Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Department of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland.
Front Endocrinol (Lausanne). 2023 Jul 17;14:1207949. doi: 10.3389/fendo.2023.1207949. eCollection 2023.
To investigate vertebral osteoporotic fracture (VF) prediction by automatically extracted trabecular volumetric bone mineral density (vBMD) from routine CT, and to compare the model with fracture prevalence-based prediction models.
This single-center retrospective study included patients who underwent two thoraco-abdominal CT scans during clinical routine with an average inter-scan interval of 21.7 ± 13.1 months (range 5-52 months). Automatic spine segmentation and vBMD extraction was performed by a convolutional neural network framework (anduin.bonescreen.de). Mean vBMD was calculated for levels T5-8, T9-12, and L1-5. VFs were identified by an expert in spine imaging. Odds ratios (ORs) for prevalent and incident VFs were calculated for vBMD (per standard deviation decrease) at each level, for baseline VF prevalence (yes/no), and for baseline VF count (n) using logistic regression models, adjusted for age and sex. Models were compared using Akaike's and Bayesian information criteria (AIC & BIC).
420 patients (mean age, 63 years ± 9, 276 males) were included in this study. 40 (25 female) had prevalent and 24 (13 female) had incident VFs. Individuals with lower vBMD at any spine level had higher odds for VFs (L1-5, prevalent VF: OR,95%-CI,p: 2.2, 1.4-3.5,p=0.001; incident VF: 3.5, 1.8-6.9,p<0.001). In contrast, VF status (2.15, 0.72-6.43,p=0.170) and count (1.38, 0.89-2.12,p=0.147) performed worse in incident VF prediction. Information criteria revealed best fit for vBMD-based models (AIC vBMD=165.2; VF status=181.0; count=180.7).
VF prediction based on automatically extracted vBMD from routine clinical MDCT outperforms prediction models based on VF status and count. These findings underline the importance of opportunistic quantitative osteoporosis screening in clinical routine MDCT data.
通过自动从常规 CT 中提取的小梁容积骨密度（vBMD）来预测椎体骨质疏松性骨折（VF），并将该模型与基于骨折患病率的预测模型进行比较。
本单中心回顾性研究纳入了在临床常规中接受两次胸腹部 CT 扫描的患者，两次扫描的平均间隔时间为 21.7±13.1 个月（范围为 5-52 个月）。通过卷积神经网络框架（anduin.bonescreen.de）自动进行脊柱分段和 vBMD 提取。计算 T5-8、T9-12 和 L1-5 水平的平均 vBMD。由脊柱成像专家识别 VF。使用逻辑回归模型，针对每个水平的 vBMD（每标准偏差降低）、基线 VF 患病率（是/否）和基线 VF 计数（n），计算患病率 VF 和新发 VF 的比值比（OR），调整年龄和性别。使用赤池信息量准则（AIC）和贝叶斯信息准则（BIC）比较模型。
本研究纳入了 420 名患者（平均年龄为 63 岁±9 岁，276 名男性）。40 名（25 名女性）患者存在患病率 VF，24 名（13 名女性）患者存在新发 VF。任何脊柱水平的 vBMD 较低的个体发生 VF 的可能性更高（L1-5，患病率 VF：OR，95%-CI，p：2.2，1.4-3.5，p=0.001；新发 VF：3.5，1.8-6.9，p<0.001）。相比之下，VF 状态（2.15，0.72-6.43，p=0.170）和计数（1.38，0.89-2.12，p=0.147）在新发 VF 预测中的表现更差。信息准则表明，基于 vBMD 的模型拟合度最佳（AIC vBMD=165.2；VF 状态=181.0；计数=180.7）。
基于从常规临床 MDCT 中自动提取的 vBMD 的 VF 预测优于基于 VF 状态和计数的预测模型。这些发现强调了在临床常规 MDCT 数据中进行机会性定量骨质疏松症筛查的重要性。
