Calibration with or without phantom for fracture risk prediction in cancer patients with femoral bone metastases using CT-based finite element models
Calibration with or without phantom for fracture risk prediction in cancer patients with femoral bone metastases using CT-based finite element models
Florieke EggermontID 0 1
Nico Verdonschot 0 1
Yvette van der Linden 1
Esther Tanck 0 1
0 Orthopaedic Research Laboratory, Radboud Institute for Health Sciences, Radboud University Medical Center , Nijmegen , The Netherlands , 2 Laboratory of Biomechanical Engineering, University of Twente , Enschede , The Netherlands , 3 Department of Radiotherapy, Leiden University Medical Center , Leiden , The Netherlands
1 Editor: Mar ??a Angeles Pe ?rez, Universidad de Zaragoza , SPAIN
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
The objective of this study was to develop a new calibration method that enables calibration
of Hounsfield units (HU) to bone mineral densities (BMD) without the use of a calibration
phantom for fracture risk prediction of femurs with metastases using CT-based finite
element (FE) models. Fifty-seven advanced cancer patients (67 femurs with bone metastases)
were CT scanned atop a separate calibration phantom using a standardized protocol.
Nonlinear isotropic FE models were constructed based on the phantom calibration and on two
phantomless calibration methods: the ?air-fat-muscle? and ?non-patient-specific? calibration.
For air-fat-muscle calibration, peaks for air, fat and muscle tissue were extracted from a
histogram of the HU in a standardized region of interest including the patient?s right leg and
surrounding air. These CT peaks were linearly fitted to reference ?BMD? values of the
corresponding tissues to obtain a calibration function. For non-patient-specific calibration,
an average phantom calibration function was used for all patients. FE failure loads were
compared between phantom and phantomless calibrations. There were no differences in
failure loads between phantom and air-fat-muscle calibration (p = 0.8), whereas there was a
significant difference between phantom and non-patient-specific calibration (p<0.001).
Although this study was not designed to investigate this, in four patients who were scanned
using an aberrant reconstruction kernel, the effect of the different kernel seemed to be
smaller for the air-fat-muscle calibration compared to the non-patient-specific calibration.
With the air-fat-muscle calibration, clinical implementation of the FE model as tool for
fracture risk assessment will be easier from a practical and financial viewpoint, since FE models
can be made using everyday clinical CT scans without the need of concurrent scanning of
collection and analysis, decision to publish, or
preparation of the manuscript.
Patients with advanced cancer and bone metastases have an increased risk of a pathological
fracture. Occurrence of these fractures in the femur of the patient leads to immediate reduced
mobility, pain and distress, and causes reduced quality of life. When patients present with a
painful femoral metastasis, treatment plans are based on the fracture risk estimated by the
clinical team: patients with a low fracture risk undergo conservative treatment such as
radiotherapy, while patients with a high fracture risk are considered for prophylactic stabilization
surgery to reduce the chance of fracturing [
]. In current clinical practice, fracture risk is
estimated using CT scans and x-rays, but this appears to be difficult, leading to over and under
treated patients .
Finite element (FE) models have shown to be promising as a tool for fracture risk prediction
]. Quantitative Computed Tomography (QCT) scans can be used to segment
patient-specific bone geometries that function as input for the FE models. Additionally, Hounsfield units
(HU) in the QCT scan can be converted to bone mineral densities (BMD) that are used to
model element-specific bone material properties [
]. Currently, these conversions to BMD
are usually done with either solid or liquid calibration phantoms that contain certain known
concentrations of for example calcium hydroxyapatite (CaCO3 or CaHA) or hydrogen
dipotassium phosphate (K2HPO4 or KHP). These calibration phantoms are of reasonable size and
need to be scanned along with the patient.
However, such separate calibration phantoms are not routinely available, quite expensive,
and result in inability to use everyday clinical CT scans without a phantom for FE modeling.
Since the FE patient databases are now dependent on CT scans including a calibration
phantom made for scientific prospective studies, the databases are currently limited in size and
clinical validation of these FE models moves slowly. If a phantomless calibration method is
available to obtain BMD from HU, FE models can be generated retrospectively from clinical
CT databases that lack calibration phantoms. In this manner, a large database could be built
more easily for further validation of FE models for clinical fracture risk assessments in patients
with femoral bone metastases. Additionally, a phantomless calibration method to use
prospectively for each patient presenting with femoral bone metastases would be very helpful for
clinical implementation of FE modeling as a fracture risk prediction tool.
Several methods have been developed for calibrating CT scans without a calibration
]. Some studies calibrate with the use of a calibration function obtained from a
separate scan containing a calibration phantom , determine calibration factors based on CT
scans that contain a calibration phantom and apply this calibration factor to CT scans without
calibration phantoms [
], or calculate BMD using a regression model based on previous
phantom calibration [
]. Another phantomless option is to use patient-specific internal calibration
methods, which are based on HU of specific tissues, such as fat and muscle tissue [
external air and either aortic blood or visceral fat . Studies comparing phantom calibration
with phantomless calibrations showed that they yielded comparable results [
studies used a single CT scanner or multiple CT scanners with a standardized protocol. Since it is
known that changes in CT protocol can yield differences in HU [
], one could expect an
effect of CT scanner or protocol for certain phantomless calibration methods on BMD or FE
outcomes. Additionally, most studies only determined the effect of different calibration
methods on vertebral trabecular BMD [
], but not on FE outcomes. The before mentioned
calibration methods function well for trabecular BMD determination, probably because they
cover HU in the same range of trabecular bone [
]. However, when applying these
calibration methods to cortical bone far outside the range of calibration values, this could lead to
extrapolation errors. Since FE models of femurs contain both trabecular and cortical bone
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material properties, it should be determined whether comparable results are obtained when
using phantomless calibrations. Although both calibration with and without a phantom are
based on linear relationships between HU and BMD, the non-linear relationship between
BMD and bone material properties  probably will affect the fracture risk as calculated by
the FE models in a non-linear manner. A study by Lee et al. tested the effect of a phantomless
calibration based on external air and visceral fat on femoral FE strength using research-quality
clinical-resolution CT scans that had been analyzed in prior clinical drug trials. They found
good correspondence between phantom and phantomless calibrations, with a mean difference
between the calibrations of 30 N (0.8%) [
]. Another study used a general calibration function
to calibrate CT scans for femoral FE models and found errors of total strain energy of 0.91% in
comparison with a phantom calibration [
Nevertheless, for FE modeling purposes, phantomless calibration methods have been
studied limitedly. In addition, comparisons between several phantomless calibration methods for
FE modeling have never been made.
The aim of this study was to develop a new calibration method that enables calibration of
HU to BMD for finite element modeling of femurs with bone metastases without the use of a
calibration phantom. The new phantomless calibration method should yield results similar to
the phantom calibration. We evaluated two phantomless calibration methods: one based on
HU of certain tissues within the CT scan and one non-patient-specific calibration function.
Between August 2006 and September 2009 [
], and January 2015 and April 2017, patients
with cancer and bone metastases in the femur that were treated with radiotherapy in one of
four radiotherapy institutes in the Netherlands (Radboud university medical center, Nijmegen;
Leiden University Medical Center, Leiden; Radiotherapeutic Insitute Friesland, Leeuwarden;
Bernard Verbeeten Institute, Tilburg) were asked to participate in a prospective cohort study
that investigated if FE models were able to predict whether patients would or would not
fracture their femur within six months. Hence, two cohorts were included both generated with an
institutional review board approved research protocol (Regionale Toetsingscommissie
Patie?ntgebonden Onderzoek, Leeuwarden (NL12568.099.06) and Commissie Mensgebonden
Onderzoek regio Arnhem?Nijmegen (2013/305)). All patients provided written informed consent.
Institutes were instructed to generate QCT images of the patients using a standardized
protocol, with the following settings: 120 kVp, 220 or variable mA, slice thickness 3 mm, pitch 1.5,
spiral and standard reconstruction, field of view (FOV) 480 mm, in-plane resolution 0.9375
mm. In a few cases, the standardized protocol was accidentally violated, resulting in CT scans
with a different FOV or reconstruction kernel. Since patients were included from four
institutes, evidently four different CT scanners were used. These CT scanners comprised two
Philips Brilliance Big Bore (Philips-1 and Philips-2, Philips Medical Systems, Eindhoven, The
Netherlands) scanners, one GE Optima CT580 (GE Healthcare, Milwaukee, WI) and one
Toshiba Aquilion/LB (Toshiba Medical Systems, Tokyo, Japan).
Patients with predominantly blastic femoral metastasis were excluded from the current
study, as in a previous study [
], we found that the bone strength of such femurs was
overestimated, probably due to unrealistically strong material properties in the FE model because of
the high degree of mineralization in blastic lesions. Also, patients were excluded if they had a
hip or knee prosthesis, the femur was incompletely scanned or the calibration phantom was
not correctly placed, or body weight was absent from the clinical research files. This resulted in
inclusion of 57 patients, with 67 femurs that were affected with bone metastases (Philips-1: 20
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patients, 27 femurs; Philips-2: 16 patients, 18 femurs; GE: 8 patients, 8 femurs; Toshiba: 13
patients, 14 femurs).
The patients were scanned on top of a solid calibration phantom (Image Analysis, Columbia,
KY), that contained four known CaHA concentrations (0, 50, 100 and 200 mg/cm3). The
known densities in this phantom were used to calibrate HU to CaHA density, which is a
measure of BMD. A mean diaphyseal calibration was applied by determining the HU in the four
rods over nine diaphyseal slices and correlate them with the known CaHA concentrations of
the calibration phantom [
]. The selection of the diaphyseal slices was protocolized by starting
with the slice containing no buttox or genitals and selecting the 8 consecutive slices. To correct
for inter-scanner differences [
16, 17, 21
], the calibration curves were corrected toward the
Philips-1 scanner, on which the FE model was validated [
3, 6, 22
], by cross-calibration. For
this, we used phantom scans (Gammex 467 phantom, RMI Gammex, Middleton, WI, USA)
] to determine the linear correlation function between aberrant CT scanner and the
Philips-1 scanner (HUcorrected = 0.998 HU+5.48 and HUcorrected = 0.98 HU?3.32, for Philips-2 and
Toshiba, respectively. GE was not corrected). Next, we applied this function to the HU within
the calibration phantom and subsequently determined the corrected calibration function. In a
similar manner, the CT scans acquired with a different reconstruction kernel were corrected
(HUcorrected = 0.97 HU+27.54 for Toshiba). CT scans with a different FOV were not corrected,
as we have shown before that the effect on HU was negligible [
Air-fat-muscle calibration. We developed a phantomless calibration based on HU of
certain tissues (air, fat, muscle and cortical tissue) within the CT scan. We first determined the
accuracy of several calibrations with different combinations of air, fat, muscle and cortical
tissue in a pilot study (see S1 File). The combination of air, fat and muscle yielded the highest
correlation with respect to the phantom calibration, and was therefore selected as the first
phantomless calibration method.
For the air-fat-muscle calibration, the same nine diaphyseal slices as used for the phantom
calibration were selected, using the same protocol for slice selection. The succeeding steps of
the air-fat-muscle calibration were completely automated. On the nine selected slices, a square
region of interest was defined including the tissue of the right leg and some surrounding air (?
1 cm on each side of the leg; Fig 1). Next, a combined histogram of all HU in the volume of
interest (i.e. nine slices together) was created to extract the peaks for air, fat and muscle tissue
(Fig 2). The mode of the HU around the histogram peak (?50 HU) was calculated, to
determine the exact peak in HU for each of the tissues. By using the mode, the method was least
susceptible to outliers. Subsequently, the determined HU peaks were linearly fitted to the
reference ?BMD? values for each patient to obtain the air-fat-muscle calibration function.
These values were obtained by phantom calibrating the HU peaks of air, fat and muscle of a
randomized subgroup comprising 10 patients scanned on the Philips-1 scanner. Subsequently,
we averaged and rounded them, resulting in reference ?BMD? values of -840, -80 and 30 for
air, fat and muscle, respectively. The linear fits between HU and ?BMD? were very good with
an average R2 of 1.000?0.000 (slope = 1.194?0.016, intercept = 2.232?12.042). No scanner- or
kernel-specific correction was applied for the air-fat-muscle calibration method.
Non-patient-specific calibration. Additionally, we determined a non-patient-specific
calibration function to convert HU to BMD by averaging all calibration functions of all 26
patients scanned on the Philips-1 scanner (6 patients were later excluded due to
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Fig 1. An example of the region of interest (white dashed box) over nine diaphyseal slices that was used for the phantomless air-fat-muscle calibration.
abovementioned reasons). We only used the 26 patients scanned on this particular CT scanner
because of inter-scanner differences we found in previous studies [
7, 16, 17
]. The used patients
were scanned on the CT scanner on which the FE model was validated [
3, 6, 22
calibration function (BMD = 0.82 HU?4.2) was then applied to each of the 57 included patients. No
scanner- or kernel-specific correction was applied for the non-patient-specific calibration
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Fig 2. An example of a histogram of the Hounsfield units within the region of interest, used to extract the peaks
for air, fat and muscle. An additional relatively small peak is visible around 1500 HU, indicating the cortical bone of
FE models were created for each included femur according to a previously described protocol
]. These FE models have previously been validated in an experimental setting, by
comparing experimental bone strength of cadaveric femurs with simulated lytic lesion with predicted
bone strength by FE models [
3, 6, 22
]. These FE models have also shown to be able to predict
fracture risk in a clinical setting . In summary, the femoral geometry was obtained from the
CT scans (Mimics 14.0, Materialise, Leuven, Belgium) and was converted to a solid mesh
(Patran 2011, MSC Software Corporation, Santa Ana, CA, USA). Non-linear isotropic bone
material properties [
] were calculated sequentially in three ways from the BMD that were
obtained with 1) use of the calibration phantom, 2) the phantomless air-fat-muscle calibration
method and 3) the non-patient-specific calibration method. For accurate comparison, only the
material properties were varied, whereas the other aspects of the FE model remained
unchanged. The FE model was positioned in a stance configuration by aligning the femoral
head center with the knee joint center. The model was distally fixated by two bundles of
highstiffness springs and via a cup on the femoral head a displacement-driven load was applied.
MSC.MARC (v2013.1, MSC Software Corporation, Santa Ana, CA, USA) was used for the FE
simulations. We used Keyak?s material model to describe the post-failure behaviour, starting
with an initial perfectly plastic phase, followed by a strain softening phase and an indefinite
perfectly plastic phase [
]. Incremental displacement and contact normal forces were
registered and it was assumed that fracture occurred when maximum total reaction force was
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reached, which was defined as the failure load. In total, 201 FE models were made (67
femurs ? 3 calibration methods).
The phantom calibration method was used as gold standard, as the FE model was validated
with the use of this calibration method [
3, 6, 22
]. The mean failure loads and standard
deviations (SD) for each of the calibration methods were determined to enable interpretation of the
results that follow from the statistical analysis. A linear mixed model was used to determine
the differences in failure load between the different calibration methods. Femur was nested
within CT scanner, because patients were only scanned on one CT scanner each. Femur,
nested in CT scanner, was added as random intercept to disregard the variability between
femurs and CT scanners [
]. In this way, the model analyzes the effects of calibration
methods, instead of differences between femurs. Although we initially did not intend to investigate
this, it was additionally tested whether adding protocol violations (other FOV or
reconstruction kernel) as a fixed factor resulted in a better model fit based on likelihood-ratio tests.
Adding FOV as a fixed factor did not significantly improve the likelihood-ratio and was therefore
not added, whereas reconstruction kernel did and was added to the model as fixed factor. The
interaction between calibration method and kernel was added to the model, since it was
significant and increased the model?s fit based on a likelihood-ratio test. The level of significance
was defined at p<0.05. Additionally, we made the comparisons between the different
calibration methods visible by creating correlation plots and Bland-Altman plots.
Patients and CT scans
Sixty-seven femurs in 57 patients affected with bone metastases were included. Despite the
protocolization of CT scanning, five femurs of five different patients scanned on two different
CT scanners were scanned with an aberrant FOV (between 509 mm and 652 mm, instead of
480 mm). We found in a previous study that changes in FOV had little effect on failure loads
]. Additionally, on one of the CT scanners, four femurs of four different patients were
scanned with a different reconstruction kernel (detail kernel for scanning of the head, instead
of the standard bone kernel). As mentioned before, CT scans acquired with a different kernel
Differences between calibrations
The mean failure loads were 6.17 kN (SD 2.11 kN), 6.16 kN (SD 1.99 kN) and 5.95 kN (SD
2.01 kN) for the phantom calibration, air-fat-muscle calibration and non-patient-specific
The correlations between the phantom calibration and other calibrations were very high
(R2 = 0.94 for air-fat-muscle calibration and R2 = 0.94 for non-patient-specific calibration, Fig
3). There were no significant differences in FE failure loads based on the phantom calibration
and air-fat-muscle calibration (0.02 kN, 95% confidence interval (CI) -0.10?0.13 kN, p = 0.8),
whereas the difference in failure loads between the phantom calibration and the
non-patientspecific calibration was significant (-0.29 kN, 95% CI -0.41 ?-0.18 kN, p < 0.001). Similarly,
the Bland-Altman plots showed a slightly higher agreement between phantom and
air-fatmuscle calibration (-0.02, 95% CI -1.04?1.01) compared to the agreement between phantom
and non-patient-specific calibration (-0.22, 95% CI -1.27?0.83, Fig 4).
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Fig 3. Correlations between phantom and air-fat-muscle calibration (A) and between phantom and non-patient-specific calibration (B).
Changing reconstruction kernel had no significant effect on the phantom and
air-fat-muscle calibration (1.31 kN, 95% CI -0.72?3.34 kN, p = 0.2 and 0.77 kN, 95% CI -1.26?2.8 kN,
p = 0.5, respectively), whereas changing reconstruction kernel resulted in significantly higher
failure loads when using the non-patient-specific calibration (2.60 kN, 95% CI 0.57?4.63 kN,
p = 0.01).
Fig 4. Bland-Altman plots for phantom versus air-fat-muscle calibration (A) and phantom versus non-patient-specific calibration (B).
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Failure locations of the individual femurs were similar for the different calibration
Previously, we developed a patient-specific FE model that can be used in clinical practice to
differentiate between high or low fracture risk in advanced cancer patients with femoral bone
]. This FE model has been based on CT scans that are calibrated using a separate
calibration phantom that is scanned together with the patient. The aim of this study was to
develop and evaluate two phantomless calibration methods for FE modeling of femurs of
patients with cancer and bone metastases.
We developed the air-fat-muscle calibration and the non-patient-specific calibration. We
found strong correlations between the phantom calibration and both phantomless calibration
methods. In addition, there was no significant difference in failure load between the phantom
calibration and air-fat-muscle calibration, whereas the difference between phantom and
nonpatient-specific calibration was significant. Although the difference between the phantom and
phantomless calibrations may seem small, it can be critical for patients that have failure loads
around the threshold distinguishing patients with a low fracture risk from patients with a high
fracture risk. Additionally, although this study was not designed to investigate this, it should
be mentioned that the non-patient-specific calibration worked well for protocolized CT scans,
but seemed to have trouble correcting for changes in reconstruction kernel. In those cases, the
air-fat-muscle calibration was more accurate. Changes in FOV did not lead to differences in
accuracy of the calibration methods, probably because changes in FOV have little effect on HU
]. When the same non-patient-specific calibration function is used for different CT
protocols, this method lacks accuracy. This is inconvenient, since one would need to have a
calibration function for each time a new CT scanner of protocol is being used. Therefore, a
patient-specific calibration method, such as the air-fat-muscle calibration, would be more
useful and robust.
Lee et al. tested a non-patient-specific phantomless calibration function on femoral FE
models, and found it to be reliable as a replacement for phantom calibration [
they used one CT scanner and well protocolized CT scans, and therefore it has not yet been
investigated whether non-patient-specific calibration methods are also useable for CT scans
obtained on different CT scanners and with different settings. Additionally, another study
investigated phantomless calibration for FE purposes and found comparable results between
the phantom and phantomless calibrations [
]. They tested their phantomless air-fat method
in 40 patients scanned on 24 different CT scanners, but only included scans that were made
according to a standard scan protocol. Other studies used combinations of air and fat [
fat and muscle [
] for their phantomless calibrations, but none used the combination of
air, fat and muscle, like we did. In a pilot study (S1 File), we first determined the accuracy of
several calibrations with different combinations of air, fat, muscle and cortical tissue, but we
found that the combination of air, fat and muscle yielded the highest correlation with respect
to the phantom calibration. Addition of cortical tissue did not to improve the correlation,
which can be explained by the large variation in cortical density between patients.
In a number of other studies small ROIs were placed within certain tissues by hand to
obtain the reference HU for the calibration [
], which can be susceptible to inter-observer
errors. We limited the variation between possible observers by automating all calibration
methods. The only step that required manual input was the selection of the CT slices used for
the calibrations. However, slice selection was strictly protocolized as well.
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Each calibration method has its advantages and disadvantages. Calibration based on a
calibration phantom that is scanned along with the patient has the benefit that the phantom is
similarly affected as the FE modeled bone itself by patient- and scan-specific characteristics or
artifacts, such as beam hardening, and might therefore be able to correct for such scan-specific
characteristics or artifacts. The air-fat-muscle calibration method may be even better in
capturing such potential artifacts, since the tissues on which the calibration is based are closer to the
FE modeled bone compared to a calibration phantom. The non-patient-specific calibration
cannot correct for any patient-specific CT artifacts.
Another important advantage of a calibration phantom is that the calcium densities in this
phantom are precisely known. However, these usually only comprise low calcium densities,
since high densities in the calibration phantom can lead to unnecessary artifacts in patient CT
scans. As a result, the low calcium densities in the calibration phantom have to be extrapolated
to higher (cortical) bone-equivalent densities, which is susceptible to errors. That also applies
to the air-fat-muscle calibration.
Furthermore, we have seen that calibration phantoms can be affected by shadow artifacts
caused by air gaps between patient and calibration phantom, leading to errors in the
7, 16, 17
]. Such shadow artifacts are not relevant for the air-fat-muscle calibration.
However, possible patient-specific variations in fat or muscle composition can affect the
air-fatmuscle calibration, mainly when a patient is suffering from pathologies that are known to
affect the attenuation values on CT [
]. We assume that variations will usually be small, since
we used the mode instead of mean or median of the HU peaks. Taking air as reference should
be without problems, as the radiodensity of air is also used for calibration of attenuation
coefficients to HU. The main disadvantage of the patient-specific calibration involves the fact that
HU can vary between different CT scanners [
16, 17, 24?26
], and therefore it might be better to
use scanner-specific calibration functions, as mentioned before. However, this requires
generating new calibration functions each time a new scanner is used, which is quite
Moreover, the major downsides of using separate calibration phantoms in clinical practice
are the fact that patients have to lie on top of a separate mattress, and for the departments the
expensive price and the logistical challenges it brings to scan each patient using the phantom.
Additionally, when using the phantom calibration, there is a need for a scanner- and
kernelspecific correction, for which extra CT scans of a tissue characterizing phantom have to be
]. This requires CT scanning and analyzing of many extra CT scans, mainly when
one would want corrections for all different reconstruction kernels, which is hardly workable.
Both the air-fat-muscle as the non-patient-specific calibration do not require additional
logistics or costs. Also, the air-fat-muscle calibration seems to be less affected by changes in CT
scanner or protocols, possibly because it uses reference tissues that are closer to the FE
modeled bone in the isocenter. It is known that CT scans are affected by scanner and settings in a
non-uniform manner, with a different effect near the isocenter of the FOV in comparison to
the edges of the FOV, where the calibration phantom is placed. On the contrary, using the
same non-patient-specific calibration function on all CT scanners and for all CT protocols, it
will not be able to supply any form of correction. As a result, air-fat-muscle calibration seemed
to be preferable over non-patient-specific calibration, as the air-fat-muscle calibration seemed
to be better in handling deviations to the scan protocol comparable to the phantom
It should be mentioned that there was no gold standard while evaluating the different
calibration methods. As the FE model has been validated while making use of a phantom
], we chose this method to be the gold standard. This validation was done by creating
FE models of cadaveric femurs, which were experimentally loaded until failure, and correlating
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the predicted failure load with the experimental failure load [
]. Although it is impossible to
achieve this, it would be better to know the real failure loads of the patients? femurs. Then it
would be possible to investigate which of all calibration methods is the best in approaching the
true failure loads.
In conclusion, phantomless calibration of CT scans using the air-fat-muscle calibration
method is preferable over the non-patient-specific calibration method. The phantomless
calibration method will stimulate the prospective use of the FE model as a fracture risk prediction
tool for each patient that presents with femoral bone metastases with clinical implementation
as ultimate goal. Additionally, with the use of the phantomless calibration method, FE models
of retrospective CT scans without calibration phantoms can be generated and a large database
can be built that can be used for the validation of FE models for application to fracture risk
assessment in patients with femoral bone metastases.
S1 Dataset. All relevant data.
S1 File. Pilot study to determine the most accurate phantomless calibration method.
Conceptualization: Florieke Eggermont, Nico Verdonschot, Yvette van der Linden, Esther
Data curation: Florieke Eggermont.
Formal analysis: Florieke Eggermont.
Methodology: Florieke Eggermont, Nico Verdonschot, Yvette van der Linden, Esther Tanck.
Supervision: Nico Verdonschot, Yvette van der Linden, Esther Tanck.
Visualization: Florieke Eggermont.
Writing ? original draft: Florieke Eggermont.
Writing ? review & editing: Florieke Eggermont, Nico Verdonschot, Yvette van der Linden,
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