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利用纹理分析对常规增强多排螺旋 CT(MDCT)进行机会性骨质疏松症筛查的可行性。

Feasibility of opportunistic osteoporosis screening in routine contrast-enhanced multi detector computed tomography (MDCT) using texture analysis.

机构信息

Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore.

Department of Radiology, Klinikum Landshut Achdorf, Landshut, Germany.

出版信息

Osteoporos Int. 2018 Apr;29(4):825-835. doi: 10.1007/s00198-017-4342-3. Epub 2018 Jan 10.


DOI:10.1007/s00198-017-4342-3
PMID:29322221
Abstract

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.

摘要

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。

相似文献

[1]
Feasibility of opportunistic osteoporosis screening in routine contrast-enhanced multi detector computed tomography (MDCT) using texture analysis.

Osteoporos Int. 2018-1-10

[2]
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[3]
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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
Radiomics and machine learning for osteoporosis detection using abdominal computed tomography: a retrospective multicenter study.

BMC Med Imaging. 2025-7-1

[2]
Advanced radiomic prediction of osteoporosis in primary hyperparathyroidism: a machine learning-based analysis of CT images.

Radiol Med. 2025-4-24

[3]
Feasibility study of opportunistic osteoporosis screening on chest CT using a multi-feature fusion DCNN model.

Arch Osteoporos. 2024-10-17

[4]
CT image-based biomarkers for opportunistic screening of osteoporotic fractures: a systematic review and meta-analysis.

Osteoporos Int. 2024-6

[5]
Channel-Boosted and Transfer Learning Convolutional Neural Network-Based Osteoporosis Detection from CT Scan, Dual X-Ray, and X-Ray Images.

J Healthc Eng. 2024

[6]
Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography.

Bioengineering (Basel). 2023-11-27

[7]
The Application of Advanced Bone Imaging Technologies in Sports Medicine.

Radiol Res Pract. 2023-12-4

[8]
Long-term reproducibility of opportunistically assessed vertebral bone mineral density and texture features in routine clinical multi-detector computed tomography using an automated segmentation framework.

Quant Imaging Med Surg. 2023-9-1

[9]
Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features.

Eur Spine J. 2023-12

[10]
Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT.

Front Endocrinol (Lausanne). 2021

本文引用的文献

[1]
Is multidetector CT-based bone mineral density and quantitative bone microstructure assessment at the spine still feasible using ultra-low tube current and sparse sampling?

Eur Radiol. 2017-6-21

[2]
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J Bone Miner Metab. 2017-4-7

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Sci Rep. 2016-12-9

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Dynamic Contrast-enhanced MR Imaging in Renal Cell Carcinoma: Reproducibility of Histogram Analysis on Pharmacokinetic Parameters.

Sci Rep. 2016-7-6

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Phys Med Biol. 2015-7-21

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BMC Med Imaging. 2015-6-26

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Entropy-based adaptive nuclear texture features are independent prognostic markers in a total population of uterine sarcomas.

Cytometry A. 2015-4

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Chin J Cancer Res. 2014-8

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Prediction of bone strength by μCT and MDCT-based finite-element-models: how much spatial resolution is needed?

Eur J Radiol. 2013-11-8

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Computational identification and quantification of trabecular microarchitecture classes by 3-D texture analysis-based clustering.

Bone. 2013-1-10

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