Yang Hui, Li Jiang, Zheng Xiuzhu, Su Datian, Jia Cheng, Qin Jian, Zhang Quan
Department of Medical Imaging, Tianjin Key Laboratory of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, People's Republic of China.
Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian, People's Republic of China.
Clin Interv Aging. 2025 Sep 13;20:1561-1569. doi: 10.2147/CIA.S538123. eCollection 2025.
There is an urgent need for a convenient and incidental method to assess the bone health status of the population, especially in primary-level hospitals lacking specialized bone density testing equipment. This study aims to investigate the association between multiple vertebral Hounsfield Unit (HU) value clusters and bone mass subtypes using an unsupervised learning approach, providing a practical tool for incidental osteoporosis screening in clinical settings.
This retrospective study included subjects who underwent chest CT and quantitative CT (QCT) from January 2023 to December 2024. Vertebral HU values (T7-T12) were measured on chest CT images. Intergroup comparisons (normal, osteopenia, and osteoporosis) in clinical findings and CT values were performed using Pearson χ test and one-way analysis of variance. An unsupervised -means clustering was applied to vertebral CT values across the cohort.
The study comprised 455 participants (260 males, 195 females) with a median age of 60 years (interquartile range, 51-67 years), who were classified into three groups: normal bone mass, 253 cases; osteopenia, 152 cases; osteoporosis, 50 cases. Among 455 participants, age inversely correlated with bone mass. Vertebrae HU values (T7-T12) exhibited significant stepwise declines from normal to osteopenia to osteoporosis (OP) groups. The clustering analysis revealed five distinct subtypes: cluster 1 strongly correlated with OP (45 of 72 cases), cluster 4 with osteopenia (107 of 146 cases), and clusters 2, 3, and 5 with normal bone mass (31 of 31 cases; 90 of 107 cases; 97 of 99 cases).
Unsupervised clustering of T7-T12 vertebral HU values effectively stratifies bone mass subtypes, offering an efficient, CT-based screening method for skeletal health assessment, especially valuable in resource-limited primary-level hospitals lacking dedicated bone densitometry.
迫切需要一种便捷且偶然的方法来评估人群的骨骼健康状况，尤其是在缺乏专业骨密度检测设备的基层医院。本研究旨在采用无监督学习方法研究多个椎体Hounsfield单位（HU）值簇与骨量亚型之间的关联，为临床环境中偶然骨质疏松筛查提供实用工具。
这项回顾性研究纳入了2023年1月至2024年12月期间接受胸部CT和定量CT（QCT）检查的受试者。在胸部CT图像上测量椎体HU值（T7 - T12）。使用Pearson χ检验和单因素方差分析对临床结果和CT值进行组间比较（正常、骨质减少和骨质疏松）。对整个队列的椎体CT值应用无监督均值聚类。
该研究包括455名参与者（260名男性，195名女性），中位年龄为60岁（四分位间距，51 - 67岁），分为三组：正常骨量，253例；骨质减少，152例；骨质疏松，50例。在455名参与者中，年龄与骨量呈负相关。椎体HU值（T7 - T12）从正常组到骨质减少组再到骨质疏松组呈现出显著的逐步下降。聚类分析揭示了五种不同的亚型：簇1与骨质疏松密切相关（72例中的45例），簇4与骨质减少相关（146例中的107例），簇2、3和5与正常骨量相关（31例中的31例；107例中的90例；99例中的97例）。
T7 - T12椎体HU值的无监督聚类有效地对骨量亚型进行了分层，为骨骼健康评估提供了一种基于CT的高效筛查方法，在缺乏专用骨密度测定仪的资源有限的基层医院尤其有价值。
