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Dentomaxillofacial Radiology (2006) 35, 1-9
© 2006 British Institute of Radiology
doi: 10.1259/dmfr/97652136

RESEARCH

The differences in panoramic mandibular indices and fractal dimension between patients with and without spinal osteoporosis

F Yasar* and F Akgünlü

Selcuk Universitesi Dis Hekimligi Fakultesi, Oral Diagnoz ve Radyoloji Anabilim Dali, 42079 Konya Turkey

*Correspondence to: Füsun Yasar, Selcuk Universitesi Dis Hekimligi Fakultesi, Oral Diagnoz ve Radyoloji Anabilim Dali, 42079 Konya Turkey; E-mail: drfyasar{at}hotmail.com

Received 1 October 2004; revised 12 April 2005; accepted 12 May 2005


   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Results
 Discussion
 References
 
Objectives: The aims of this study were to evaluate the relationship between osteoporosis, oral signs, body mass index and age; and to assess the possibility of using these parameters as an indicator of post-menopausal osteoporosis. The oral signs were panoramic-based mandibular indices, such as cortical width, cortical index, panoramic mandibular index and mandibular crest resorption degree; the number of teeth and fractal dimension analysis.

Methods: Forty-eight post-menopausal women between the ages of 40 years and 64 years were evaluated. Mandibular indices and the number of mandibular teeth were measured and evaluated from panoramic radiographs and fractal dimension was calculated from the direct digital periapical radiographs of the mandibular premolar-molar region in box-counting method. Weight, height, menopausal status and age of the patients were recorded by questionnaire. Bone mineral densities of the patients were measured with dual energy X-ray absorptiometry.

Results: In this study there were no statistically significant differences between the osteoporotic and non-osteoporotic patients for cortical width, panoramic mandibular index, alveolar crest resorption degree, fractal dimension and the number of mandibular teeth, but there was a difference for cortical index. Binary logistic regression analyses demonstrated that age (P=0.015) was an important risk factor for osteoporosis.

Conclusion: The results of this study demonstrated that osteoporotic patients were more likely to have altered inferior cortex morphology than non-osteoporotic patients and age is an important risk factor for osteoporosis.

Keywords: osteoporosis;; panoramic radiography;; digital radiography;; fractal dimension


   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Results
 Discussion
 References
 
Osteoporosis is a condition characterized by a loss in bone mineral density and micro-architectural deterioration in bone tissue leading to fractures.1,2 Since the disease is preventable, diagnostic techniques are of major importance.2 The earliest suggestion of an association between osteoporosis and oral bone loss was made in 1960.3 The dentist is often the most regularly visited doctor in the elderly population, and dental radiographs are the most frequently used imaging modalities for these patients. Various studies have demonstrated that individuals with osteoporosis have altered morphology of the mandible.4,5 A number of mandibular indices based on panoramic radiographs, and image processing and analysing techniques have been developed to allow quantification of mandibular bone mass and trabecular architecture in order to discriminate individuals with osteoporosis from those without osteoporosis. Cortical width (CW),6 panoramic mandibular index (PMI),7 alveolar crest resorption degree (M/M) ratio,8 cortical index (CI)9 and fractal dimension (FD)10,11 are among them. In various studies, it has been shown that the decreased bone mineral density (BMD) affects the morphometric,8,9,12 densitometric13 and architectural properties14–16 of mandibular bone in the osteoporotic patients on radiographs.

Fractal analysis is a mathematical technique that can aid in the quantification of complex structures.17 In general, the higher the dimension, the more complex the shape.17,18 This technique has been evaluated with varying degrees of success in different imaging modalities such as plain film radiography,10,19 mammography,20,21 CT and MRI.22 Bone is a natural material which is designed to resist gravitational and mechanical forces and thus, has an internal structure that is highly oriented (anisotropic).23 Various investigators have evaluated this anisotropic structure of trabecular bone on dental radiographs, by using different FD methods in order to discriminate individuals with osteoporosis from those without osteoporosis. While some reported a decrease in FD,11 others reported an increase in FD in osteoporotic patients.15,16

The aims of this study were to evaluate the relationship between various oral signs and osteoporosis and to develop an equation that might be applied in assessing the probability of this relationship existing in certain patients.


   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Results
 Discussion
 References
 
The study was based on 48 post-menopausal Caucasian women varying in age from 40 years to 64 years. The patients were all in natural menopause and none had known systemic disease that would affect bone metabolism (hyperparathyroidism, hypoparathyroidism, Paget's disease, osteomalacia, renal osteodystrophy or osteogenesis imperfecta), cancers with bone metastasis or significant renal impairment. They were not using specific drugs or hormones (corticosteroids, excess thyroid hormone) which are known to have adverse effects on bone metabolism. They were non-smokers and did not drink alcohol.

For the examination of the bone mineral density status of the patients, anteroposterior dual X-ray absorptiometry (DXA) scans were performed using a Hologic QDR-4500 bone densitometer (Hologic Inc., Waltham, MA). The patients were classified as normal (T-score>–1.0) or osteoporotic (T-score of <–2.5) based on the BMD data of the lumbar spine.24 Osteopenic patients were not included the study. In order to determine the presence of thoracic spine fractures, lateral chest radiographs were evaluated by a radiologist according to the radiographic criteria of Pacifici et al.25

The spinal height of the anterior, central, and posterior third at each vertebra from the 5th thoracic spine to 12th thoracic spine was measured with a vernier calliper. Compression fracture was defined as a loss of posterior height greater than 15% compared with the mean of the posterior height of the nearest (inferior and superior) intact vertebrae. When the posterior height was within the normal range, wedging and biconcave fractures were defined as a loss of anterior and central height greater than 20% compared with the posterior height of the same vertebra. The patients who did not have thoracic fractures were included the study.

The data regarding menopausal status, age, weight and height were obtained by questionnaire. The patients did not have menstruation for at least the last year. The body mass index (BMI) was computed as weight divided by the square of height (BMI; kg m–2). The number of mandibular teeth, excluding third molars was recorded from panoramic radiographs. Root remnants were counted as teeth except the ones which were totally imbedded in bone. The time and cause of tooth loss were unknown.

One panoramic radiograph (2002 CC Proline; PlanMeca, Helsinki, Finland) and two direct digital periapical radiographs in bisecting angle technique were exposed from the mandibular premolar-molar region. An intraoral X-ray unit (Trophy CCX Digital Periapical X-ray machine, France) and Dimaxis (1.52) (PlanMeca, Helsinki, Finland) intraoral computed radiography system were used to capture the images. Direct digital radiographs were exposed with 65 kV, 10 mA and 2.5 mm equivalent total filtration. Direct digital and panoramic radiographs were clinically acceptable.

ImageJ (1.28)26 was used for image processing and analysis.

Morphometric measurements
All measurements were made in millimetres by the same investigator with a calliper and plastic millimetre ruler. The same viewing box, magnifier (x2), calliper and ruler were used all in the measurements. When the mental foramen was visible bilaterally, the measurements were done bilaterally and their mean was used as the exposure measure in the analysis; when only one foramen was visible, the measurements were done only on that side. To evaluate the intraexaminer reliability, 48 panoramic radiographs were re-analysed for CW, PMI, M/M ratio, CI and the number of mandibular teeth with a period of 6 months interval by the same examiner. The information on age and BMD status of the patients was blinded to the examiner in order to eliminate information bias.

The inferior edge of the mental foramen was traced, and a line parallel to the long axis of the mandible and tangential to the inferior border of the mandible was drawn. A line perpendicular to this tangent intersecting the inferior border of the mental foramen was constructed. CW, PMI and M/M ratio were measured along this line (Figure 1Go).


Figure 1
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  		Figure 1 Measurements on the panoramic radiograph. CW: a; PMI: a/b; M/M ratio: d/c

 
CW is the thickness of the mandibular cortex on this line (a).6 It was measured with a calliper and plastic ruler on the right and left side of the panoramic radiograph and the mean of the measurements was taken as the CW of the patient.

PMI is the ratio of the thickness of the mandibular cortex to the distance between the mental foramen and the inferior mandibular cortex.7 The thickness of the cortex was divided by the distance from the inferior margin of the mental foramen to the inferior border of the mandible (a/b).

The total mandibular height was divided by the height from the centre of the mental foramen to the inferior border of the mandible to obtain the M/M ratio. This was an index of the mandibular alveolar bone resorption degree (M/M ratio) (d/c).8

In order to evaluate the CI of the mandible, the morphology of mandibular inferior cortex was visually examined distally from the mental foramen bilaterally using Klemetti's classification.9

Fractal dimension analysis
First, the 16-bit direct digital radiographs were converted to 8-bit images with ImageJ (1.28).26 Rectangular regions of interest (ROI), whose dimensions were the same (23 x 155 pixels) for all radiographs, were created between second premolar and first molar teeth (Figure 2aGo). ROIs were created distally to mental foramen in edentate patients (Figure 2bGo). When creating ROIs, great care was shown not to include lamina dura, periodontal ligament space and anatomical structures and they were created apically as far as possible from the crestal bone, because it has been stated that beyond 2.5 mm there is no effect of bacterial plaque on alveolar process bone.27 Digital images were segmented to binary image in a similar way described by White and Rudolph.5,14 To remove large scale variations in brightness on the image, the ROI was blurred through use of a Gaussian filter with radius of 35 pixels (Figure 2cGo). The causes of these variations are differences in thickness of the object or the presence of partially overlapping soft tissue. This step removed all fine-scale and medium-scale structure and retained only large variations in density (low-pass filtering). The resulting heavily blurred image was then subtracted from the original, and 128 was added to the result at each pixel location. This generated an image with a mean value of 128, regardless of the initial intensity of the image. The idea was that individual variations in this image (brightness levels) then reflect particular types of features, in this case trabeculae and marrow spaces. The image was then made binary, thresholding on a brightness value of 128 and thus segmenting the image into components that visually (radiographically) approximate the trabeculae and marrow (Figure 2dGo). The image of the trabeculae was then inverted to make the trabeculae black and then boundary description was applied to the binary image and outlines of the trabeculae were obtained (Figure 2eGo). FD was calculated in box counting method with ImageJ (1.28).26 As two direct digital images were exposed from both right and left sides of the mandible in the premolar-molar region, two separate FD values were calculated. The mean of these two values was used in the study.


Figure 2
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  		Figure 2 The original radiograph with a region of interest (ROI). (a) The selected ROI. (b) The result of blurring this region. (c) The result of subtracting B from A and adding 128. (d) Binary version of the image. (e) The trabecular pattern (white region in image 2d) is outlined. (f) The addition of images A and E to demonstrate visually that the outlined image corresponds to the original trabeculae

 
Statistics
The strength of the relationship between repeated measurements for CW, PMI, M/M ratio, and the number of teeth was assessed by calculation of Pearson's Correlation Coefficients and the repeatability was examined using the method of Bland and Altman.28 Weighted Kappa ({kappa}) index was used as a measure of intraexaminer agreement for CI evaluation and chi-squared test was performed to evaluate the relation between osteoporosis and CI. Kolmogorov-Smirnov and Levene's tests were used, respectively, to check for the normality and homogeneity of the data. Independent samples t-test was used to evaluate the differences of oral signs (CW, PMI, M/M ratio, the number of mandibular teeth present and FD, age and BMI), in patients with osteoporosis from those without osteoporosis. A probability equation for osteoporosis was calculated by applying binary logistic regression analysis to the age, oral signs and BMI. SPSS 10.0 for Windows (SPSS, Inc., Chicago, IL) was used in the statistical analysis.


   Results
Top
 Abstract
 Introduction
 Materials and methods
 Results
Discussion
 References
 
According to the DXA measurements made from lumbar vertebrae, 27 of the patients were osteoporotic (56.25%) and 21 were non-osteoporotic (43.75%), based on their T-score as described earlier.24

Eight different variables were evaluated in this study for their relation with osteoporosis. Seven of the variables were continuous (CW, PMI, M/M ratio, FD, the number of mandibular teeth present, age and BMI) and one of them, CI, was categorical data. Table 1Go shows the mean values and standard deviations of the continuous variables for the osteoporotic and non-osteoporotic groups. Normal distribution of the data was controlled with Kolmogorov-Smirnov test (Table 1Go) and homogeneity of variances was checked with the Levene's Test for equality of variances (Table 2Go). Independent samples t-test was used in comparing the differences of CW, PMI, M/M ratio, FD, the number of teeth, age and BMI for each BMD category (osteoporotic and non-osteoporotic) (Table 2Go). CW, PMI, M/M ratio, and the number of mandibular teeth were evaluated twice by one of the investigators, and the mean of the two evaluations was used in the independent samples t-test. Bonferroni criteria with significance set at P<0.007 level was used in the independent samples t-test (Table 2Go). None of the parameters evaluated were found to differ significantly between the osteoporotic and non-osteoporotic group.


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  		Table 1 Means, standard deviations and Kolmogorov-Simirnov test results for osteoporotic and non-osteoporotic patients

 

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  		Table 2 Levene's Test for equality of variances and independent samples t-test (P<0.007)

 
Pearson correlation coefficients were used to assess the repeatability of CW, PMI, M/M ratio and the number of mandibular teeth (Table 3Go). The strength of the relationship of panoramic measurements (CW, PMI, and M/M ratio) and number of mandibular teeth was high, as indicated by the correlation coefficients. The highest correlation coefficient was found for the number of teeth (r=1; P<0.01) and the lowest correlation coefficient was found for M/M ratio (r=.864; P<0.01). For each patient, the mean differences between the two measurements of CW, PMI and M/M ratio were plotted as a scatter diagram against the average of the two measurements (Bland and Altman Plot). As the counted number of teeth present was same in both observations (r=1), no scatter diagram was plotted for it. The mean difference, limits of agreement, coefficient of repeatability and precision and Pearson's correlation coefficients of the variables are shown in Table 3Go. There were four outliers for PMI and two outliers for M/M ratio. Outliers were included in the study.


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  		Table 3 Repeatability and reliability

 
FD calculations were performed on the selected ROIs only once, as the aim of this study was not to evaluate the effect of varying the ROI on calculations of fractal index. When the same image processing and analysis techniques are used for the same ROI in repeated analysis, the FD will always have the same value.

Weighted Kappa statistic was used to evaluate the agreement of CI for both observations (Table 4Go). Weighted {kappa} was found as 0.85 and there was almost perfect agreement between the observations for 99% confidence interval. Interpretation of the Kappa statistics was quoted from the guidelines of Landis and Koch: Less than 0.00 poor agreement, 0.00–0.20 slight agreement, 0.21–0.40 fair agreement, 0.41–0.60 moderate agreement, 0.61–0.80 substantial agreement and 0.81–1 almost perfect agreement.29 The frequency distribution of osteoporosis for CI classification in two observations and the Pearson chi-squared test results are shown in Table 5Go (P=0.002 for the first observation and P=0.001 for the second observation with 99% confidence interval). There was a statistically significant difference between the CI classifications in osteoporotic and non-osteoporotic patients in both observations. As there was a high agreement between the first and second observations of CI, the two tables were combined (Table 6Go). Later, risk ratio for osteoporosis was calculated for three CI classifications. There was a significant difference in all CI categories with 99% confidence interval (Table 7Go). The risk of osteoporosis in CI 3 category was 1/0.079 (12.6) times more than CI 2, the risk of osteoporosis in CI 2 category was 1/0.083 (12) times more than CI 1, and the risk of osteoporosis in CI 3 category was 1/0.007 (142.8) times more than CI 1 (Table 8Go).


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  		Table 4 Cross-table for the cortical index (CI) classification and Kappa statistic's results

 

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  		Table 5 The frequency distribution of osteoporosis for cortical index (CI) classification in two observations and the Pearson Chi-squared test results

 

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  		Table 6 The combined table of the first and second observations frequency distribution of osteoporosis for cortical index (CI) classification and the Pearson Chi-squared test results

 

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  		Table 7 Pearson chi-squared P-values displaying the differences between cortical index (CI) categories

 

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  		Table 8 Risk ratio table

 
As our interest was to model the predictors for osteoporosis, a categorical dichotomous outcome, then the appropriate multivariate analysis was binary logistic regression.30 Binary logistic regression analysis was used to measure the validity of mandibular cortical indices, FD, BMI, age and the number of teeth in the diagnosis of reduced skeletal BMD and osteoporosis. Patients were classified as normal (T-score>–1.0) or osteoporotic (T-score< –2.5) according to the results of DXA scans. Osteoporosis was the dependent variable and CW, PMI, M/M ratio, FD, the number of mandibular teeth, age and BMI were the covariates. Osteoporotic status was defined as 0 and non-osteoporotic status was defined as 1. Estimates of the logistic regression are shown in Table 9Go. There was one outlier and it was excluded from the binary logistic regression analysis (Figure 3Go). The Wald estimates give the importance of the contribution of each variable in the model.30 The higher the value, the more important it is. Among the parameters evaluated, only age was found to be an important risk factor (P=0.0152) for osteoporosis. Binary logistic regression analysis used age to generate the following equation for the probability of having osteoporosis for a new subject.


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  		Table 9 Binary logistic regression table

 

Figure 3
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  		Figure 3 Simple box plot showing an outlier for M/M ratio

 
Z=14.75–0.2 (age)

Pv=1/(1+e–z)

Pv is the probability value. The numerical value was obtained from the B estimates from Table 9Go. Frequently our interest is to use the logistic model to predict the outcome for a new subject. When the age of a new patient is written in the equation, a Z value can be obtained and then P can be calculated. If P<0.50 then Pv =0 and it can be said that it is very likely that this person is osteoporotic and if P≥0.50; Pv =1 and the subject is more likely to be non-osteoporotic. The overall accuracy of this model to predict subjects having osteoporosis was 99% (Table 10Go).


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  		Table 10 The overall accuracy of the model to predict subjects having osteoporosis (confidence interval: 99%, P=0.05)

 

   Discussion
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
References
 
The aims of this study were to evaluate the relationship between various oral signs and osteoporosis and to develop an equation that might be applied in assessing the probability of osteoporosis in certain patients. There were no statistically significant differences between the osteoporotic and non-osteoporotic groups for CW, PMI, M/M ratio, number of teeth present, FD, age and BMI. BMD of the lumbar spine was used as the gold standard in defining the osteoporotic patients. Although the only real gold standard measure of BMD would be by bone biopsy, it is likely that dual X-ray absorptiometry (DXA) offers the best means of obtaining accurate information in vivo.31 DXA itself is subject to some inaccuracy in BMD measurements but this is small.13 Precision of DXA ranges from 0.5% to 5%.32

Post-menopausal women constitute more than 15% of the population in developed countries whereas this rate is 5–8% in less developed regions of the world.33 By 2030, the world population of menopausal and post-menopausal women is expected to increase to 1.2 billion, with 47 million new cases each year.33,34 Osteoporosis and fractures are more difficult and costly to treat than to prevent. Therefore, several health care interventions have been proposed to identify those people who may be at risk and who could benefit from preventive interventions.35

CW, PMI, M/M ratio, CI, the number of teeth present and FD are the parameters evaluated in various studies for detecting their effectiveness in screening osteoporosis. Some of the investigators reported that they could be used in screening osteoporosis8–10,14,16,36–38 but others reported that there was no relation between these parameters and osteoporosis.39,40 Kingsmill and Boyde41 studied the variability in the anatomy of mandibles of differing ages and differing stages of tooth loss. They studied the cross sectional slices of the dry mandibles and measured CW from the radiographs of those slices. They concluded that unlike other bones, the mandible may show an increase in apparent density with age, implying that the mandible may not be suitable for evaluating osteoporotic status and they found no relationship between radiographic cortical thickness and age. Taguchi et al42 studied the mandibular bone density of women who were in different post-menopausal stages and reported that BMD of the mandibular cortical bone had greater correlation (r=0.73) with lumbar trabecular bone mineral density in recent post-menopausal group than in long-term post-menopausal group (r=0.46). Kingsmill and Boyde's result about density is supporting the results of Taguchi et al.42 In this study we were unable to show any relation between osteoporosis and the oral signs such as CW, PMI, M/M ratio, number of teeth present and FD.

The age at menopause is between 45 years and 55 years all over the world. Cessation of menses between age 40 years and 45 years has been defined as early menopause.43,44 13 patients (27.08%) participating this study were below 45 years. The mean age of the patients participating in this study was relatively young (osteoporotic patients: 52.11 years, non-osteoporotic patients: 48.38 years). Besides this, none of the patients in this study had osteoporotic fractures. These two factors might be the limitations of the study and also they might be the reason of not finding an association between the oral signs, except CI, and osteoporosis. The other reason might be the BMI of the women. Unchangeable causes of increased bone loss are increasing age, family history of osteoporosis, being female, being in menopause, and being thin.35 The World Health Organization45 classified BMI as follows:

Normal weight 18.5≥ BMI< 25

overweight 25≥ BMI<30

obese BMI ≥ 30

It was reported that BMI significantly correlates with BMD and people with a BMI of 20–25 kg m–2 have a higher rate of bone loss than those who are heavier.35 The mean BMI of the women included in this study was classified as overweight according to WHO criteria (mean BMI: 27.78; SD: 4.32).

Various investigators reported different levels of repeatability for CW, PMI, M/M ratio and CI.46,47 Generally authors used correlation coefficients in describing agreement between two quantitative variables but high correlation does not imply good agreement.48 Bland and Altman introduced the Bland and Altman plot to describe agreement between two quantitative measurements.48 There is no P-value available to describe this agreement but rather a quality control concept. The difference of the paired two measurements is plotted against the mean of the two measurements and they recommended that 95% of the data points should lie within the ±1.96 (approximately ±2) standard deviation of the mean difference. In this study, the strength of the relationship between repeated measurements was assessed by Pearson's correlation coefficient and the repeatability was assessed with the method of Bland and Altman.28 Making measurements from panoramic radiographs is associated with intrinsic errors and observer variability. There is a limitation in the repeatability of panoramic radiographic measurements.49,50 The number of mandibular teeth had the highest repeatability (r=1; SD: 0,000), M/M ratio (r=0.864; coefficient of repeatability: 0.65), PMI (r=0.871; coefficient of repeatability: 0.12) were following it in decreasing order and cortical width (r=0.869; coefficient of repeatability: 1.17) had the worst repeatability. Obtaining high agreement in measuring gross structures, such as counting teeth, is easier than obtaining high agreement in measuring in millimetres, because in these measurements, the visual perception of human eye and brain should also be taken into consideration. The linear measurements have greater observer dependency and another factor is to measure X-ray films by hand to better than the nearest 0.5 mm is not justifiable.41 To obtain the same results in repeated observations, the borders of the mental foramen should be determined correctly and later the necessary drawings should be made and finally these configurations should coincide with each other. That is, as the operations that must be performed for measuring an index increases, the measurement error would also increase; so it might be more effective to use simple indexes to reduce measurement error and increase agreement between the repeated measurements. The repeatability of CI was evaluated by weighted Kappa statistic. Kappa does not take into account the degree of disagreement between observers and all disagreement is treated equally as total disagreement. Therefore when the categories are ordered, it is preferable to use weighted Kappa.29 There was a very good agreement (0.851) between the two observations. This finding was unsurprising because CI is relatively simple index and it requires no measurements.

One of the most commonly studied parameters of mandibular bone in relation to osteoporosis is the porosity of the mandibular cortical bone (CI). Some of the investigators have found an association between CI and osteoporosis,9,37,38,46,47 but Drozdzowska et al40 reported that there was no relationship between osteoporosis and CI. In this study, the best performing parameter in discriminating the osteoporotic patients from non-osteoporotics was CI with a very good agreement in repeatability (0.851). The risk of osteoporosis in CI 3 category was 12.6 times more than CI 2, the risk of osteoporosis in CI 2 category was 12 times more than CI 1 and the risk of osteoporosis in CI 3 category was 142.8 times more than CI 1 with 95% confidence interval. For the other parameters, some measurements must be performed and some calculations are necessary, and as it was stated before, the complexity of an index may increase its measurement error and decrease its repeatability. CI is a simple classification based on the appearance of the lower border of mandibular cortex.13 In this study, CI showed a statistically significant difference in the osteoporotic and non-osteoporotic patients even in the younger age range and in non-fractured patients.

The structure of the trabecular bone has been analysed in relation to osteoporosis with FD in many studies and it has provided conflicting results. While some authors stated that FD decreases in osteoporosis,22,51 others reported an increase.10,15 Generally digitized forms of conventional radiographs were used in the other studies but in this study direct digital images were used. Each imaging modality may have its own non-linear artefacts such as sampling frequency and noise and this may have an affect on the results of FD.23 Geraets and van der Stelt23 have stated that most reports based on radiographs in vivo find an association between osteoporosis and increased values of FD.10,15 In this study, the mean FD in osteoporotic patients was 1.40 and it was 1.38 in non-osteoporotic patients but there was no statistically significant difference between the two groups. Relatively young mean age and high BMI might have an effect on this. The other reasons might be the method and the region that was used to calculate FD. In this study, FD was calculated from mandibular bone but BMD was measured from the lumbar spine of the patients. Maybe it would be more accurate to compare the FD and the BMD of the same bone. Osteoporosis might not be affecting all bones at the same time with the same severity.

In conclusion, the results of this study demonstrated that CW, PMI, M/M ratio, number of mandibular teeth present and FD do not lend themselves to the diagnosis of osteoporosis. CI, which has a relatively easier application, is a useful oral sign in screening the patients for osteoporosis and it might be used as a subsidiary diagnostic tool in referring the patients to bone densitometry clinics.


   References
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

  1. Concensus Development Conference, Prophylaxis and treatment of osteoporosis. Am J Med 1991; 90: 107–110.[CrossRef][Medline]
  2. Link TM, Majumdar S, Grammp S, Guglielmi G, van Kuijk C, Imhof H, et al. Imaging of trabecular bone structure in osteoporosis. Eur Radiol 1999; 9: 1781–1788.[CrossRef][Medline]
  3. Groen JJ, Duyvensz F, Halsted JA. Diffuse alveolar atrophy of the jaw (non-inflammatory form of paradental disease) and presenile osteoporosis. Geront Clin 1960; 2: 68–86.
  4. Hildebolt CF. Osteoporosis and oral bone loss. Dentomaxillofac Radiol 1997; 26: 3–15.[Abstract]
  5. White SC. Oral radiographic predictors of osteoporosis. Dentomaxillofac Radiol 2002; 31: 84–92.[Abstract]
  6. Taguchi A, Tanimoto K, Suei Y, Otani K, Wada T. Tooth loss and mandibular osteopenia. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 1995; 79: 127–132.[CrossRef][Medline]
  7. Benson BW, Prihoda TJ, Glass BJ. Variations in adult cortical bone mass as measured by a panoramic mandibular index. Oral Surg Oral Med Oral Pathol 1991; 71: 349–356.[CrossRef][Medline]
  8. Taguchi A, Tanimoto K, Suei Y, Otani K, Wada T. Oral signs as indicators of possible osteoporosis in elderly women. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 1995; 80: 612–616.[CrossRef][Medline]
  9. Klemetti E, Kolmakow S. Morphology of the mandibular cortex on panoramic radiographs as an indicator of bone quality. Dentomaxillofac Radiol 1997; 26: 22–25.[Abstract]
  10. Pothuaud L, Lespesailles E, Harba R, Jennane R, Royant V, Eynard E, et al. Fractal analysis of trabecular bone texture on radiographs: discriminant value in postmenopausal osteoporosis. Osteoporosis Int 1998; 8: 618–625.[CrossRef][Medline]
  11. Southard TE, Southard KA, Jakobsen JR, Hillis SL, Najim CA. Fractal dimension in radiographic analysis of alveolar process bone. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 1996; 82: 569–576.[CrossRef][Medline]
  12. Kribbs PJ, Chesnut 3rd CH, Ott SM, Kilcoyne RF. Relationships between mandibular and skeletal bone in an osteoporotic population. J Prosthet Dent 1989; 62: 703–707.[CrossRef][Medline]
  13. Horner K, Devlin H. The relationships between two indices of mandibular bone quality and bone mineral density measured by dual energy X-ray absorptiometry. Dentomaxillofac Radiol 1998; 27: 17–21.[Abstract]
  14. White SC, Rudolph DJ. Alterations of the trabecular pattern of the jaws in patients with osteoporosis. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 1999; 88: 628–635.[CrossRef][Medline]
  15. Ruttimann UE, Webber RL, Hazelrig JB. Fractal dimension from radiographs of peridental alveolar bone: a possible diagnostic indicator of osteoporosis. Oral Surg Oral Med Oral Pathol 1992; 74: 98–110.[CrossRef][Medline]
  16. Law AN, Bollen AM, Chen SK. Detecting osteoporosis using dental radiographs: a comparison of four methods. JADA 1996; 127: 1734–1742.
  17. Peitgen HO, Jurgens H, Saupe D. Chaos and fractals. New York: Springer-Verlag; 1992.
  18. Caligiuri P, Giger ML, Favus M. Multifractal radiographic analysis of osteoporosis. Med Phys 1994; 21: 503–508.[CrossRef][Medline]
  19. Chen J, Zheng B, Chang YH, Shaw CC, Towers JD, Gur D. Fractal analysis of trabecular patterns in projection radiographs: an assessment. Invest Radiol 1994; 29: 624–629.[CrossRef][Medline]
  20. Velanovich V. Fractal analysis of mammographic lesions: a feasibility study quantifying the difference between benign and malignant masses. Am J Med Sci 1996; 311: 211–214.[CrossRef][Medline]
  21. Velanovich V. Fractal analysis of mammographic lesions: a prospective, blind trial. Breast Cancer Res Treat 1998; 49: 245–249.[CrossRef][Medline]
  22. Link TM, Majumdar S, Lin JC, Newitt D, Augat P, Ouyang X, et al. A comparative study of trabecular bone properties in the spine and femur using high resolution MRI and CT. J Bone Miner Res 1998; 13: 122–132.[CrossRef][Medline]
  23. Geraets WG, van der Stelt PF. Fractal properties of bone. Dentomaxillofac Radiol 2000; 29: 144–153.[Abstract]
  24. World Health Organization, Assessment of fracture risk and application to screening for postmenopausal osteoporosis. WHO Technical Report Series, 843. Geneva, Switzerland: WHO, 1994.
  25. Pacifici R, Susman N, Carr PL, Birge SJ, Avioli LV. Single and dual energy tomographic analysis of spinal trabecular bone: a comparative study in normal and osteoporotic women. J Clin Endocrinol Metab 1987; 64: 209–214.[Abstract/Free Full Text]
  26. http://rsb.info.nih.gov/ij/ [Accessed 26 September 2005].
  27. Southard KA, Southard TE, Schlechte JA, Meis PA. The relationship between the density of the alveolar process and that of post-cranial bone. J Dent Res 2000; 79: 964–969.[Abstract/Free Full Text]
  28. Bland JM, Altman DG. Applying the right statistics: analyses of measurement studies. Ultrasound Obstet Gynecol 2003; 22: 85–93.[Medline]
  29. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33: 159–174.[CrossRef][Medline]
  30. Chan YH. Biostatistics 202: Logistic regression analysis. Singapore Med J 2004; 45: 149–153.[Medline]
  31. Horner K, Devlin H. The relationship between mandibular bone mineral density and panoramic radiographic measurements. J Dent 1998; 26: 337–343.[CrossRef][Medline]
  32. Adams JE. Osteoporosis and bone mineral densitometry. Curr Opin Radiol 1992; 4: 11–20.[Medline]
  33. World Health Organization, Research on the menopause in the 1990s, report of A WHO Scientific Group. WHO technical report series 866. Geneva: World Health Organization; 1996.
  34. Hill K. The demography of menopause. Maturitas 1996; 23: 113–127.
  35. Espallargues M, Sampietro-Colom L, Estrada MD, Sola M, del Rio L, Sertoain J, et al. Identifying bone-mass-related risk factors for fracture to guide bone densitometry measurements: a systematic review of the literature. Osteoporos Int 2001; 12: 811–822.[CrossRef][Medline]
  36. Devlin H, Horner K. Mandibular radiomorphometric indices in the diagnosis of reduced skeletal bone mineral density. Osteoporos Int 2002; 13: 373–378.[CrossRef][Medline]
  37. Bollen AM, Taguchi A, Hujoel PP, Hollender LG. Case-control study on self reported osteoporotic fractures and mandibular cortical bone. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2000; 90: 518–524.[Medline]
  38. White SC, Taguchi A, Kao D, Wul S, Service SK, Yoon D, et al. Clinical and panoramic predictors of femur bone mineral density. Osteoporos Int 2005; 16: 339–346.[CrossRef][Medline]
  39. Mohajery M, Brooks SL. Oral radiographs in the detection of early signs of osteoporosis. Oral Surg Oral Med Oral Pathol 1992; 73: 112–117.[CrossRef][Medline]
  40. Drozdzowska B, Pluskiewicz W, Tarnawska B. Panoramic based mandibular indices in relation to mandibular bone mineral density and skeletal status assessed by dual energy X-ray absorptiometry and quantitative ultrasound. Dentomaxillofac Radiol 2002; 31: 361–367.[Abstract]
  41. Kingsmill VJ, Boyde A. Variation in the apparent density of human mandibular bone with age and dental status. J Anat 1998; 192: 233–244.
  42. Taguchi A, Tanimoto K, Suei Y, Ohama K, Wada T. Relationship between the mandibular and lumbar vertebral bone mineral density at different postmenopausal stages. Dentomaxillofac Radiol 1996; 25: 130–135.[Abstract]
  43. Tibiletti MG, Testa G, Wegetti W, Alagna F, Taborelli M, Dalpra L, et al. The idiopathic forms of premature menopause and early menopause show the same genetic pattern. Hum Reprod 1999; 14: 2731–2734.[Abstract/Free Full Text]
  44. Cassau B, Derriennic F, Monfort C, Dell’ Accio P, Touranchet A. Risk factors of early menopause in two generations of gainfully employed French women. Maturitas 1997; 26: 165–174.[CrossRef][Medline]
  45. World Health Organization, Obesity: preventing and managing the global epidemic. WHO obesity technical report series 2000; no. 894. Geneva: Switzerland World Health Organization; 2000.
  46. Nakamoto T, Taguchi A, Ohtsuka M, Suei Y, Fujita M, Tanimoto K, et al. Dental panoramic radiograph as a tool to detect postmenopausal women with low bone mineral density: untrained general dental practitioners’ diagnostic performance. Osteoporos Int 2003; 14: 659–664.[CrossRef][Medline]
  47. Taguchi A, Suei Y, Ohtsuka M, Otani K, Tanimoto K, Ohtaki M. Usefulness of panoramic radiography in the diagnosis of postmenopausal osteoporosis in women. Width and morphology of inferior cortex of the mandible. Dentomaxillofac Radiol 1996; 25: 263–267.[Abstract]
  48. Chan YH. Biostatistics 104: Correlation analysis. Singapore Med J 2003; 44: 614–619.[Medline]
  49. Devlin CV, Horner K, Devlin H. Variability in measurement of radiomorphometric indices by general dental practitioners. Dentomaxillofac Radiol 2002; 30: 120–125.
  50. Jowitt N, MacFarlane T, Devlin H, Klemetti E, Horner K. The reproducibility of the mandibular cortical index. Dentomaxillofac Radiol 1999; 28: 141–144.[Abstract]
  51. Fazzalari NL, Parkinson IH. Fractal properties of cancellous bone of the iliac crest in vertebral crush fractures. Bone 1998; 23: 53–57.[Medline]



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