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基于机器学习的纹理分析对骶髂关节炎症性与退行性改变的鉴别诊断。

Differentiation of inflammatory from degenerative changes in the sacroiliac joints by machine learning supported texture analysis.

机构信息

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; Faculty of Medicine, University of Zurich, Switzerland.

Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland; Faculty of Medicine, University of Zurich, Switzerland.

出版信息

Eur J Radiol. 2021 Jul;140:109755. doi: 10.1016/j.ejrad.2021.109755. Epub 2021 May 11.


DOI:10.1016/j.ejrad.2021.109755
PMID:33989966
Abstract

PURPOSE: To compare the diagnostic performance of texture analysis (TA) against visual qualitative assessment in the differentiation of spondyloarthritis (SpA) from degenerative changes in the sacroiliac joints (SIJ). METHOD: Ninety patients referred for suspected inflammatory lower back pain from the rheumatology department were retrospectively included at our university hospital institution. MRI at 3 T of the lumbar spine and SIJ was performed with oblique coronal T1-weighted (w), fluid-sensitive fat-saturated (fs) TIRM and fsT1w intravenously contrast-enhanced (CE) images. Subjects were divided into three age- and gender-matched groups (30 each) based on definite clinical diagnosis serving as clinical reference standard with either degenerative, inflammatory (SpA) or no changes of the SIJ. SIJ were rated qualitatively by two independent radiologists and quantitatively by region-of-interest-based TA with 304 features subjected to machine learning logistic regression with randomized ten-fold selection of training and validation data. Qualitative and quantitative results were evaluated for diagnostic performance and compared against clinical reference standard. RESULTS: Agreement of radiologist's diagnose with clinical reference was fair for both readers (κ = 0.32 and 0.44). ROC statistics revealed significant outperformance of TA compared to qualitative ratings for differentiation of SpA from remainder (AUC = 0.89 vs. 0.75), SpA from degenerative (AUC = 0.91 vs. 0.67) and TIRM-positive SpA (i.e. with bone marrow edema) from remainder cases (AUC = 0.95 vs. 0.76). T1w-CE images were the most important discriminator for detection of SpA. CONCLUSIONS: TA is superior to qualitative assessment for the differentiation of inflammatory from degenerative changes of the SIJ. Intravenous CE-images increase diagnostic yield in quantitative TA.

摘要

目的:比较纹理分析(TA)与视觉定性评估在鉴别骶髂关节(SIJ)的脊柱关节炎(SpA)与退行性改变中的诊断性能。

方法:我们在大学附属医院回顾性纳入了 90 例因疑似炎症性下腰痛而就诊的患者。对腰椎和 SIJ 进行了 3T 的磁共振成像(MRI)检查,包括斜冠状 T1 加权(w)、液体敏感脂肪饱和(fs)TIRM 和 fsT1w 静脉对比增强(CE)图像。根据明确的临床诊断,将患者分为三组,每组 30 例,年龄和性别匹配,作为临床参考标准,分别为 SIJ 无改变、退行性改变和炎症性(SpA)。由两位独立的放射科医生对 SIJ 进行定性评估,并通过基于感兴趣区域的 TA 进行定量评估,采用 304 个特征进行机器学习逻辑回归,随机选择 10 折的训练和验证数据。评估定性和定量结果的诊断性能,并与临床参考标准进行比较。

结果:两位放射科医生的诊断与临床参考标准的一致性为中等(κ=0.32 和 0.44)。ROC 统计显示,TA 对 SpA 的鉴别诊断性能明显优于定性评分(AUC=0.89 比 0.75),SpA 与退行性改变(AUC=0.91 比 0.67),以及 TIRM 阳性的 SpA(即骨髓水肿)与其余病例(AUC=0.95 比 0.76)。T1w-CE 图像是鉴别 SpA 的最主要的鉴别因素。

结论:TA 优于定性评估,可用于鉴别 SIJ 的炎症性与退行性改变。静脉 CE 图像增加了定量 TA 的诊断效能。

相似文献

[1]
Differentiation of inflammatory from degenerative changes in the sacroiliac joints by machine learning supported texture analysis.

Eur J Radiol. 2021-7

[2]
Spine and sacroiliac joints on magnetic resonance imaging in patients with early axial spondyloarthritis: prevalence of lesions and association with clinical and disease activity indices from the Italian group of the SPACE study.

Reumatismo. 2016-9-9

[3]
Does spinal MRI add incremental diagnostic value to MRI of the sacroiliac joints alone in patients with non-radiographic axial spondyloarthritis?

Ann Rheum Dis. 2014-1-22

[4]
Magnetic resonance imaging compared to conventional radiographs for detection of chronic structural changes in sacroiliac joints in axial spondyloarthritis.

J Rheumatol. 2013-7-1

[5]
Improved detection of subchondral erosions in the sacroiliac joints with T1-weighted fat-suppressed MRI.

Eur Radiol. 2021-9

[6]
Prevalence of degenerative and spondyloarthritis-related magnetic resonance imaging findings in the spine and sacroiliac joints in patients with persistent low back pain.

Eur Radiol. 2016-4

[7]
[The specificity and limitations of sacroiliac joint magnetic resonance imaging in the diagnosis of axial spondyloarthritis in patients with chronic low back pain].

Zhonghua Nei Ke Za Zhi. 2016-11-1

[8]
Spondyloarthritis-related and degenerative MRI changes in the axial skeleton--an inter- and intra-observer agreement study.

BMC Musculoskelet Disord. 2013-9-23

[9]
Does evaluation of the ligamentous compartment enhance diagnostic utility of sacroiliac joint MRI in axial spondyloarthritis?

Arthritis Res Ther. 2015-9-13

[10]
Performance of magnetic resonance imaging in the diagnosis of axial spondyloarthritis: a systematic literature review.

Rheumatology (Oxford). 2019-11-1

引用本文的文献

[1]
Application of Artificial Intelligence in rheumatic disease classification: an example of ankylosing spondylitis severity inspection model.

Ann Med. 2025-12

[2]
Evaluating potential for AI automation of quantitative and semi-quantitative MRI scoring in arthritis, especially at the knee: a systematic literature review.

Skeletal Radiol. 2025-3-26

[3]
Machine learning and deep learning for the diagnosis and treatment of ankylosing spondylitis- a scoping review.

J Clin Orthop Trauma. 2024-4-24

[4]
Research on automatic recognition radiomics algorithm for early sacroiliac arthritis based on sacroiliac MRI imaging.

J Orthop Surg Res. 2024-1-30

[5]
Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI.

Diagnostics (Basel). 2023-12-27

[6]
Beyond the horizon: Innovations and future directions in axial-spondyloarthritis.

Arch Rheumatol. 2023-11-22

[7]
Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model.

Arthritis Res Ther. 2023-11-24

[8]
Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review.

RMD Open. 2023-11-23

[9]
Radiomics for the Detection of Active Sacroiliitis Using MR Imaging.

Diagnostics (Basel). 2023-8-3

[10]
Can radiomics replace the SPARCC scoring system in evaluating bone marrow edema of sacroiliac joints in patients with axial spondyloarthritis?

Clin Rheumatol. 2023-6

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