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Eur J Radiol. 2019 Jul;116:212-218. doi: 10.1016/j.ejrad.2019.05.009. Epub 2019 May 8.

Using texture analysis of head CT images to differentiate osteoporosis from normal bone density.

Author information

1
Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston Massachusetts, United States; Department of Radiology, Nihon University School of Dentistry at Matsudo, Chiba, Japan.
2
Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston Massachusetts, United States; Department of Radiology, Jichi Medical University School of Medicine, Shimotsuke, Tochigi, Japan.
3
Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston Massachusetts, United States.
4
Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston Massachusetts, United States; Department of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Massachusetts, United States.
5
Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston Massachusetts, United States; Department of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Massachusetts, United States; Department of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, Boston Massachusetts, United States. Electronic address: osamu.sakai@bmc.org.

Abstract

OBJECTIVES:

To investigate the use of texture analysis for the detection of osteoporosis on noncontrast head CTs, and to explore optimal sampling regions within the craniofacial bones.

METHODS:

In this IRB-approved, retrospective study, the clivus, bilateral sphenoid triangles and mandibular condyles were manually segmented on each noncontrast head CT, and 41 textures features were extracted from 29 patients with normal bone density (NBD); and 29 patients with osteoporosis. Basic descriptive statistics including a false discovery rate correction were performed to evaluate for differences in texture features between the cohorts.

RESULS:

Sixteen texture features demonstrated significant differences (P < 0.01) between NBD and osteoporosis in the clivus including 4 histogram features, 2 gray-level co-occurrence matrix features, 8 gray-level run-length features and 2 Law's features. Nineteen texture features including 9 histogram features, 1 GLCM features, 2 GLRL features, 5 Law's features and 2 GLGM features demonstrated statistically significant differences in both sides of the sphenoid triangles. A total 24 texture features demonstrated statistically significant differences between normal BMD and osteoporosis in the left sphenoid and a total of 31 texture features in the left condyle. Furthermore, a total of 22 texture features including 6 histogram features, 3 GLCM features, 9 GLRL features, 2 Law's features and 2 GLGM features demonstrated statistically significant differences in both sides of the mandibular condyles.

CONCLUSION:

The results of this investigation suggest that specific texture analysis features derived from regions of interest placed within multiple sites within the skull base and maxillofacial bones can distinguish between patients with normal bone mineral density compared to those with osteoporosis. This study demonstrates the potential utility of a texture analysis for identification of osteoporosis on head CT, which may help identify patients who have not undergone screening with traditional DXA.

KEYWORDS:

CT; Craniofacial bones; Normal bone density; Osteoporosis; Texture analysis

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