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Assessing bone mineral density in non-weight bearing regions of the body: a texture analysis approach using abdomen and pelvis computed tomography Hounsfield units-a cross-sectional study.
Kim, Min Woo; Huh, Jung Wook; Noh, Young Min; Seo, Han Eol; Lee, Dong Ha.
Afiliação
  • Kim MW; Department of Orthopedic Surgery, Busan Medical Center, Busan, Republic of Korea.
  • Huh JW; Department of Orthopedic Surgery, Busan Medical Center, Busan, Republic of Korea.
  • Noh YM; Department of Orthopedic Surgery, Busan Medical Center, Busan, Republic of Korea.
  • Seo HE; Department of Orthopedic Surgery, Busan Medical Center, Busan, Republic of Korea.
  • Lee DH; Department of Orthopedic Surgery, Busan Medical Center, Busan, Republic of Korea.
Quant Imaging Med Surg ; 13(11): 7484-7493, 2023 Nov 01.
Article em En | MEDLINE | ID: mdl-37969628
Background: Highlighting a gap in comprehending bone microarchitecture's intricacies using dual-energy X-ray absorptiometry (DXA), this study aims to bridge this chasm by analyzing texture in non-weight bearing regions on axial computed tomography (CT) scans. Our goal is to enrich osteoporosis patient management by enhancing bone quality and microarchitecture insights. Methods: Conducted at Busan Medical Center from March 1, 2013, to August 30, 2022, 1,320 cases (782 patients) were screened. After applying exclusion criteria, 458 samples (296 patients) underwent bone mineral density (BMD) assessment with both CT and DXA. Regions of interest (ROIs) included spine pedicle's maximum trabecular area, sacrum Zone 1, superior/inferior pubic ramus, and femur's greater/lesser trochanters. Texture features (n=45) were extracted from ROIs using gray-level co-occurrence matrices. A regression model predicted BMD, spotlighting the top five influential texture features. Results: Correlation coefficients ranged from 0.709 (lowest for total femur BMD) to 0.804 (highest for femur intertrochanter BMD). Mean squared error (MSE) values were also provided for lumbar and femur BMD/bone mineral content (BMC) metrics. The most influential texture features included contrast_32, correlation_32_v, and three other metrics. Conclusions: By melding traditional DXA and CT texture analysis, our approach presents a comprehensive bone health perspective, potentially revolutionizing osteoporosis diagnostics.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article