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ORIGINAL ARTICLE

Vertebral fracture risk (VFR) score for fracture prediction

in postmenopausal women

M. Lillholm &A. Ghosh &P. C. Pettersen &

M. de Bruijne &E. B. Dam &M. A. Karsdal &

C. Christiansen &H. K. Genant &M. Nielsen

Received: 25 January 2010 /Accepted: 2 September 2010 /Published online: 11 November 2010# International Osteoporosis Foundation and National Osteoporosis Foundation 2010

Abstract

Summary Early prognosis of osteoporosis risk is not only

important to individual patients but is also a key factor

when screening for osteoporosis drug trial populations. We

prese nt an osteo porosis fracture risk score based on

vertebral heights. The score separated individuals who

sustained fractures (by follow-up after 6.3 years) from

healthy controls at baseline.

Introduction This casecontrol study was designed to

assess the ability of three novel fracture risk scoring

methods to predict first incident lumbar vertebral fractures

in postmenopausal women matched for classical risk factors

such as BMD, BMI, and age.

Methods This was a casecontrol study of 126 postmeno-

pausal women, 25 of whom sustained at least one incident

lumbar fracture and 101 controls that maintained skeletal

integrity over a 6.3-year period. Three methods for fracture

risk assessment were developed and tested. They are based

on anterior, middle, and posterior vertebral heights mea-

sured from vertebrae T12-L5 in lumbar radiographs at

baseline. Each scores fracture prediction potential was

investigated in two variants using (1) measurements from

the single most deformed vertebra or (2) average measure-

ments across vertebrae T12-L5. Emphasis was given to the

vertebral fracture risk (VFR) score.

Results All scoring methods demonstrated significant sepa-

ration of cases from controls at baseline. Specifically, for the

VFR score, cases and controls were significantly different

(0.670.04 vs. 0.350.03, p

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Introduction

Postmenopausal osteoporosis remains a serious condition

affecting millions of individuals worldwide. Current epide-

miological evidence suggests that in industrialized

countries, approximately 40% of postmenopausal women

at the age of 60 and as many as 70% of women at the age of

80 suffer from osteoporosis [1]. Postmenopausal osteopo-rosis is characterized by a reduction in bone mass due to

increased bone resorption and a simultaneous but less

pron ounce d increase in bone formation, resu lting in

negative net calcium balance. This ongoing process fueled

by chronic estrogen deficiency may eventually lead to micro-

architectural osteoporosis, possible fractures, and substantial

deterioration in the quality of life. The cardinal feature of

osteoporosis is the occurrence of fragility fractures, typically

in the spine, but also in the forearm and hips. Whereas limb

fractures are easy to diagnose, the case is different for the

spine region, where mild vertebral fractures are often

asymptomatic. Though the mortality rate from osteoporoticfractures is the highest for those of the hip, vertebral fractures

are the most common type of fragility fractures with an

estimated occurrence of 750,000 cases per year in the USA

[2]. Osteoporotic vertebral fractures typically occur earlier

and are an established risk factor for hip fractures [3].

Presence of severe vertebral fractures has been associated

with acute and chronic pain, impaired quality of life,

increased risk of osteoporotic limb fractures, and shortened

life expectancy [4]. There is, therefore, a continuing interest

in identifying independent predictors of vertebral fractures

that could facilitate the detection of high-risk patients, who

would benefit the most from early prevention.

Vertebral fractures are often diagnosed and graded by

experienced radiologists using qualitative [5] or semi-

quantitative methods such as those described by Genant et

al. [6] and others [711]. The methods were shown to be

robust to intra-observer variations but may be difficult to

apply uniformly across different clinical centers. More

importantly, it is yet to be decided which one of these semi-

quantitative methods should be used as a gold standard

[12]. In order to overcome some of these problems, fully

quantitative methods have been developed [1323]. One of

the shortcomings of the discretenature (due to the use of

thresholds) of most of these methods is the inability to

quantify subtle changes in the vertebral shape. Hence, a

more robust and detailed study of the vertebral/spine shape

abnormalities should produce (1) an objective quantifiable

measure for detection and severity-grading of fractures and

(2) details of pre-fracture vertebral-shape changes that lead

to betterprediction of osteoporotic fractures.

The present study investigated whether computer-based

measures of fracture risk, calculated using vertebral pre-

fracture shape variations, could differentiate healthy sub-

jects who later sustain a vertebral fracture from those who

maintain vertebral integrity when matched for of an array of

traditional risk factors, including bone mineral density

(BMD). The rationale behind such an investigation is that

the detection of pre-fracture conditions and successful

prediction of vertebral fragility fractures will help the study

of osteoporosis in the following important ways: (1) early

diagnosis and treatment for patients, (2) more preciseassessment of efficacy of fracture prevention drugs by the

identification of subjects with a high likelihood of

sustaining an osteoporotic fracture, and (3) decrease in the

required sample size for clinical studies by inclusion of

high-risk subjects. In this paper, we pursue parts of the

second and third objectives and present quantitative

analyses of pre-fracture vertebral-shape changes that yield

subject scores indicating first-incidence lumbar vertebral

fracture risk. It is demonstrated how this score can be used

to rank a screening population and drive the selection of a

net study population with increased average fracture risk.

Materials and methods

A casecontrol study was designed such that case-group

patients had no prevalent lumbar vertebra fractures and by

follow-up 6.3 years later had sustained at least one fracture

in the lumbar spine only. The control group maintained

skeletal integrity throughout and was matched with respect

to an array of traditional risk factors.

The study population was chosen from the PERF cohort

[24] which consisted of 4,062 community-recruited post-

menopausal Danish women first screened between 1992 and

1995 and subsequently reviewed between 2000 and 2001.

PERF contained 662 patients with at least one new vertebral

fracture at follow-up; 88 of the 662 had no prevalent

fractures at baseline and of those, 25 suffered incident

fractures in the lumbar region (T12 to L5) only and were

selected as case patients. The case group was matched by a

fracture-free 101 large control group also from the PERF

cohort with comparable risk factors such as age, body mass

index (BMI), family history of osteoporosis, alcohol and

milk consumption, history of hormone replacement therapy,

spine BMD, smoking habits, and self-reported physical

exercise. Any patients with non-osteoporotic vertebral

deformities or non-osteoporotic fractures were excluded

before case and control groups were selected.

At baseline, none of the 126 subjects displayed any sign of

disorders of calcium metabolism or bone disease, or took any

medication known to affect bone metabolism. All subjects

were interviewed to obtain information on their medical

history, use of medication, and other life style factors. Subjects

underwent a complete physical examination; weight and

height were determined without shoes and with the subjects

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wearing light indoor clothes, and the BMI was calculated.

BMD of the spine was determined by bone densitometry

using a Lunar Prodigy scanner and lateral radiographs of the

lumbar spine were taken of the patients using a standard

technique [24]. Written consent was obtained from each

participant according to the Helsinki Declaration II. The

study was approved by the local ethics committee.

Spine radiographs acquisition and fracture assessment

Spinal examinations were performed according to pre-

approved protocols. Radiographs of the lumbar region were

taken for each of the subjects at baseline and follow-up. In the

lateral position, pillows were used to ensure good alignment

of the vertebral bodies. The distance between the focal plane

and the film was kept constant at 1.2 m and the central beam

was directed to L2. Patients were asked to hold in their

expiration for the duration of the radiograph acquisition. The

same group of staff examined each of the subjects. Anterior

posterior (AP) radiographs were used for a general view andassessment of vertebral deformities (i.e., scoliosis). Obvious

vertebral fractures in AP radiographs were noted but the

primary fracture assessment was performed on lateral radio-

graphs. Fracture assessment and classification from the

original PERF study [24] was re-evaluated and confirmed

using Genants semi-quantitative method [6] by PP who was

trained in and had several years of experience using the

method. Baseline and follow-up radiographs were viewed

simultaneously to avoid confusing fracture incidence with

undetected or borderline prevalence. The primary utility of

the fracture assessment was to establish baseline and follow-

up fracture presence. These readings were used to identify

the case (n=36) and controls group (n=108) in the paper by

Pettersen [25] in which the fracture risk predictive power of

a computer-based measure of curvature irregularity was

investigated. The case and control groups in this study were

specifically selected as subsets of the Pettersens case and

control group as described below.

Digitization of radiographs and six-point annotations

All lateral radiographs were digitized at 45 m (570

DPI). For further analysis of the images, six points

(called the height points) were placed at the corners and

at the middle points of the vertebral endplates, by the

same radiologist using a computer program; see Fig. 1a.

Using these measured heights, all vertebrae were evaluat-

ed by a computer algorithm using a modification of

Genants methodology with a strict measured threshold of

0.2 as fracture absence/presence indicator, that is, a

vertebra was considered fractured if either of the ratios

between any of the anterior, middle, and posterior heights

was 0.8 or less.

Subpopulation and borderline fractures

The Pettersens[25] population was reduced selecting only

subjects where the quantitative fracture classification was in

agreement with the experts fracture assessment. This

procedure reduced the case and controls groups described

above to 25 cases and 101 controls, respectively. The

additional step deliberately filtered out subjects where there

was borderline disagreement between the SQ- and QM-

based fracture assessments. This prevented the idea that the

fracture prediction results reported in this paper were

influenced by such disagreements.

T12

L1

L2

L3

L4

L5

baFig. 1 aAn example of a lateral

lumbar radiograph with

six-point annotations in redand

vertebra labels in black. The

midpoints are always marked on

the lower of the endplate con-

tours. b The shape of vertebrae

for various values ofHmax, Hmincompared to Genants height

ratio. For illustration purposes,

the smallest heightHminis

placed to the left and the largestheightHmax is placed to the

right in each vertebra. Each

vertebra has the corresponding

height ratio noted. Theblue area

indicates the high-risk patients

according to VFR. The green

area indicates the high-risk

patients according to Genants

height ratio and as depicted

different patients may be

indicated as high-risk patients

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Computer-based prediction of vertebral fractures

Three quantitative scores based on vertebral height mor-

phometry were developed to predict first incident lumbar

fractures. The scores were named the vertebral fracture risk

(VFR), the most deformed height ratio (MDHR), and the

most deformed height anterior height posterior ratio

(MDHaHp). Each of the three scores was tested in twoversions: (1) only the most deformed vertebra determined

the score and (2) the average over vertebrae T12-L5

determined the score.

In the following, we initially describe the VFR score on

the most deformed vertebra in detail. The remaining two

scores and the average versions of all scores use the same

overall methodology as the VFR score and only deviations

from this are presented.

Computation of the VFR score consisted of two steps:

(1) selection of the most deformed vertebra and (2) scoring

of this vertebra to form a single score for the patient:

Step 1

Selection of the most deformed vertebra: For a given

patient, the vertebral height ratios of the smallest to the

largest anterior, middle, or posterior heights were computed

for all vertebrae. The vertebra among T12 to L5 with

minimal height ratio was denoted the most deformed and

chosen to represent each patient. Due to the patient

selection described above, all ratios were above 0.8.

Step 2

VFR scoring: Each selected vertebra was represented by the

maximal Hmax and minimal Hmin of its three vertebral

heights. The selected heights were normalized by the mean

of the vertebral heights in question:

Hmax maxfHant;Hmed;Hpostg=meanfHant;Hmed;Hpostg;Hmin minfHant;Hmed;Hpostg=meanfHant;Hmed;Hpostg

Consequently, Hmax1.0 and Hmin1.0 for any vertebra.

The relationship between Hmax, Hmin, and height ratios as

used in, for example, Genants method is illustrated in

Fig. 1b. Each patient in the case and control groups was

represented by their normalized height pair. Vertebral height

pairs in eac h gro up wer e ass ume d to be normally

distributed, that is following a bivariate normal distribution

in (Hmax, Hmin). The empirical mean and covariance

matrix were estimated as standard maximum likelihood

estimates for each of the case and control groups and the

likelihood Pof belonging to each group expressed as:

Pcase Hmax;Hmin N mcase;P

case Pcontrol Hmax;Hmin N mcontrol;

Pcontrol

For new vertebrae, the relative likelihood:

Pcase Hmax;Hmin = Pcase Hmax;Hmin Pcontrol Hmax;Hmin

of belonging to the case group was computed from the

estimated normal distributions. This relative likelihood ratio

was defined to be the VFR score. It is, as constructed, a

number between 0 and 1 representing the probability of

sustaining a fracture. It should be expected that a patientwith a clear prevalent fracture will have a VFR score close

to 1; namely that the chance of sustaining a future fracture

is trivially very high unless the already fractured vertebra is

excluded from the score calculation. The VFR for all

patients was computed in a leave-one-out procedure to

avoid bias and underestimation of the variance. The explicit

modeling of shape variations through bivariate normal

distributions as opposed to single thresholds on, say, height

ratios allowed for a fuller representation of both normal and

fracture-prone shape variation.

The remaining two scores are calculated using the same

overall methodology as the VFR score with the followingexceptions: For MDHR, a vertebra was represented by the

minimal height ratio and the most deformed representative

selected as described above. For the MDHaHp ratio, the

minimal height ratio was calculated based on the anterior and

posterior heights only but was otherwise identical to MDHR.

This means that both the MDHR and MDHaHp scores have

one-dimensional representations (ratios) and the fitted normal

distributions were thus univariate. Furthermore, the explicit

height normalization step from the VFR score was not needed

due to the implicit normalization through ratios.

All three scores were also tested in mean versions

(MVFR, MHR, and MHaHp) where a patient was repre-

sented as the mean of height pairs or ratios over vertebrae

T12-L5 instead of the single most deformed vertebra as

described above.

We compared these morphometric prognostic markers

based on analysis of the individual vertebrae with the

irregularity measure based on spine curvature suggested for

fracture prediction by Pettersen [25]. Any comparisons with

the Pettersens method are reported on the reduced dataset

presented in this paper for both ours and Pettersens method.

Method for high-risk clinical study screening

The high-risk population selection-mechanism outlined

below was aimed at maximizing the number of subjects

most likely to sustain first incident lumbar vertebral

fractures in a trial population selected from a larger

screening population. Population selection is only described

for the VFR score but could also be realized using any of

the other five proposed scores.

The subpopulation selection-mechanism consisted of

three steps: (1) scoring all subjects from the gross

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population using VFR, (2) sorting them in descending VFR

order, and (3) selecting the required number of subjects;

here we selected the 50% with the highest VFR score.

The selection was evaluated on the same study popula-

tion as the scoring methods outlined above.

Statistical analysis

Results are presented as mean SEM unless otherwise

specified. The scores of different groups of subjects were

compared using the non-parametric MannWhitney U test.

Differences were considered statistically significant if p

values were less than 0.05.

The ability to separate cases from controls was further

characterized through the area under the ROC curve

(AUC). Significance of differences between AUC was

tested with Delongs method [26]. Odds ratios (ORs) and

95% confidence intervals between highest and lowest

tertiles are reported for each method and the significance

of differences between ORs was tested using Taronesvariant of the BreslowDay test [27].

As a test of the BMD influence on the predictive value

of the VFR score, logistic regression with the VFR score

and BMD as independent variables was used.

The subpopulation selection result is reported as the

relative number of cases above the median of the VFR

scores across both the case and control datasets.

To assess the inter-annotator stability of suggested

VFR score, the 126 baseline radiographs were six-point

annotated (T12-L5) an additional two times by two

experienced X-ray technicians. We report mean SEM,

AUC, and ORs including significance levels for the two

repeat annotations where the VFR score was trained on

the original annotation. To assess the inter-observer

stability of the suggested high-risk population selection

methodology, we further report the percentage of cases in

the top half of the VFR ordered dataset for each repeat

annotation.

We report an overall simulated estimate of expected

performance for repeat annotations through an estimate of

the annotation scatter observed across the three annotators.

The repeat annotations yielded a total of 126664; 500 vertebrae points annotated by three trained observ-ers. These data were used to estimate the mean and standard

deviation for inter-observer annotation variability forx and

yannotation coordinates separately. The original full dataset

was subsequently perturbed with normally distributed

variations in the x and y directions according to the

estimated means and standard deviations. The main

experiment described above was repeated using the

perturbed dataset. This random perturbation and subsequent

experimentation was repeated 50 times. We report the

median AUC and 95% confidence intervals and the median

percentage of cases in the top half over the 50 perturbation

trials.

The percentage of cases where the most deformed vertebra

at baseline is one of the fractured vertebrae at follow-up is

reported. This number is supplemented by the percentage of

cases where this would be observed by chance.

Finally, we report results of the main experiment on the

full Pettersen [25] dataset and compare to results achievedon the reduced dataset (see page 7) used throughout.

All data were analyzed using Matlab (Mathworks, USA).

Results

Study population

The skeletal and demographic characteristics of the case

and control groups are presented in Table 1 where the main

statistics reported for each group is the median value;

mean SD is given in parentheses for completeness. Basedon BMD measurements both the case and control groups

contained approximately half normal (non-osteoporotic)

and half osteopenic patients at baseline; furthermore both

groups contained two osteoporotic patients at baseline.

Fracture prediction

The three suggested morphometric fractures prediction scores

based on the single most deformed vertebra VFR, MDHR,

and MDHaHp showed significant differences between case

and control patients at baseline: VFR (0.670.04 vs. 0.35

0.03; p

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Neither MDHR nor MDHaHp was significantly better

than any of the mean variants.

The highest versus lowest tertile ORs with 95%

confidence intervals for the three suggested methods (both

most deformed and mean variants), Pettersens irregularity

measure, and BMD are given in Fig. 4. The VFR ORs was

significantly more predictive than Irregularity or BMD

alone (p=0.03 and p =0.004).

Additional results are given for the VFR score only:

Figure5is a box and whisker plot of the VFR scores for the

case and control group at baseline. There was a significant

difference between baseline and follow-up VFR scores for

both the case and the control groups: case (0.670.04 vs.

0.990.01; p

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Across the 50 datasets perturbed by typical inter-

annotator variation, the median AUC with 95% confi-

dence intervals was 0.73 (0.610.82) and the median

percentage of cases in the top half of the total population

was 76%.

The most deformed vertebra at baseline was one of the

fractured vertebrae at follow-up for 32% of the case

subjects. With an average of 1.2 fractures (Table 1) per

case subject, this would happen for 21% of cases by

chance.

The AUC and ORs for the main experiment repeated on

the full Pettersen dataset [25] were: AUC, 0.84 (0.770.90);

p

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forces. In the lumbar spine, the convexity is produced

partly by differences in growth of the anteriorposterior

parts of the vertebrae [28]. This leads to slight differences

in the heights usually confined to an acceptable range.

Subsequent changes in vertebral heights can lead to

changes in the spinal curvature and to a redistribution of

forces upon the vertebrae endplates. A vertebra is expected

to fracture when the loads imposed are similar to or greaterthan its strength [29]. From this we expect that an un-

fractured lumbar vertebra that presents an abnormal change

of one or more of three vertebral heights, due to, for

example, osteoporosis, is more likely to fracture or cause

fractures than the one which keeps within the normal range

of shape variations.

Inspired by this, three morphometric computer-based

scores, each in two variants, were trained to predict lumbar

fractures in postmenopausal women through modeled

variability of measured anterior, middle, and posterior

vertebral heights in lumbar vertebrae. They were applied

in a casecontrol study matched for BMD and the othermajor risk factors for osteoporosis. All three methods based

on the most deformed vertebra were able to significantly

differentiate subjects who would sustain at least one lumbar

vertebral fracture from those who maintained vertebral

integrity over a 6.3-year period. Furthermore, the results

suggest that the variants based on the most deformed

vertebra produced better fracture predictions than the

variants based on means across vertebrae T12-L5. Specif-

ically, the most deformed VFR variant yields significantly

better prediction than the MVFR and the MHR and a

Delong p value of 0.1 for MHaHp. Conversely, neither of

the other two scores based on the most deformed vertebra

was significantly better than VFR or any of the mean

variants. This suggests that the VFR score, which is based

on two normalized height measurements and thus 2 df,

measures more diverse shape variations (Fig. 1b) than any

o f the s ug ge sted 1 df (ratio) scores. Furthermore,

morphometry-based lumbar fracture prediction using the

single most deformed vertebra is stronger than prediction

based on an average or summary across the lumbar spine.

Finally, VFR delivers significantly better fracture prediction

than the curvature-based irregularity measure suggested by

Pettersen [25]. Although, L5 was included in this analysis

to make a direct comparison with Pettersens work possible,

the suggested methods (including VFR) are directly

applicable to datasets where only T12-L4 are annotated in

lumbar radiographs. Experiments (not included) on the

current data-set indicated a slight but not statistically

significant performance drop if only T12-L4 were included.

This is supported by the relatively larger morphological and

annotation induced variation of L5; in the context of this

work most likely too large to consistently add significant

information.

The VFR score showed an increased risk for sustaining

a second fracture in the case-group through a significant

difference between cases at baseline and follow-up. This is

in accordance with the literature [30, 31] and our expect-

ations in designing the VFR score. There was also a

significant difference for control-subjects measured at

baseline and follow-up pointing to an overall increas e in

risk for an incident fracture. Although the control groupremained fracture free, this increase in risk is not

unexpected over a 6.3 years observation period of

postmenopausal women where half were osteopenic at

baseline.

Our findings, on selection of high-risk subpopulations

were that a clear majority of the case-patients was found in

the top half of the combined cases and controls group

sorted by VFR. This suggests VFR as a viable supplement

to BMD and standard risk factors to select fracture free but

likely to fracture subjects from a general screening

population.

The discriminative performances of the two repeatannotations were not as high as the original annotation

but still significant and well within the confidence intervals

of simulated performance using estimated annotator scatter.

The same was the case for high-risk subpopulation

selection performance for both the individual repeat

annotations and the simulation. These performance drops

are not unexpected as the repeat sets were scored using the

original set as training/reference data and not in a leave-

one-out fashion and are in this sense less biased. Further-

more, the two repeat sets were annotated by trained X-ray

technicians and not radiologists as was the case for the

original set. Indirectly, this is reflected by the observed

larger SEMs of VFR scores within the case group for the

repeat annotators compared with the original annotation.

That fracture prediction of the suggested kind is more

sensitive to annotation quality and variation than, say,

standard SQ or QM-based fracture classification is, as

mentioned, not surprising. The discriminative factors of

VFR are subtle pre-fracture shape changes that are smaller

than standard implicit or explicit SQ and QM thresholds

[6]. Based on this, we emphasize that high quality,

consistent expert annotations are important in achieving

good separation. This is especially the case if the methods

were applied to less matched populations as in, say, clinical

trial screening. Here, one would need to train the algorithm

on a similar (same inclusion criteria) but independent

reference population prior to screening of novel subjects.

In that scenario, we would not expect discriminative

performance significantly above the reported median

simulated performance.

The vertebrae that fractured were also the most deformed

vertebrae for 32% of the cases which is somewhat higher

than was expected by chance alone (21%). This suggests

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that a high VFR score indicates an overall lumbar fracture

risk in terms of, e.g., an uneven biomechanical load

distribution or overall systemic effects indirectly signaled

by the most deformed vertebra.

Finally, the validation of the experiment on the full

Pettersens population [25] confirmed that the performed

population reduction to avoid predictive performance based

on borderline disagreement between SQ and QM did notlead to improved results as expected.

Limitations of the study

The subjects used in this study were all community-

recruited postmenopausal women roughly equally split

between normal and osteopenic. The case group was

pre-selected from a gross population of 4,062 as fracture

free at baseline and with at least one lumbar only

fracture at follow-up. The control group was matched

with respect to traditional osteoporosis risk factors and

maintained skeletal integrity throughout. Any patientswith non-osteoporotic deformities and/or fractures were

excluded whether these were real or caused by projec-

tion errors. The main results of this paper, although

promis ing, mus t be valida ted in future studies to

establish applicability on populations with fewer restric-

tions than described above.

This study focused on fracture risk in the lumbar region

(including T12). This was primarily done to facilitate easy

comparison with the related results in the Pettersens paper

[25] but also in recognition of the fact that pre-fracture

shape changes are more pronounced in the relatively larger

lumbar vertebrae. A natural generalization in future studies

will be to test if pre-fracture shape changes in thoracic

vertebrae also have fracture-predictive value. Furthermore,

thoracic and lumbar vertebrae are very likely to give a

better combined prediction of both lumbar and overall

fracture risk. It is our expectation that a single linear model

as presented in this paper would be insufficient to model

the range of combined lumbar and thoracic morphological

variance. We, however, believe that two such models

trained on thoracic and lumbar vertebrae respectively, in

combination would be sufficient.

For the purposes of the pre-selection and the study in

general, fracture absence and presence was established

using Genants semi-quantitative method [6] by both a

radiologist and subsequently by a computer algorithm using

manually measure vertebral heights with a strict fracture

threshold 0.2. In future studies, it would be relevant to

establish how robust the proposed fracture risk score are

with respect to changing the ground truth fracture

assessment methodology to other established methods as

suggested by, e.g., Eastell, Melton, Minne, and McCloskey

[13,18,19,23].

Conclusion

We have presented three morphometric scores each in

t wo va ri an ts . I n a c as ec on tro l s tud y b as e d o n

community-recruited postmenopausal women with the

limitations iterated above, all six variants were able to

pre dic t first inc ide nt lumba r fract ures. In gen eral,

variants based on only measuring the single mostdeformed vertebra showed more promise than the mean

variants. More specifically, the 2 df VFR score is

significantly better than two other mean-based variants

but not significantly better that the other two suggested

single vertebra morphometric methods. Subject to the

limitations iterated above and the availability of high-

quality radiologist six-point annotations, we conclude that

relative vertebral heights or height ratios measured on the

single most deformed vertebra used in combination with

machine learning techniques appears a promising ap-

proach for (1) first incident lumbar fracture prediction

and (2) selection of fracture-prone populations for clinicaltrials investigating treatment and prevention of osteopo-

rosis and osteoporotic fractures.

It is, however also, clear that VFR-based fracture

prediction is highly operator/annotator dependent; this and

a generalization to both lumbar and thoracic fracture

prediction will be the focus of future studies.

Acknowledgements The authors gratefully acknowledge the fund-

ing from the Danish Research Foundation (Den Danske Forsknings-

fond) supporting this work. The authors thank Jane Petersen and

Annette Olesen for the repeat annotations.

Conflicts of interest The VFR-methodology is part of a pendingpatent. Martin Lillholm is an employee of Synarc Imaging Techonol-

ogies/Nordic Bioscience Imaging (SIT/NBI). Anarta Ghosh is a former

employee of SIT/NBI. Paola C Pettersen is an employee of Center for

Clinical and Basic Research (CCBR). Erik B Dam is an employee of

SIT/NBI. Morten A Karsdal is an employee and shareholder of Nordic

Bioscience (NB). Claus Christiansen is an employee and shareholder of

NB and CCBR. Harry K Genant is an employee and shareholder of

Synarc. Mads Nielsen is partly funded by SIT/NBI. Marleen de Bruijne

was previously funded by Nordic Bioscience.

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