Trabecular bone patterns as a fracture risk predictor: a systematic review

Figures & data

Figure 1. Selection process: PRISMA flow diagram according to Moher et al. (2009).

Table 1. Included articles – characteristics of included studies.

Author, Year, Country	Study design	Study duration (years)	Study groups: Index vs. reference	Patients (n)	Mean age (years)	Men (%)	Outcome variables
Jonasson and Billhult [14], 2013, Sweden	Cohort	15 years, 1996–2011	Intraoral radiograph vs. FRAX and/or BMD (by DXA)	136	64.1 (2011) SD 11.2 Range 35–94	0	Fracture risk
Sundh et al. [12], 2017, Sweden	Cohort	10 years 1980–1992 10 years 1992–2002	Panoramic radiograph vs. FRAX	499 (1980–1992) 412 (1992–2002)	Range 50–66 (1980–1992) Range 62–78 (1992–2002)	0	Fracture risk
Verheij et al. [24], 2009, Belgium, Greece, The Netherlands, Sweden, United Kingdom	Cross- sectional	–	Panoramic and intraoral radiographs vs. BMD (by DXA)	607	54.8 SD 6.1 Range 45–71	0	Sensitivity and specificity for osteoporosis measured with DXA^a

The two combined models were not considered in the GRADE ratings of the cohort studies, and age was not included in the cross-sectional study.

BMD: Bone mineral density; DXA: Dual-energy X-ray absorptiometry; FRAX: Fracture risk assessment tool.

^a DXA BMD T-score $\le$ −2.5.

Table 2. Excluded articles and main reason for exclusion.

Author, year	Reason for exclusion
^1Ay, S., et al., 2005. Assessment of mandibular bone mineral density in patients with type 2 diabetes mellitus.	Wrong I
^2Engel, M.B., et al., 1994. Radiological evaluation of bone status in the jaw and in the vertebral column in a group of women.	Wrong I
^3Geraets, W.G., C. Lindh, and H. Verheij, 2012. Sparseness of the trabecular pattern on dental radiographs: visual assessment compared with semi-automated measurements.	Wrong O
^4Geraets, W.G., et al., 2007 Prediction of bone mineral density with dental radiographs.	Wrong O
^5Geraets, W.G., et al., 2008 Selecting regions of interest on intraoral radiographs for the prediction of bone mineral density.	Wrong O
^6Hedstrom, L., A. Baigi, and H. Bergh, 2010. The relation between bone mineral density in the heel and pixel intensity in the mandibular jaw bone among elderly women.	Wrong C
^7Jonasson, G., 2009. Bone mass and trabecular pattern in the mandible as an indicator of skeletal osteopenia: a 10-year follow-up study.	Wrong O
^8Jonasson, G., 2015. Five-year alveolar bone level changes in women of varying skeletal bone mineral density and bone trabeculation	Wrong O
^9Jonasson, G., G. Bankvall, and S. Kiliaridis, 2001. Estimation of skeletal bone mineral density by means of the trabecular pattern of the alveolar bone, its interdental thickness, and the bone mass of the mandible.	Wrong O
^10Jonasson, G., L. Jonasson, and S. Kiliaridis, 2006. Changes in the radiographic characteristics of the mandibular alveolar process in dentate women with varying bone mineral density: a 5-year prospective study.	Wrong O
^11Jonasson, G., L. Jonasson, and S. Kiliaridis, 2007. Skeletal bone mineral density in relation to thickness, bone mass, and structure of the mandibular alveolar process in dentate men and women.	Wrong O
^12Jonasson, G., et al., 2011. A prospective study of mandibular trabecular bone to predict fracture incidence in women: a low-cost screening tool in the dental clinic.	Duplicate material of Jonasson G., 2013
^13Jonasson, G., et al., 2013. Mandibular bone changes in 24 years and skeletal fracture prediction.	Case series not reporting risks or complications
^14Kathirvelu, D. and M. Anburajan, 2014. Prediction of low bone mass using a combinational approach of cortical and trabecular bone measures from dental panoramic radiographs.	Wrong O
^15Kavitha, M.S., et al., 2015. Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women.	Wrong I
^16Kavitha, M.S., et al., 2016. Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches.	Wrong I
^17Khojastehpour, L., et al., 2013. Comparison of the mandibular bone densitometry measurement between normal, osteopenic and osteoporotic postmenopausal women.	Wrong I
^18Koh, K.J., H.N. Park, and K.A. Kim, 2012. Prediction of age-related osteoporosis using fractal analysis on panoramic radiographs.	Wrong P
^19Law, A.N., A.M. Bollen, and S.K. Chen, 1996. Detecting osteoporosis using dental radiographs: a comparison of four methods.	Wrong P
^20Lee B.D., and S.C. White, 2005. Age and trabecular features of alveolar bone associated with osteoporosis.	Data not extractable
^21Leite, A.F., et al., 2015. Systematic review with hierarchical clustering analysis for the fractal dimension in assessment of skeletal bone mineral density using dental radiographs.	Wrong I
^22Licks, R., et al., 2010. Development of a prediction tool for low bone mass based on clinical data and periapical radiography.	Wrong O
^23Lindh, C., et al., 2008. The use of visual assessment of dental radiographs for identifying women at risk of having osteoporosis: the OSTEODENT project.	Wrong P
^24Lopez-Lopez, J., et al., 2015. Radiolucent periapical lesions and bone mineral density in post-menopausal women.	Wrong P
^25Lundstrom, A., et al., 2001. Periodontal conditions in 70-year-old women with osteoporosis.	Wrong P
^26Lurie, A., et al., 2012. Recursive hierarchic segmentation analysis of bone mineral density changes on digital panoramic images.	Wrong I
^27Mohajery, M. and S.L. Brooks, 1992. Oral radiographs in the detection of early signs of osteoporosis.	Wrong I
^28Mohammad, A.R., M. Alder, and M.A. McNally, 1996. A pilot study of panoramic film density at selected sites in the mandible to predict osteoporosis.	Wrong P
^29Nackaerts, O,. 2008 Nackaerts, O., et al., 2008. Osteoporosis detection using intraoral densitometry.	Wrong P
^30Roberts, M.G., J. Graham, and H. Devlin, 2013. Image texture in dental panoramic radiographs as a potential biomarker of osteoporosis.	Wrong I
^31Scheibel, P.C., A.L. Ramos, and L.C. Iwaki, 2013. Is there correlation between alveolar and systemic bone density?	Wrong I
^32Sindeaux, R., et al., 2014. Fractal dimension and mandibular cortical width in normal and osteoporotic men and women.	Wrong P
^33Takaishi, Y., et al., 2013. Assessment of alveolar bone mineral density as a predictor of lumbar fracture probability.	Wrong I
^34Tosoni, G.M., et al., 2006. Pixel intensity and fractal analyses: detecting osteoporosis in perimenopausal and postmenopausal women by using digital panoramic images.	Wrong P
^35White, S.C. and D.J. Rudolph, 1999. Alterations of the trabecular pattern of the jaws in patients with osteoporosis.	Wrong P
^36White S.C., et al., 2005. Change in mandibular trabecular pattern and hip fracture rate in elderly women.	Case series not reporting risks or complications
^37White, S.C., et al., 2005. Clinical and panoramic predictors of femur bone mineral density.	Wrong P
^38Yasar, F. and F. Akgunlu, 2006. The differences in panoramic mandibular indices and fractal dimension between patients with and without spinal osteoporosis.	Wrong O
^39Yilmaz, H.H., F. Akgunlu, and U. Aydin, 2004. Comparison of trabecular pattern in healthy and osteoporotic women.	Wrong P

P: population; I: intervention; C: controls; O: outcomes.

Table 3. Outcome variable – fracture prediction.

Author, Year, country	Study design	Number of patients, n =	Withdrawals – dropouts	Results		Comments	Directness^a	Study limitations^a	Precision^a
				Index test (Trabecular bone pattern assessment on dental radiographs)	Reference test (FRAX, BMD)
Jonasson and Billhult [14], 2013, Sweden	Observational, prospective cohort study	1996 n = 166 2011 n = 136 replied (14 died)	n = 30	Fracture prediction: AR 14.7% Visual evaluation, sparse trabeculation RR 1.52 95% CI: 0.56–4.11 AR 35% Jaw-X (software) without clinical variables RR 1.87 95% CI: 0.73–4.71 AR 30% Bone texture (software) RR 4.74 95% CI: 1.49–15.04 AR 36.8%	Fracture prediction: FRAX with BMD RR 2.53 95% CI: 1.13–5.63 FRAX without BMD RR 1.34 95% CI: 0.56–3.20 BMD RR 2.37 95% CI: 0.93–6.02	BMD: DXA forearm To calculate the RR, the FRAX value was dichotomized, where ≥ 7% indicated an increased fracture risk. Blinded, according to author. Information on alcohol was not available 1996, and not included in FRAX. 6 women had a T-score ≤ −2.5	+	?	–
Sundh et al. [12], 2017, Sweden	Observational, prospective cohort study	1980/1981 n = 499 1992/1993 n = 412	No dropouts	Fracture prediction 1980–1992: AR 14.2% Visual evaluation, sparse trabeculation RR 2.09 95% CI: 1.3–3.5 AR 23% OR 3.03 95% CI: 1.81–5.08 Fracture prediction 1992–2002: AR 16.3% Visual evaluation, sparse trabeculation RR 3.7 95% CI: 2.2–6.4 AR 26.1% OR 4.57 95% CI: 2.47–8.45	Fracture prediction 1980–1992: FRAX >15% RR 1.85 95% CI: 0.7–5.6 AR 36.7% OR 4.35 95% CI: 2.28–8.31 Fracture prediction 1992–2002: FRAX >15% RR 4.1 95% CI: 2.4–7.2 AR 26.9% OR 5.28 95% CI: 2.84–9.94	Blinded, according to author. Information on fractures in parents was not available and not included in the FRAX calculation. 397 participated in both (1980–1993 and 1992–2002) examinations.	+	+/?	–

AR: Absolute incident fracture rate; BMD: bone mineral density; Bone texture: computer software; CI: confidence interval; FRAX: fracture risk assessment tool; Jaw-X: computer software; based on an image-processing algorithm; OR: odds ratio; RR: relative risk.

^a ‘+’ no or minor problems; ‘?’ Some problems; ‘–’ Major problems.

Table 4. Outcome variable – sensitivity and specificity for osteoporosis measured with DXA.

Author, Year, country	Study design	Number of patients, n =	Withdrawals– dropouts	Results		Comments	Directness^a	Study limitations^a	Precision^a
				Index test (trabecular bone pattern assessment on dental radiographs)	Reference test (FRAX, BMD)
Verheij et al. [24], 2009, Five countries in Europe: The Netherlands, Belgium, Greece, UK, Sweden.	Observational cross-sectional study	n = 671	n = 64	Morphological digital analysis of trabecular bone pattern (software): Sensitivity 0.70 (95% CI 0.62–0.78) Specificity 0.69 (95% CI 0.64–0.73)	DXA hip, spine   BMD Cut-off T-score ≤ −2.5	Convenience sample. No pre-determined threshold. Split of Index variable to obtain maximum sensibility and specificity, i.e. no blinding. Mean age 54.8 years (SD 6.1) Regions of interest on the radiographs were chosen by preference. No indication of blinding.	+	?	–

BMD: bone mineral density; CI: confidence interval; DXA: dual-energy X-ray absorptiometry.

^a‘+’ No or minor problems; ‘?’ Some problems; ‘–’ Major problems.

Table 5. Summary of findings.

Outcomes	Study design Number of studies	Relative risk (95%CI)		Certainty of evidence GRADE*
		Index test	Reference test
Fracture prediction	1 cohort study with 15-year follow-up (n = 136)	Visual evaluation of sparse trabeculation RR 1.52 (0.56–4.11) Jaw-X (without clinical variables) RR 1.87 (0.73–4.71) Bone Texture RR 4.74 (1.49–15.04)	FRAX ≥ 7% (without BMD) RR 1.34 (0.56–3.20) BMD (T-score, underarm) RR 2.37 (0.93–6.02)	⊕⊕○○^a
	1 cohort study with 10-year follow-up, two overlapping groups (n = 499 and 412, respectively)	Visual evaluation of sparse trabeculation 1980–1992 RR 2.09 (1.3–3.5) 1992–2002 RR 3.7 (2.2–6.4)	FRAX > 15% (without BMD) RR 1.85 (0.7–5.6) RR 4.1 (2.4–7.2)
Sensitivity and specificity	1 cross-sectional study (n = 671)	Sensitivity: 0.70 (0.62–0.78) Specificity: 0.69 (0.64–0.73)	BMD T-score $\le$ −2.5	⊕○○○^b
*Certainty of evidence
High certainty ⊕⊕⊕⊕	We are very confident that the true effect lies close to that of the estimate of the effect.
Moderate certainty ⊕⊕⊕○	We are moderately confident about the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.
Low certainty ⊕⊕○○	Confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect.
Very low certainty ⊕○○○	We have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect.

BMD: bone mineral density; CI: confidence interval; FRAX: fracture risk assessment tool; Jaw-X: a computer software; based on an image-processing algorithm; Bone Texture: software evaluation of bone texture; RR: relative risk.

^aSome study limitations (patient flow not clearly described), uncertain precision (few events), and large effect (upgrade one step).

^bSerious study limitations (missing data, blinding not reported, x-ray location of analysis on preference), and serious imprecision (hypothesis-generating design, low prevalence).

Jonasson G, Billhult A. Mandibular bone structure, bone mineral density, and clinical variables as fracture predictors: a 15-year follow-up of female patients in a dental clinic. Oral Surg Oral Med Oral Pathol Oral Radiol. 2013;116:362–368.

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Sundh V, Hange D, Ahlqwist M, et al. FRAX and mandibular sparse trabeculation as fracture predictors: a longitudinal study from 1980 to 2002. Eur J Oral Sci. 2017;125:135–140.

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Verheij JG, Geraets WG, van der Stelt PF, et al. Prediction of osteoporosis with dental radiographs and age. Dentomaxillofac Radiol. 2009;38:431–437.

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Data availability statement

All data generated and analysed in this review are included within the article.