PDF(Pubmed)
Abstract：
Osteoporosis is underdiagnosed, especially in ethnic and racial minorities who are thought to be protected against bone loss, but often have worse outcomes after an osteoporotic fracture. We aimed to determine the prevalence of osteoporosis by opportunistic CT in patients who underwent lung cancer screening (LCS) using non-contrast CT in the Northeastern United States. Demographics including race and ethnicity were retrieved. We assessed trabecular bone and body composition using a fully-automated artificial intelligence algorithm. ROIs were placed at T12 vertebral body for attenuation measurements in Hounsfield Units (HU). Two validated thresholds were used to diagnose osteoporosis: high-sensitivity threshold (115-165 HU) and high specificity threshold (<115 HU). We performed descriptive statistics and ANOVA to compare differences across sex, race, ethnicity, and income class according to neighborhoods\' mean household incomes. Forward stepwise regression modeling was used to determine body composition predictors of trabecular attenuation. We included 3708 patients (mean age 64 ± 7 years, 54 % males) who underwent LCS, had available demographic information and an evaluable CT for trabecular attenuation analysis. Using the high sensitivity threshold, osteoporosis was more prevalent in females (74 % vs. 65 % in males, p < 0.0001) and Whites (72 % vs 49 % non-Whites, p < 0.0001). However, osteoporosis was present across all races (38 % Black, 55 % Asian, 56 % Hispanic) and affected all income classes (69 %, 69 %, and 91 % in low, medium, and high-income class, respectively). High visceral/subcutaneous fat-ratio, aortic calcification, and hepatic steatosis were associated with low trabecular attenuation (p < 0.01), whereas muscle mass was positively associated with trabecular attenuation (p < 0.01). In conclusion, osteoporosis is prevalent across all races, income classes and both sexes in patients undergoing LCS. Opportunistic CT using a fully-automated algorithm and uniform imaging protocol is able to detect osteoporosis and body composition without additional testing or radiation. Early identification of patients traditionally thought to be at low risk for bone loss will allow for initiating appropriate treatment to prevent future fragility fractures. CLINICALTRIALS.GOV IDENTIFIER: N/A.
摘要：
骨质疏松未被诊断,特别是在被认为可以防止骨质流失的少数民族和种族中，但通常在骨质疏松性骨折后结局更差。我们旨在通过机会性CT在美国东北部使用非对比CT进行肺癌筛查（LCS）的患者中确定骨质疏松症的患病率。检索了包括种族和族裔在内的人口统计数据。我们使用全自动人工智能算法评估骨小梁和身体成分。将ROI放置在T12椎体以Hounsfield单位（HU）进行衰减测量。使用两个经过验证的阈值来诊断骨质疏松症：高灵敏度阈值（115-165HU）和高特异性阈值（<115HU）。我们进行了描述性统计和方差分析来比较不同性别的差异，种族,种族,和收入阶层根据社区的意思是家庭收入。使用正向逐步回归模型来确定小梁衰减的身体成分预测因子。我们纳入了3708例患者(平均年龄64±7岁，54%的男性)接受LCS，具有可用的人口统计信息和可评估的CT用于小梁衰减分析。使用高灵敏度阈值，骨质疏松症在女性中更为普遍(74%vs.65%的男性,p<0.0001)和白人(72%vs49%非白人，p<0.0001)。然而,骨质疏松症存在于所有种族中(38%是黑人，55%亚洲人，56%的西班牙裔)，并影响所有收入阶层(69%，69%,91%处于低位，中等,和高收入阶层，分别)。高内脏/皮下脂肪比，主动脉钙化,和肝脂肪变性与低小梁衰减相关（p<0.01），而肌肉质量与小梁衰减呈正相关(p<0.01)。总之,骨质疏松症在所有种族中都很普遍，接受LCS的患者的收入阶层和两性。使用全自动算法和统一成像协议的机会性CT能够检测骨质疏松症和身体成分，而无需额外的测试或辐射。早期识别传统上认为骨丢失风险较低的患者将允许开始适当的治疗以防止未来的脆性骨折。临床医师。GOVIDENTIFIER:N/A.
