Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore.
Department of Radiology, Klinikum Landshut Achdorf, Landshut, Germany.
Osteoporos Int. 2018 Apr;29(4):825-835. doi: 10.1007/s00198-017-4342-3. Epub 2018 Jan 10.
This study investigated the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis. The results showed an acceptable reproducibility of texture features, and these features could discriminate healthy/osteoporotic fracture cohort with an accuracy of 83%.
This aim of this study is to investigate the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis.
We performed texture analysis at the spine in routine MDCT exams and investigated the effect of intravenous contrast medium (IVCM) (n = 7), slice thickness (n = 7), the long-term reproducibility (n = 9), and the ability to differentiate healthy/osteoporotic fracture cohort (n = 9 age and gender matched pairs). Eight texture features were extracted using gray level co-occurrence matrix (GLCM). The independent sample t test was used to rank the features of healthy/fracture cohort and classification was performed using support vector machine (SVM).
The results revealed significant correlations between texture parameters derived from MDCT scans with and without IVCM (r up to 0.91) slice thickness of 1 mm versus 2 and 3 mm (r up to 0.96) and scan-rescan (r up to 0.59). The performance of the SVM classifier was evaluated using 10-fold cross-validation and revealed an average classification accuracy of 83%.
Opportunistic osteoporosis screening at the spine using specific texture parameters (energy, entropy, and homogeneity) and SVM can be performed in routine contrast-enhanced MDCT exams.
本研究旨在探讨利用纹理分析在常规增强 MDCT 检查中进行机会性骨质疏松筛查的可行性。结果表明，纹理特征具有可接受的重现性，这些特征可以以 83%的准确率区分健康/骨质疏松性骨折组。
本研究旨在探讨利用纹理分析在常规增强 MDCT 检查中进行机会性骨质疏松筛查的可行性。
我们在常规 MDCT 检查中对脊柱进行纹理分析，并研究了静脉内对比剂（IVCM）（n=7）、层厚（n=7）、长期重现性（n=9）和区分健康/骨质疏松性骨折组（n=9 对年龄和性别匹配的患者）的能力。使用灰度共生矩阵（GLCM）提取了 8 个纹理特征。采用独立样本 t 检验对健康/骨折组的特征进行排序，并采用支持向量机（SVM）进行分类。
结果显示，来自有/无 IVCM 的 MDCT 扫描的纹理参数之间存在显著相关性（r 高达 0.91），1mm 与 2mm 和 3mm 层厚之间的相关性（r 高达 0.96），以及扫描-扫描之间的相关性（r 高达 0.59）。SVM 分类器的性能通过 10 倍交叉验证进行评估，平均分类准确率为 83%。
在常规增强 MDCT 检查中，可以使用特定的纹理参数（能量、熵和同质性）和 SVM 在脊柱上进行机会性骨质疏松筛查。
