Department of Orthopedic Surgery, Busan Medical Center, Busan, Republic of Korea.
School of Biomedical Convergence Engineering, Pusan National University, Yangsan, Republic of Korea.
Curr Med Imaging. 2023;19(10):1186-1195. doi: 10.2174/1573405619666221116115206.
This study aimed to establish an academic basis for using a computed tomography (CT) model for predicting osteoporosis in the clinical setting by illustrating the effectiveness of morphometric texture analysis. We introduce texture analysis and quantitative approaches using CT Hounsfield units (HU) to screen osteoporosis.
From March 6, 2013, to August 11, 2020, a total of 4,333 cases (1,766 patients) were included in the study. After applying exclusion criteria concerning the patient status and scan interval between CT and DXA, we selected only 1,647 samples (736 patients) and analyzed both their CT and DXA bone mineral density (BMD) results. BMD was measured in the femoral neck and L1 spine body. A region of interest (ROI) was extracted from each patient's CT as the maximum trabecular area of the L1 spine body and femoral neck. A total of 45 texture features were extracted from every ROI using gray-level co-occurrence matrices. Machine-learning techniques, including linear regression (LR) and artificial neural network (ANN), were applied to predict BMD.
We assigned samples to (1) Set 1 (857 lumbar spine samples in chest model, L1 spine DXA BMD), (2) Set 2 (392 lumbar spine samples in lumbar spine CT model, L1 spine DXA BMD), (3) Set 3 (1,249 lumbar spine samples in both chest and lumbar spine CT model, L1 spine DXA BMD), (4) Set 4 (398 femoral neck samples in hip and pelvis CT model, femoral neck DXA BMD), and (5) Set 5 (a total of 1,647 samples). When we applied LR, the correlation coefficients between estimated and reference values for Sets 1, 2, 3, and 4 were 0.783, 0.784, 0.757, and 0.652, respectively. For total samples (Set 5), LR and ANN provided correlation coefficients of 0.707 and 0.782, respectively.
The modality using morphometric texture analysis with CT HU can be an additional diagnostic tool for osteoporosis and an alternative for DXA.
通过阐述形态纹理分析在预测骨质疏松症方面的有效性，为在临床中使用计算机断层扫描（CT）模型预测骨质疏松症建立学术基础。我们引入纹理分析和使用 CT 亨氏单位（HU）的定量方法来筛查骨质疏松症。
从 2013 年 3 月 6 日至 2020 年 8 月 11 日，共有 4333 例（1766 例患者）纳入研究。在排除患者状态和 CT 与 DXA 之间的扫描间隔的排除标准后，我们仅选择了 1647 例样本（736 例患者），并分析了他们的 CT 和 DXA 骨密度（BMD）结果。BMD 分别在股骨颈和 L1 椎体测量。从每位患者的 CT 中提取感兴趣区域（ROI），作为 L1 椎体和股骨颈最大的骨小梁区域。使用灰度共生矩阵从每个 ROI 中提取了 45 个纹理特征。采用线性回归（LR）和人工神经网络（ANN）等机器学习技术来预测 BMD。
我们将样本分为（1）Set1（857 例胸部模型 L1 脊柱 DXA BMD 的腰椎样本）、（2）Set2（392 例腰椎 CT 模型 L1 脊柱 DXA BMD 的腰椎样本）、（3）Set3（1249 例胸部和腰椎 CT 模型的腰椎样本，L1 脊柱 DXA BMD）、（4）Set4（398 例髋关节和骨盆 CT 模型的股骨颈样本，股骨颈 DXA BMD）和（5）Set5（共 1647 例样本）。当我们应用 LR 时，Set1、2、3 和 4 的估计值与参考值之间的相关系数分别为 0.783、0.784、0.757 和 0.652。对于总样本（Set5），LR 和 ANN 分别提供了 0.707 和 0.782 的相关系数。
使用 CT HU 进行形态纹理分析的方式可以成为骨质疏松症的另一种诊断工具，也是 DXA 的替代方法。
