The Utility of Radiomorphometric Mandibular Indices on Cone Beam Computer Tomography in the Assessment of Bone Mass in Postmenopausal Women: A Cross-Sectional Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. CBCT Measurements
- Anterior index (A)—the thickness of the inferior mandibular cortex 10 mm anterior from the MF;
- Molar index (M)—the thickness of the inferior mandibular cortex 10 mm posterior from the MF;
- Posterior index (P)—the thickness of the inferior mandibular cortex 25 mm posterior from the MF;
- Symphysis index (S)—the thickness of the inferior mandibular cortex equidistant from the centers of the right and left MF.
2.2. Bone Mineral Density Measurements
- T-score −2.5 or below in the lumbar spine, femoral neck, total proximal femur, or 1/3 radius;
- Low-trauma spine or hip fracture (regardless of bone mineral density);
- T-score between −1.0 and −2.5 and a fragility fracture of the proximal humerus, pelvis, or distal forearm;
- T-score between −1.0 and −2.5 and high fracture risk assessment tool (FRAX® University of Sheffield, UK) fracture probability based on country-specific thresholds.
2.3. Statistical Analysis
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Camacho, P.M.; Petak, S.M.; Binkley, N.; Clarke, B.L.; Harris, S.T.; Hurley, D.L.; Kleerekoper, M.; Lewiecki, E.M.; Miller, P.D.; Narula, H.S.; et al. American association of clinical endocrinologists and American college of endocrinology clinical practice guidelines for the diagnosis and treatment of postmenopausal osteoporosis—2016. Endocr. Pract. Off. J. Am. Coll. Endocrinol. Am. Assoc. Clin. Endocrinol. 2016, 22 (Suppl. S4), 1111–1118. [Google Scholar] [CrossRef] [PubMed]
- Tang, C.H. Osteoporosis: From Molecular Mechanisms to Therapies 2.0. Int. J. Mol. Sci. 2020, 21, 8005. [Google Scholar] [CrossRef] [PubMed]
- Camacho, P.M.; Petak, S.M.; Binkley, N.; Diab, D.L.; Eldeiry, L.S.; Farooki, A.; Harris, S.T.; Hurley, D.L.; Kelly, J.; Lewiecki, E.M.; et al. American Association of Clinical Endocrinologists/American College of Endocrinology Clinical Practice Guidelines for the Diagnosis and Treatment of Postmenopausal Osteoporosis—2020 Update. Endocr. Pract. 2020, 26, 1–46. [Google Scholar] [CrossRef]
- Aibar-Almazán, A.; Voltes-Martínez, A.; Castellote-Caballero, Y.; Afanador-Restrepo, D.F.; Carcelén-Fraile, M.d.C.; López-Ruiz, E. Current Status of the Diagnosis and Management of Osteoporosis. Int. J. Mol. Sci. 2022, 23, 9465. [Google Scholar] [CrossRef]
- Sirufo, M.M.; De Pietro, F.; Bassino, E.M.; Ginaldi, L.; De Martinis, M. Osteoporosis in Skin Diseases. Int. J. Mol. Sci. 2020, 21, 4749. [Google Scholar] [CrossRef] [PubMed]
- Agrawal, K.K.; Singh, N.; Chand, P.; Singh, S.V.; Solanki, N.; Garg, R.K.; Chaurasia, A. Associations among gene polymorphisms, crestal bone loss, and bone mineral density in patients receiving dental implants. J. Taibah Univ. Med. Sci. 2024, 19, 313–320. [Google Scholar] [CrossRef]
- Isayev, A.; Velieva, N.; Isedisha, L.; Isayeva, Z.; Kamburoğlu, K.; Kuyumcu, F. Cone-Beam Computed Tomography as a Prediction Tool for Osteoporosis in Postmenopausal Women: A Systematic Literature Review. Diagnostics 2023, 13, 1027. [Google Scholar] [CrossRef]
- Poiana, I.R.; Dobre, R.; Popescu, R.-I.; Pituru, S.-M.; Bucur, A. Utility of Cone-Beam Computed Tomography in the Detection of Low Bone Mass—A Systematic Review. J. Clin. Med. 2023, 12, 5890. [Google Scholar] [CrossRef]
- Sornay-Rendu, E.; Duboeuf, F.; Chapurlat, R. Postmenopausal women with normal BMD who have fracture have deteriorated bone microarchitecture: A prospective analysis from the OFELY study. Bone 2024, 182, 117072. [Google Scholar] [CrossRef]
- Basudan, A.M.; Shaheen, M.Y.; Niazy, A.A.; Beucken, J.J.v.D.; Jansen, J.A.; Alghamdi, H.S. Effect of Osteoporosis on Well-Integrated Bone Implants. Appl. Sci. 2021, 11, 723. [Google Scholar] [CrossRef]
- Lemos, C.A.A.; de Oliveira, A.S.; Faé, D.S.; e Oliveira, H.F.F.; Rosa, C.D.D.R.D.; Bento, V.A.A.; Verri, F.R.; Pellizzer, E.P. Do dental implants placed in patients with osteoporosis have higher risks of failure and marginal bone loss compared to those in healthy patients? A systematic review with meta-analysis. Clin. Oral Investig. 2023, 27, 2483–2493. [Google Scholar] [CrossRef]
- Albayati, R.M.; Saliem, S.S.; Al Nakib, L.H. Osteoporosis effect on posterior mandible in preimplanted area for postmenopausal females using cone-beam computed tomography. J. Indian Soc. Periodontol. 2018, 22, 390–394. [Google Scholar] [CrossRef] [PubMed]
- Seigneur, M.; Hascoët, E.; Chaux, A.-G.; Lesclous, P.; Hoornaert, A.; Cloitre, A. Characteristics and management of dental implants displaced into the maxillary sinus: A systematic review. Int. J. Oral Maxillofac. Surg. 2023, 52, 245–254. [Google Scholar] [CrossRef] [PubMed]
- Francisco, I.; Nunes, C.; Pereira, F.; Travassos, R.; Ribeiro, M.P.; Marques, F.; McEvoy, M.; Santos, M.; Oliveira, C.; Marto, C.M.; et al. Bone Mineral Density through DEXA and CBCT: A Systematic Review with Meta-Analysis. Appl. Sci. 2023, 13, 5962. [Google Scholar] [CrossRef]
- Klintström, E.; Smedby, O.; Klintström, B.; Brismar, T.B.; Moreno, R. Trabecular bone histomorphometric measurements and contrast-to-noise ratio in CBCT. Dentomaxillofac. Radiol. 2014, 43, 20140196. [Google Scholar] [CrossRef] [PubMed]
- Heuchert, J.; Kozieł, S.; Spinek, A.E. Radiomorphometric indices of the mandible as indicators of decreased bone mineral density and osteoporosis—Meta-analysis and systematic review. Osteoporos. Int. 2024, 35, 401–412. [Google Scholar] [CrossRef] [PubMed]
- Borghesi, A.; Di Salvo, D.; Ciolli, P.; Falcone, T.; Ravanelli, M.; Farina, D.; Carapella, N. Detection Rate and Variability in Measurement of Mandibular Incisive Canal on Cone-Beam Computed Tomography: A Study of 220 Dentate Hemi-Mandibles from Italy. J. Imaging 2022, 8, 161. [Google Scholar] [CrossRef] [PubMed]
- Wang, C.-W.J.; McCauley, L.K. Osteoporosis and Periodontitis. Curr. Osteoporos. Rep. 2016, 14, 284–291. [Google Scholar] [CrossRef]
- de Castro, J.G.K.; Carvalho, B.F.; de Melo, N.S.; Figueiredo, P.T.d.S.; Moreira-Mesquita, C.R.; Vasconcelos, K.d.F.; Jacobs, R.; Leite, A.F. A new cone-beam computed tomography–driven index for osteoporosis prediction. Clin. Oral Investig. 2020, 24, 3193–3202. [Google Scholar] [CrossRef]
- Geibel, M.A.; Löffler, F.; Kildal, D. Osteoporosis detection using cone-beam computed tomography. Orthopäde 2016, 45, 1066–1071. [Google Scholar] [CrossRef]
- Barra, S.G.; Gomes, I.P.; Amaral, T.M.P.; Brasileiro, C.B.; Abreu, L.G.; Mesquita, R.A. New mandibular indices in cone beam computed tomography to identify low bone mineral density in postmenopausal women. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 2020, 131, 347–355. [Google Scholar] [CrossRef]
- Güngör, E.; Yildirim, D.; Çevik, R. Evaluation of osteoporosis in jaw bones using cone beam CT and dual-energy X-ray absorptiometry. J. Oral Sci. 2016, 58, 185–194. [Google Scholar] [CrossRef] [PubMed]
- Koh, K.-J.; Kim, K.-A. Utility of the computed tomography indices on cone beam computed tomography images in the diagnosis of osteoporosis in women. Imaging Sci. Dent. 2011, 41, 101–106. [Google Scholar] [CrossRef] [PubMed]
- Mostafa, R.A.; Arnout, E.A.; Abo El-Fotouh, M.M. Feasibility of cone beam computed tomography radiomorphometric analysis and fractal dimension in assessment of postmenopausal osteoporosis in correlation with dual X-ray absorptiometry. Dentomaxillofac. Radiol. 2016, 45, 20160212. [Google Scholar] [CrossRef] [PubMed]
- Franciotti, R.; Moharrami, M.; Quaranta, A.; Bizzoca, M.E.; Piattelli, A.; Aprile, G.; Perrotti, V. Use of fractal analysis in dental images for osteoporosis detection: A systematic review and meta-analysis. Osteoporos. Int. 2021, 32, 1041–1052. [Google Scholar] [CrossRef] [PubMed]
- Brasileiro, C.B.; Chalub, L.L.F.H.; Abreu, M.H.N.G.; Barreiros, I.D.; Amaral, T.M.P.; Kakehasi, A.M.; Mesquita, R.A. Use of cone beam computed tomography in identifying postmenopausal women with osteoporosis. Arch. Osteoporos. 2017, 12, 26. [Google Scholar] [CrossRef]
- Lewiecki, E.M.; Binkley, N.; Morgan, S.L.; Shuhart, C.R.; Camargos, B.M.; Carey, J.J.; Gordon, C.M.; Jankowski, L.G.; Lee, J.-K.; Leslie, W.D. Best Practices for Dual-Energy X-ray Absorptiometry Measurement and Reporting: International Society for Clinical Densitometry Guidance. J. Clin. Densitom. 2016, 19, 127–140. [Google Scholar] [CrossRef] [PubMed]
- Cheng, C.-H.; Chen, L.-R.; Chen, K.-H. Osteoporosis Due to Hormone Imbalance: An Overview of the Effects of Estrogen Deficiency and Glucocorticoid Overuse on Bone Turnover. Int. J. Mol. Sci. 2022, 23, 1376. [Google Scholar] [CrossRef]
- Masri, D.; Masri-Iraqi, H.; Nissan, J.; Naishlos, S.; Ben-Zvi, Y.; Rosenfeld, E.; Avishai, G.; Chaushu, L. On the Association between Dental Implants, Osteoporosis and Bone Modulating Therapy. Appl. Sci. 2023, 13, 3398. [Google Scholar] [CrossRef]
- Taguchi, A.; Tsuda, M.; Ohtsuka, M.; Kodama, I.; Sanada, M.; Nakamoto, T.; Inagaki, K.; Noguchi, T.; Kudo, Y.; Suei, Y.; et al. Use of dental panoramic radiographs in identifying younger postmenopausal women with osteoporosis. Osteoporos. Int. 2006, 17, 387–394. [Google Scholar] [CrossRef]
- Yamada, S.; Ohyama, K.; Taguchi, M.; Takeshita, A.; Morita, K.; Takano, K.; Sano, T. A study of the correlation between morphological findings and biological activities in clinically nonfunctioning pituitary adenomas. Neurosurgery 2007, 61, 580–585. [Google Scholar] [CrossRef] [PubMed]
- Taguchi, A. Bone mineral density and mandibular bone quality in patients receiving dental implants. Osteoporos. Int. 2007, 18, 121–122. [Google Scholar] [CrossRef] [PubMed]
- Sozen, T.; Ozisik, L.; Basaran, N.C. An overview and management of osteoporosis. Eur. J. Rheumatol. 2017, 4, 46–56. [Google Scholar] [CrossRef] [PubMed]
- Munhoz, L.; Morita, L.; Nagai, A.Y.; Moreira, J.; Arita, E.S. Mandibular cortical index in the screening of postmenopausal at low mineral density risk: A systematic review. Dentomaxillofac. Radiol. 2021, 50, 20200514. [Google Scholar] [CrossRef] [PubMed]
- Guerra, E.N.S.; Almeida, F.T.; Bezerra, F.V.; Figueiredo, P.T.D.S.; Silva, M.A.G.; Canto, G.D.L.; Pachêco-Pereira, C.; Leite, A.F. Capability of CBCT to identify patients with low bone mineral density: A systematic review. Dentomaxillofac. Radiol. 2017, 46, 20160475. [Google Scholar] [CrossRef]
- Lee, J.-H.; Yun, J.-H.; Kim, Y.-T. Deep learning to assess bone quality from panoramic radiographs: The feasibility of clinical application through comparison with an implant surgeon and cone-beam computed tomography. J. Periodontal Implant. Sci. 2024, 54, e5. [Google Scholar] [CrossRef]
Parameter | T-Score ≤ −2.5 * | T-Score ≥ −2.5 * |
---|---|---|
Number | 45 | 59 |
Age at menopause (years) | 47.18 ± 4.49 | 47.43 ± 5.73 |
Femoral neck T-score (SD) | −2.06 ± 0.66 | −1.15 ± 0.85 |
Total hip T-score (SD) | −1.67 ± 0.78 | −0.6 ± 1.04 |
Lumbar T-score (L1–L4) (SD) | −3.12 ± 0.69 | −1.3 ± 0.92 |
Femoral neck BMD (g/cm2) | 0.727 ± 0.082 | 0.852 ± 0.11 |
Total hip BMD (L1–L4) (g/cm2) | 0.713 ± 0.366 | 0.941 ± 0.132 |
Lumbar BMD (g/cm2) | 0.805 ± 0.088 | 1.018 ± 0.012 |
TBS score (g/cm2) | 1.207 ± 0.181 | 1.313 ± 0.101 |
CBCT Parameter | Lumbar T-Score > −2.5 SD (n = 62) | Lumbar T-Score ≤ −2.5 SD (n = 42) | Low Bone Quality (TBS ≤ 1.23, n = 21) | Intermediate Bone Quality (TBS > 1.23 and <1.31, n = 32) | Normal Bone Quality (TBS ≥ 1.31, n = 33) | Femoral Neck T-Score > −2.5 DS (n = 85) | Femoral Neck T-Score ≤ −2.5 DS (n = 14) | p Value |
---|---|---|---|---|---|---|---|---|
S | 2.03 ± 0.6 | 1.73 ± 0.57 | 1.85 ± 0.63 | 1.75 ± 0.49 | 2.0 ± 0.63 | 1.9 ± 0.59 | 1.75 ± 0.55 | <0.001 |
A | 2.93 ± 0.91 | 2.30 ± 0.72 | 2.22 ± 0.67 | 2.41 ± 0.85 | 2.9 ± 0.78 | 2.6 ± 0.78 | 1.65 ± 0.47 | <0.001 |
M | 2.72 ± 0.74 | 1.9 ± 0.6 | 2.04 ± 0.65 | 2.05 ± 0.82 | 2.6 ± 0.75 | 2.3 ± 0.76 | 1.57 ± 0.47 | <0.001 |
P | 2.6 ± 0.66 | 2.04 ± 0.69 | 2.17 ± 0.69 | 2.26 ± 0.71 | 2.5 ± 0.72 | 2.4 ± 0.70 | 1.71 ± 0.54 | <0.001 |
Parameters for Correlations † | S | A | M | P |
---|---|---|---|---|
Lumbar T-score * | 0.043, p = 0.675 | 0.387, p < 0.0001 | 0.429, p < 0.0001 | 0.285, p = 0.004 |
Femoral neck T-score * | 0.079, p = 0.44 | 0.468, p < 0.0001 | 0.515, p < 0.0001 | 0.363, p < 0.0001 |
Total hip T-score * | 0.143, p = 0.16 | 0.441, p < 0.0001 | 0.492, p < 0.0001 | 0.410, p < 0.0001 |
TBS score # | 0.125, p = 0.25 | 0.48, p < 0.0001 | 0.420, p < 0.0001 | 0.291, p = 0.006 |
TBS quality assessment *** | 0.153, p = 0.156 | 0.480, p < 0.0001 | 0.465, p < 0.0001 | 0.341, p < 0.001 |
Lumbar BMD ** | −0.004, p = 0.968 | 0.368, p < 0.0001 | 0.369, p < 0.0001 | 0.240, p = 0.017 |
Femoral neck BMD ** | 0.11, p = 0.28 | 0.478, p < 0.0001 | 0.526, p < 0.0001 | 0.387, p < 0.0001 |
Total hip BMD ** | 0.162, p = 0.11 | 0.428, p < 0.0001 | 0.52, p < 0.0001 | 0.389, p < 0.0001 |
Osteoporosis defined as lumbar T-score ≤ −2.5 SD | 0.028, p = 0.87 | 0.326, p = 0.052 | 0.281, p = 0.098 | 0.002, p = 0.99 |
Pearson’s Correlation † | S | A | M | P |
---|---|---|---|---|
Lumbar T-score * | 0.043, p = 0.67 | 0.387, p < 0.0001 | 0.429, p < 0.0001 | 0.285, p < 0.0001 |
Femoral neck T-score * | 0.079, p = 0.43 | 0.468, p < 0.0001 | 0.515, p < 0.0001 | 0.363, p < 0.0001 |
Total hip T-score * | 0.143, p = 0.15 | 0.441, p < 0.0001 | 0.492, p < 0.0001 | 0.410, p < 0.0001 |
TBS score # | 0.125, p = 0.24 | 0.480, p < 0.0001 | 0.420, p < 0.0001 | 0.291, p = 0.006 |
TBS quality assessment *** | 0.153, p = 0.15 | 0.480, p < 0.0001 | 0.465, p < 0.0001 | 0.341, p < 0.001 |
Lumbar BMD ** | −0.004, p = 0.968 | 0.368, p < 0.0001 | 0.369, p < 0.0001 | 0.240, p = 0.017 |
Femoral neck BMD ** | 0.11, p = 0.28 | 0.478, p < 0.0001 | 0.526, p < 0.0001 | 0.387, p < 0.0001 |
Total hip BMD ** | 0.162, p = 0.11 | 0.428, p < 0.0001 | 0.520, p < 0.0001 | 0.389, p < 0.0001 |
Osteoporosis defined as lumbar T-score ≤ −2.5 SD | 0.028, p = 0.87 | 0.326, p = 0.052 | 0.281, p = 0.098 | 0.002, p = 0.99 |
Osteoporosis defined as femoral neck T-score ≤ −2.5 SD | −0.043, p = 0.81 | 0.29, p = 0.1 | 0.278, p = 0.11 | 0.11, p = 0.53 |
Spearman’s rho | ||||
Lumbar T-score * | 0.166, p = 0.1 | 0.379, p < 0.0001 | 0.437, p < 0.0001 | 0.281, p < 0.0001 |
Femoral neck T-score * | 0.277, p = 0.005 | 0.458, p < 0.0001 | 0.507, p < 0.0001 | 0.331, p < 0.0001 |
Total hip T-score * | 0.311, p = 0.002 | 0.411, p < 0.0001 | 0.477, p < 0.0001 | 0.317, p < 0.001 |
TBS score # | 0.230, p = 0.026 | 0.507, p < 0.0001 | 0.448, p < 0.0001 | 0.296, p < 0.0001 |
TBS quality assessment *** | 0.239, p = 0.026 | 0.492, p < 0.0001 | 0.485, p < 0.0001 | 0.329, p < 0.0001 |
Lumbar BMD ** | 0.10, p = 0.32 | 0.373, p < 0.0001 | 0.373, p < 0.0001 | 0.217, p = 0.032 |
Femoral neck BMD ** | 0.315, p = 0.002 | 0.456, p < 0.0001 | 0.5, p < 0.0001 | 0.339, p < 0.001 |
Total hip BMD ** | 0.355, p < 0.0001 | 0.389, p < 0.0001 | 0.486, p < 0.0001 | 0.320, p < 0.001 |
Osteoporosis defined as lumbar T-score ≤ −2.5 SD | 0.038, p = 0.826 | 0.353, p = 0.034 | 0.327, p = 0.052 | 0.003, p = 0.98 |
Osteoporosis defined as femoral neck T-score ≤ −2.5 SD | 0.1, p = 0.58 | 0.285, p = 0.1 | 0.232, p = 0.194 | −0.002, p = 0.99 |
(a) Linear Regression | |||||
Parameters | Variable | Regression Value | Constant of the Model | Model’s Sig. | |
Lumbar T-score * | S | 0.65, p = 0.675 | −2.07, p < 0.0001 | R2 = 0.002, p = 0.675 | |
Femoral neck T-score * | 0.855, p = 0.77 | −1.63, p < 0.0001 | R2 = 0.006, p = 0.43 | ||
Total hip T-score * | 0.18, p = 0.14 | −1.35, p < 0.0001 | R2 = 0.021, p = 0.15 | ||
TBS score # | 0.016, p = 0.24 | 1.254, p < 0.0001 | R2 = 0.016, p = 0.24 | ||
Lumbar T-score * | A | 0.634, p < 0.0001 | −3.51, p < 0.0001 | R2 = 0.15, p < 0.0001 | |
Femoral neck T-score * | 0.544, p < 0.0001 | −2.82, p < 0.0001 | R2 = 0.219, p < 0.0001 | ||
Total hip T-score * | 0.6, p < 0.0001 | −2.49, p < 0.0001 | R2 = 0.194, p < 0.0001 | ||
TBS score # | 0.006, p < 0.0001 | 1.128, p < 0.0001 | R2 = 0.230, p < 0.0001 | ||
Lumbar T-score * | M | 0.724, p < 0.0001 | −3.58, p < 0.0001 | R2 = −0.184, p < 0.0001 | |
Femoral neck T-score * | 0.625, p < 0.0001 | −2.87, p < 0.0001 | R2 = 0.265, p < 0.0001 | ||
Total hip T-score * | 0.693, p < 0.0001 | −2.56, p < 0.0001 | R2 = 0.242, p < 0.0001 | ||
TBS score # | 0.005, p < 0.0001 | 1.159, p < 0.0001 | R2 = 0.177, p < 0.0001 | ||
Lumbar T-score * | P | 0.52, p = 0.004 | −3.14, p < 0.0001 | R2 = 0.081, p < 0.0001 | |
Femoral neck T-score * | 0.474, p < 0.0001 | −2.54, p < 0.0001 | R2 = 0.132, p < 0.0001 | ||
Total hip T-score * | 0.623, p < 0.0001 | −2.42, p < 0.0001 | R2 = 0.168, p < 0.0001 | ||
TBS score # | 0.004, p = 0.006 | 1.185, p < 0.0001 | R2 = 0.085, p = 0.006 | ||
(b) Logistic Regression | |||||
Parameters | Variable | Odds Ratio | Model’s Sig. | ||
TBS quality assessment *** | S | 1.26, 95% CI (0.74, 2.16) | p = 0.384 | ||
Osteoporosis defined as lumbar T-score ≤ −2.5 SD * | 0.9, 95% CI (0.53, 1.51) | p = 0.69 | |||
Osteoporosis defined as femoral neck T-score ≤ −2.5 SD * | 1.13, 95% CI (0.63, 2.06) | p = 0.68 | |||
Osteoporosis based on AACE criteria ** | 0.79, 95% CI (0.48, 1.28) | p = 0.34 | |||
TBS quality assessment *** | A | 1.14, 95% CI (1.06, 1.21) | p < 0.0001 | ||
Osteoporosis defined as lumbar T-score ≤ −2.5 SD * | 0.92, 95% CI (0.87, 0.97) | p = 0.007 | |||
Osteoporosis defined as femoral neck T-score ≤ −2.5 SD * | 0.89, 95% CI (0.80, 0.97) | p = 0.014 | |||
Osteoporosis based on AACE criteria ** | 0.9, 95% CI (0.85, 0.95) | p < 0.0001 | |||
TBS quality assessment *** | M | 1.14, 95% CI (1.06, 1.22) | p < 0.0001 | ||
Osteoporosis defined as lumbar T-score ≤ −2.5 SD * | 0.89, 95% CI (0.83, 0.95) | p = 0.001 | |||
Osteoporosis defined as femoral neck T-score ≤ −2.5 SD * | 0.83, 95% CI (0.75, 0.94) | p = 0.002 | |||
Osteoporosis based on AACE criteria ** | 0.88, 95% CI (0.82, 0.94) | p < 0.0001 | |||
TBS quality assessment *** | P | 1.1, 95% CI (1.023, 0.174) | p = 0.009 | ||
Osteoporosis defined as lumbar T-score ≤ −2.5 SD * | 0.92, 95% CI (0.86, 0.98) | p = 0.008 | |||
Osteoporosis defined as femoral neck T-score ≤ −2.5 SD * | 0.86, 95% CI (0.76, 0.96) | p = 0.007 | |||
Osteoporosis based on AACE criteria ** | 0.9, 95% CI (0.85, 0.97) | p = 0.002 |
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Poiană, I.R.; Dobre, R.; Pițuru, S.-M.; Bucur, A. The Utility of Radiomorphometric Mandibular Indices on Cone Beam Computer Tomography in the Assessment of Bone Mass in Postmenopausal Women: A Cross-Sectional Study. J. Pers. Med. 2024, 14, 843. https://doi.org/10.3390/jpm14080843
Poiană IR, Dobre R, Pițuru S-M, Bucur A. The Utility of Radiomorphometric Mandibular Indices on Cone Beam Computer Tomography in the Assessment of Bone Mass in Postmenopausal Women: A Cross-Sectional Study. Journal of Personalized Medicine. 2024; 14(8):843. https://doi.org/10.3390/jpm14080843
Chicago/Turabian StylePoiană, Ioana Ruxandra, Ramona Dobre, Silviu-Mirel Pițuru, and Alexandru Bucur. 2024. "The Utility of Radiomorphometric Mandibular Indices on Cone Beam Computer Tomography in the Assessment of Bone Mass in Postmenopausal Women: A Cross-Sectional Study" Journal of Personalized Medicine 14, no. 8: 843. https://doi.org/10.3390/jpm14080843