Cone Beam Computed Tomography Panoramic Mandibular Indices in the Screening of Postmenopausal Women with Low Bone Mass: Correlations with Bone Quantity and Quality

1. Introduction

First defined in 1993, osteoporosis is a widespread bone condition marked by reduced bone density and structural weakening of bone tissue, leading to heightened bone fragility and an increased risk of fractures [1]. Although this condition can affect almost any person from any age group, postmenopausal women are a population “at-risk” to suffer from osteoporosis, mainly because of the changes in estrogen levels [2]. For the measurement of bone mineral density (BMD), the dual-energy-X-ray absorptiometry (DXA) is the gold standard for the diagnosis of osteoporosis [3] , defined as follows, according to American Association Of Clinical Endocrinologists and American College Of Endocrinology (AACE/ACE) Clinical Practice Guidelines criteria: a. T-score –2.5 or below in the lumbar spine, femoral neck, total, and/or 33% (one-third) radius; b. Low-trauma spine or hip fracture (regardless of BMD); c. Osteopenia or low bone mass (T-score between –1 and –2.5) with a fragility fracture of proximal humerus, pelvis, or possibly distal forearm; d. Low bone mass or osteopenia and high FRAX® fracture probability based on country-specific thresholds [4].

The threshold for diagnosing osteoporosis based on BMD is important for clinical assessment and treatment, but although it is the best tool to predict fracture risk, has several limitations that reduce its effectiveness in identifying patients who will eventually experience a fracture [1,5].

Knowing that there might be a significant correlation the BMD of the mandible and that in the femoral neck and lumbar spine measured in patients with osteoporosis [6,7], some tools like cone beam computed tomography (CBCT), which assesses jaw bone density and quality might be used to evaluate BMD in patients with low bone mass [8]. Barra et al [8] stated that radiomorphometric indices measured in specific centers from mental foramina are promising tools in CBCT of the mandible for assessing the BMD in postmenopause, exhibiting significant differences among patients with normal BMD and the ones with osteopenia and/or osteoporosis. As well, Koh et al [7] reported that specific indices on CBCT axial, sagittal and coronal images are useful methods for osteoporosis screening. These panoramic quantitative indices are the most commonly used to evaluate the low bone mass in the mental foramina region.

We aimed to evaluate the correlations between panoramic mandibular and bone mass density in postmenopausal women, with regards to both quantity and quality.

2. Materials and Methods

This study was performed on postmenopausal women. Written informed consent was obtained from patients before the study. The study was approved by the Ethics Committee of “C. I. Parhon” National Institute of Endocrinology, Bucharest, Romania.

The inclusion criteria were: female sex; age greater than 50 years, DXA with TBS evaluation, biological evaluation, CBCT evaluation. Exclusion criteria were: presence of systemic diseases affecting bone metabolism, such as neoplasia, osteomalacia/history of rickets, endocrine disorders (hyperthyroidism, hyperparathyroidism, Cushing’s syndrome, or acromegaly), severe renal failure, liver failure, malabsorption disorders (Crohn disease, celiac disease); presence of rheumatologic diseases (rheumatoid arthritis, ankylosing spondylitis), history of oophorectomy; usage of medications interfering with bone turnover/density (glucocorticoids, aromatase inhibitors, selective serotonin reuptake inhibitors, medroxyprogesterone acetate, antiepileptic drugs, unfractionated heparin).

The included postmenopausal women had normal BMD, osteopenia or osteoporosis, with or without antiresorbtive/anabolic treatment.

2.1. CBCT Measurements

According to the modified Ledgerton’s classification [9], we used the following panoramic mandibular indices: CTI(S), CTI(I), and CTMI, defined as follows:

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CTI(S): superior CT mandibular index ( the ratio of the inferior cortical width to the distance from the superior margin of the mental foramen to the inferior mandibular border)

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CTI(I): inferior CT mandibular index ( the ratio of the inferior cortical width to the distance from the inferior margin of the mental foramen to the inferior mandibular border)

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CTMI: CT mental index (the inferior mandibular cortical width)

Table 1. Indices and their significance.

Index	Value
CTI(S)	W/S
CTI(I)	W/I
CTMI	W
*W-inferior cortical width of the mandible; S- distance from the superior margin of the mental foramen to the inferior mandibular border; I- distance from the inferior margin of the mental foramen to the inferior mandibular border

Table 1. Indices and their significance.

Index	Value
CTI(S)	W/S
CTI(I)	W/I
CTMI	W
*W-inferior cortical width of the mandible; S- distance from the superior margin of the mental foramen to the inferior mandibular border; I- distance from the inferior margin of the mental foramen to the inferior mandibular border

The measurements of S, I, and W indices were performed on coronal images. The W index represents the inferior cortical width of the mandible; the S index represents the distance from the superior margin of the mental foramen to the inferior mandibular border; the I index represents the distance from the inferior margin of the mental foramen to the inferior mandibular border. The measurements were performed by an experimented radiologist.

The CBCT images were obtained using NewTom VGi EVO Cone Beam 3D Imaging (CEFLA s.c. – Via Selice Provinciale 23/a IMOLA, Italy), at 110 kV, 7,5 mA, 3,5 s, pixel size 0,2 mm. The images were reconstructed using NewTom NNT (ISDP©10003:2020 compliant in accordance with EN ISO/IEC 17065:2012 certificate number 2019003109-2) with Viewer software.

2.2. Bone Mineral Density Measurements

BMD was measured at the lumbar spine (LS), femoral neck (FN), and total hip by DXA (General Electric Prodigy Lunar, Bedford, UK) using an enCore Software 10,50,086. BMD was expressed in grams per square centimeters (g/cm2), and by comparing the BMD with the peak bone mass of a young adult, a T-score was obtained, expressed in standard deviations (SD) and a Z-score for age-matched SD [15]. All measurements were done according to the International Society for Clinical Osteodensitometry [10]

TBS values were obtained by analyzing the L1–L4 vertebrae DXA images with an iNsight Software version 2.2.0.0 (Medimaps Group SA Headquarters, Switzerland).

All patients were scanned on the same DXA machine, yet by two different operators, thus allowing a user bias.

3. Statistical Analysis

We statistically analyzed the patients based on the value of the BMD (lumber spine, femoral neck, total hip scores), T score (lumber spine, femoral neck, total hip scores), respectively and TBS as continuous values, regardless of the osteoporosis diagnosis at the time of CBCT evaluation.

Alternatively, we used binary logistic analysis to divide the patients based on the osteoporosis diagnosis (according to AACE/ACE criteria) [4]. We also employed the regression analysis, the t-test, the Pearson’s correlation coefficient and Spearman’s rho, using IBM SPSS Statistics, version 25 (SPSS Inc, Chicago, IL, USA) for Mac OS.

4. Results

The characteristics of the patients included in the study are presented in the table below (Table 2). We included in the study 104 postmenopausal women, with mean age of 65.15 ± 9.12 years old. The mean age for menopause was 47.22 ± 5.3 years old, with a mean body mass index (BMI) of 26.58 ± 7.48. The mean lumbar BMD score was 0.944 g/cm³ (0.544, 1.437), with a mean T lumbar score of -1.95 SD (-5.3, 2.1). For femoral neck, the mean BMD score was 0.944 g/cm³ (0.554, 1.221), with a mean T score of -1.95 SD (-3.5, 1.3). The mean score for TBS was 1.284 g/cm³ ± 105.83 SD (1.062, 1,558).

Table 3 lists the mean values of the CBCT parameters, depending on the bone quantity (assessed with lumbar and femoral neck T-scores), and bone quality (assessed with TBS). There were statistically significant differences based on bone quality and quantity, higher values of the studied CBCT parameters being observed in patients with higher T score and higher TBS (p < 0.0001).

Statistically significant correlations were also observed between bone quantity, this time expressed as linear value using lumbar, femoral neck and total hip BMD, not only using T-score in the three mentioned regions. This emphasizes the importance of BMD in assessing bone quantity, more than just the T-score (Table 4). All CBCT parameters were moderately correlated (the highest correlation coefficient being 0.551 for CTMI with femoral neck T-score, see table 4). Using Pearson’s correlation coefficient and Spearman’s rho, we found the same moderate correlation between CBCT indices and bone mass (Table 5).

The study revealed moderate correlations between CBCT indices and BMD/TBS scores. Specifically:

• CTMI showed the highest correlation with femoral neck T-score (r = 0.551, p < 0.0001).
• CTI(S) and CTI(I) also displayed significant correlations with T-scores and BMD measurements across various sites (lumbar spine, femoral neck, and total hip), with correlation coefficients ranging from 0.322 to 0.522 (p < 0.0001).
• TBS scores were also moderately correlated with CBCT indices, with CTMI having a correlation coefficient of 0.431 (p < 0.0001).

By using regression analysis (linear and logistic), we listed in Table 6 and Table 7 the prediction power of the studied CBCT parameters for assessing the bone mass. We found that CTMI, CTI(I) and CTI(S) re effective in predicting bone quality and quantity (p<0.0001). In the logistic regression analysis, all models reached statistical significance (see Table 7).

5. Discussion

This study examined the use of computed tomography panoramic mandibular indices on CBCT images for the evaluation of BMD in postmenopausal women. The results demonstrated that CTMI, CTI(S) and CTI(I) are tools that can be used in mandibular CBCT for the assessment of bone mass in postmenopausal women, these indices exhibiting significant differences among this this category of patients, depending of the bone mass quantity and quality evaluated on DXA scan.

The study found significant correlations between the panoramic mandibular indices and lumbar T-scores. The computed tomography mental index (CTMI), computed tomography mandibular index superior (CTI(S)), and computed tomography mandibular index inferior (CTI(I)) showed the following Pearson correlation coefficients with lumbar T-scores: CTMI: r = 0.429, p < 0.0001; CTI(S): r = 0.387, p < 0.0001; CTI(I): r = 0.364, p < 0.0001. These results indicate a moderate positive correlation, suggesting that as the lumbar T-score increases, indicating higher bone density, the values of the mandibular indices also tend to increase. The correlations with femoral neck T-scores were even stronger. The higher correlation coefficients reflect a stronger relationship between mandibular indices and femoral neck bone density compared to the lumbar spine. Similarly, total hip T-scores also showed significant correlations. Concerning TBS, the moderate correlations suggest that higher mandibular indices are associated with better trabecular bone quality.

Koh and colleagues [7,11] reported that specific CBCT indices could be effective for osteoporosis screening. Their findings align with the current study, which demonstrates moderate to strong correlations between CBCT indices and BMD/TBS measurements. Koh et al. also highlighted the usefulness of CBCT in assessing bone quality in postmenopausal women.

TBS is a relatively new metric derived from lumbar DXA scans. Unlike BMD, which measures bone mineral density, TBS provides an indirect assessment of trabecular microarchitecture. It is a valuable tool in evaluating bone quality and predicting fracture risk, as it reflects the structural integrity of trabecular bone, which is crucial in understanding bone strength and fragility. The current study explored the correlation between CBCT-derived mandibular indices and TBS. The key findings are: CTMI showed a moderate positive correlation with TBS (r = 0.431, p < 0.0001); CTI(S) had a similar moderate positive correlation with TBS (r = 0.421, p < 0.0001); CTI(I) also correlated with TBS, albeit slightly lower than the other two indices (r = 0.351, p < 0.0001). These correlations indicate that higher mandibular index values, as measured by CBCT, are associated with better trabecular bone quality as indicated by TBS scores. Diba SF et al [12] stated that the depletion of trabeculae bone structure is more obvious in osteoporotic postmenopausal women; thus, the initial bone quality assessment can be done using trabecular thickness parameters. However, in this study, they used dental radiographs as bone quality screening tool. This is the first study to correlate TBS with panoramic mandibular CBCT indices regarding the assessment of bone quality in postmenopausal women.

The consistent moderate to strong correlations across different T-scores and BMD measurements indicate that CBCT indices are reliable indicators of bone health. The highest correlation was observed between CTMI and femoral neck T-scores (r = 0.551), suggesting that mandibular indices might be particularly sensitive to changes in the bone density of the femoral neck. In a study by Munhoz et al. [13], significant correlations were found between mandibular cortical width and BMD. This study corroborates the present findings, particularly the strong correlation between CTMI and femoral neck BMD, reinforcing the potential of mandibular indices as indicators of overall bone health.

Barra et al. [14] also found significant differences in mandibular radiomorphometric indices among patients with normal BMD or low bone mass. Their study emphasized the potential of CBCT measurements in reflecting systemic bone density changes, similar to the findings in the present study.

While DXA remains the gold standard for diagnosing osteoporosis, this study highlights some limitations, such as the inability to predict fractures effectively. It has been cited in the literature that one of the limitations BMD measurement is that the majority of fragility fractures occur at non-osteoporotic BMD (T-scores >−2.5), compromising its role as a screening tool. Sornay-Rendu et al. [15] studied the radius bone microarchitecture with high-resolution peripheral computed tomography (HR-pQCT) in their OFELY study in postmenopausal women without low BMD. During a median of 15 years of follow-up, 46 women sustained incident fragility fractures, including 19 women with a major fragility fracture (clinical spine, forearm, proximal humerus, hip). Li et al. [16] investigated the fracture predictive value of QCT-based trabecular BMD of thoracic vertebrae derived from coronary artery calcium scan for hip fractures in a nationwide multi-center cohort (6814 subjects). Over a median follow-up of 17.4 years, in non-osteoporotic participants, 174 had radiographic moderate to severe compression vertebral fractures and 21 had hip fracture. CBCT offers an alternative by providing additional bone quality metrics through panoramic mandibular indices. The significant correlations between CBCT indices and DXA measurements support the recognition of CBCT as a bone mass predictor, especially taking into consideration the increased use for this tool in the general population.

Our findings suggest that recognizing the potential role of CBCT to assess bone mass could enhance early detection and management of osteoporosis in postmenopausal women. Since CBCT can be more accessible and less cumbersome than DXA, it offers a practical approach for widespread osteoporosis screening in addition to the existing tools. This is particularly important given the increasing prevalence of osteoporosis among the aging populations.

6. Conclusions

The findings of this study suggest that CBCT-derived mandibular indices can be reliable indicators of low bone mass in postmenopausal women. These indices could potentially be integrated into routine dental assessments, providing an accessible screening tool for osteoporosis. Future research should aim to: validate these findings in larger, more diverse populations, explore the longitudinal predictive value of these indices for fracture risk, and investigate the cost-effectiveness and practical implementation of CBCT screenings in clinical practice.