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基于双能骨质成像的 CT 纹理分析在多发性骨髓瘤特征描述中的作用:与组织学和已建立的血清学参数的比较。

Role of computed tomography texture analysis using dual-energy-based bone marrow imaging for multiple myeloma characterization: comparison with histology and established serologic parameters.

机构信息

Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.

Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany.

出版信息

Eur Radiol. 2021 Apr;31(4):2357-2367. doi: 10.1007/s00330-020-07320-8. Epub 2020 Oct 3.

DOI:10.1007/s00330-020-07320-8
PMID:33011876
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7979667/
Abstract

OBJECTIVE

To identify textural features on dual-energy CT (DECT)-based bone marrow images in myeloma which correlate with serum markers of myeloma activity and the degree of medullary involvement.

METHODS

A total of 110 patients (63.0 ± 11.0 years, 51 female) who underwent unenhanced whole-body DECT between September 2015 and February 2019 were retrospectively included, which was approved by our institutional ethics committee with a waiver of the informed consent requirement. All patients had current hematologic laboratory tests. Using DECT post-processing, non-calcium bone marrow images were reconstructed. The vertebral bodies T10-L5 were segmented for quantification of textural features, which were compared with serologic parameters and myeloma stages by the Mann-Whitney U test. In a subgroup of 56/110 patients with current bone marrow biopsies, textural features were correlated with the degree of bone marrow infiltration.

RESULTS

First-order features were higher in patients with advanced stage of myeloma (p < .02), whereas the 2nd-order "gray-level co-occurrence matrix (GLCM) cluster prominence" was lower (p < .04). In patients with elevated serum-free light chains (SFLC) or kappa/lambda SFLC ratio above 1.56, the "entropy" and 2nd-order GLCM features were lower (p < .03). The degree of bone marrow infiltration correlated with 1st-order features (e.g., "uniformity"; r = 0.49; p < .0001), whereas "entropy" and 2nd-order GLCM features were negatively correlated (e.g., "difference entropy"; r = - 0.54; p < .0001).

CONCLUSIONS

CT textural features applied on non-calcium bone marrow images correlate well with myeloma-related serologic parameters and histology showing a more uniform tissue structure and higher attenuation with increasing medullary infiltration and could therefore be used as additional imaging biomarkers for non-invasive assessment of medullary involvement.

KEY POINTS

• Texture analysis applied on dual-energy reconstructed non-calcium bone marrow images provides information about marrow structure and attenuation. • Myeloma-related serologic parameters and the degree of myeloma cell infiltration correlate with 1st- and 2nd-order features which could be useful as additional imaging biomarkers for non-invasive assessment of medullary involvement.

摘要

目的

确定多发性骨髓瘤患者基于双能 CT(DECT)的骨髓图像中的纹理特征与多发性骨髓瘤活动的血清标志物和骨髓受累程度相关。

方法

回顾性纳入 2015 年 9 月至 2019 年 2 月期间进行全身增强 DECT 的 110 例患者(63.0±11.0 岁,51 例女性),该研究获得了我们机构伦理委员会的批准,并豁免了知情同意书的要求。所有患者均进行了当前的血液学实验室检查。使用 DECT 后处理,重建非钙骨髓图像。对 T10-L5 椎体进行分段,以定量评估纹理特征,并通过 Mann-Whitney U 检验将其与血清学参数和多发性骨髓瘤分期进行比较。在 110 例具有当前骨髓活检的患者亚组中,纹理特征与骨髓浸润程度相关。

结果

高级别多发性骨髓瘤患者的一阶特征更高(p<0.02),而二阶“灰度共生矩阵(GLCM)聚类显着性”较低(p<0.04)。血清游离轻链(SFLC)升高或 κ/λ SFLC 比值大于 1.56 的患者,“熵”和二阶 GLCM 特征较低(p<0.03)。骨髓浸润程度与一阶特征相关(例如,“均匀性”;r=0.49;p<0.0001),而“熵”和二阶 GLCM 特征呈负相关(例如,“差异熵”;r= -0.54;p<0.0001)。

结论

应用于非钙骨髓图像的 CT 纹理特征与多发性骨髓瘤相关的血清学参数和组织学相关性良好,表现出更均匀的组织结构和更高的衰减,随着骨髓浸润程度的增加,可作为骨髓受累无创评估的额外成像生物标志物。

关键要点

  • 应用于双能重建非钙骨髓图像的纹理分析提供了有关骨髓结构和衰减的信息。

  • 与多发性骨髓瘤相关的血清学参数和骨髓瘤细胞浸润程度与一阶和二阶特征相关,可作为骨髓受累无创评估的额外成像生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/00cb13071ea7/330_2020_7320_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/1dd8eae30f8e/330_2020_7320_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/5c25770c34c3/330_2020_7320_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/311f8abee2da/330_2020_7320_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/4fa8340bb892/330_2020_7320_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/91bb6adc8d01/330_2020_7320_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/de0793eba060/330_2020_7320_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/00cb13071ea7/330_2020_7320_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/1dd8eae30f8e/330_2020_7320_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/5c25770c34c3/330_2020_7320_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/311f8abee2da/330_2020_7320_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/4fa8340bb892/330_2020_7320_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/91bb6adc8d01/330_2020_7320_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/de0793eba060/330_2020_7320_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dde4/7979667/00cb13071ea7/330_2020_7320_Fig7_HTML.jpg

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