction of subsequent 26 femurs (4 with a metastasis). In spite of the fact that fracture occurrence is based on stress and/or strain criteria, none of the previous publications on the topic report on any validation procedure for these quantities. Finally, none of these past publications performed histological analyses of the fractured bones to determine the type of metastases and whether the presence of a tumor influenced the fracture location.

Leveraging the success of predicting the mechanical response of intact femurs with very high accuracy by high-order FEMs [27], [31], [25], [26], we extend the developed methods to femurs with metastatic tumors. There are four novelties in the present study: a) a large cohort of femurs with real metastatic tumors (14 femurs from seven donors) is considered; b) a variety of metastatic tumors representing several different types of cancers are investigated; c) a detailed and thorough investigation of the femur's mechanical response (displacements and strains are validated); d) pathological examination of the fracture surface to identify whether metastases are present and the precise tumor type.

We aim to provide rigorous evidence that patient-specific high-order FEMs are accurate and reliable to be used as a decision support system by orthopedic surgeons, especially in complex situations of femurs with metastatic tumors.

Section snippets

Materials and methods

Fourteen fresh-frozen human femurs (seven pairs denoted by FFM1–FFM7) with proximal metastatic tumors were chosen by an experienced orthopedic physician based on radiographs (see Fig. 1) and cause of death. Donor details are summarized in Table 2.

These femurs underwent mechanical experiments after they were defrosted, cleaned of soft tissues and degreased with ethanol. The proximal femur (~ 250 mm from the top of the head) was fixed into a cylindrical metallic sleeve by PMMA, immersed in water

Experimental results

Strains and displacements recorded during the mechanical tests showed a linear relationship with the applied load (excluding the fracture experiments) as shown by a typical example in Fig. 7.

A non-typical response was noticed for the FFM2 pair, i.e. an increase in strains with an increase in the femoral inclination angle. This response was not previously noted in any of our prior experiments on 31 femoral specimens. Therefore the FFM2 results were excluded with the belief that they represented

Discussion

When presented with a patient with a metastatic long bone tumor the physician must make several clinical decisions. These are dependent on the projected treatment response of the lesion, the mechanical strength of the affected bone and the patient's estimated life expectancy. If the tumor is deemed possibly responsive to treatment, then its strength may be expected either not to deteriorate or even to increase. On this basis the physician must decide either to allow the patient normal

Conflict of interest

None of the authors have any conflict of interest to declare that could bias the presented work.

Acknowledgments

The authors thank Mr. Ilan Gilad and Natan Levin from the Ben-Gurion University of the Negev, Israel, for their help with the experiments. The first author gratefully acknowledges the generous support of the Technical University of Munich-Institute for Advanced Study, funded by the German Excellence Initiative. This study was supported in part by grant no. 3-00000-7375 from the Chief Scientist Office of the Ministry of Health, Israel.

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