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.
UNLABELLED: 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%. INTRODUCTION: This aim of this study is to investigate the feasibility of opportunistic osteoporosis screening in routine contrast-enhanced MDCT exams using texture analysis. METHODS: 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). RESULTS: 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%. CONCLUSIONS: 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。
引言: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。
方法: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: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%。
结论: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。
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