使用多维拓扑数据分析来识别髋关节骨关节炎的特征。
Using multidimensional topological data analysis to identify traits of hip osteoarthritis.
机构信息
Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco, California, USA.
出版信息
J Magn Reson Imaging. 2018 Oct;48(4):1046-1058. doi: 10.1002/jmri.26029. Epub 2018 May 7.
BACKGROUND
Osteoarthritis (OA) is a multifaceted disease with many variables affecting diagnosis and progression. Topological data analysis (TDA) is a state-of-the-art big data analytics tool that can combine all variables into multidimensional space. TDA is used to simultaneously analyze imaging and gait analysis techniques.
PURPOSE
To identify biochemical and biomechanical biomarkers able to classify different disease progression phenotypes in subjects with and without radiographic signs of hip OA.
STUDY TYPE
Longitudinal study for comparison of progressive and nonprogressive subjects.
POPULATION
In all, 102 subjects with and without radiographic signs of hip osteoarthritis.
FIELD STRENGTH/SEQUENCE: 3T, SPGR 3D MAPSS T /T , intermediate-weighted fat-suppressed fast spin-echo (FSE).
ASSESSMENT
Multidimensional data analysis including cartilage composition, bone shape, Kellgren-Lawrence (KL) classification of osteoarthritis, scoring hip osteoarthritis with MRI (SHOMRI), hip disability and osteoarthritis outcome score (HOOS).
STATISTICAL TESTS
Analysis done using TDA, Kolmogorov-Smirnov (KS) testing, and Benjamini-Hochberg to rank P-value results to correct for multiple comparisons.
RESULTS
Subjects in the later stages of the disease had an increased SHOMRI score (P < 0.0001), increased KL (P = 0.0012), and older age (P < 0.0001). Subjects in the healthier group showed intact cartilage and less pain. Subjects found between these two groups had a range of symptoms. Analysis of this subgroup identified knee biomechanics (P < 0.0001) as an initial marker of the disease that is noticeable before the morphological progression and degeneration. Further analysis of an OA subgroup with femoroacetabular impingement (FAI) showed anterior labral tears to be the most significant marker (P = 0.0017) between those FAI subjects with and without OA symptoms.
DATA CONCLUSION
The data-driven analysis obtained with TDA proposes new phenotypes of these subjects that partially overlap with the radiographic-based classical disease status classification and also shows the potential for further examination of an early onset biomechanical intervention.
LEVEL OF EVIDENCE
2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1046-1058.
背景
骨关节炎（OA）是一种具有多种变量的多方面疾病，这些变量会影响诊断和疾病进展。拓扑数据分析（TDA）是一种先进的大数据分析工具，可将所有变量组合到多维空间中。TDA 用于同时分析影像学和步态分析技术。
目的
确定能够将有和没有放射学征象的髋 OA 患者的不同疾病进展表型分类的生化和生物力学生物标志物。
研究类型
比较有进展和无进展受试者的纵向研究。
人群
共 102 例有和无髋 OA 放射学征象的受试者。
磁场强度/序列：3T，SPGR 3D MAPSS T/T，中等权重脂肪抑制快速自旋回波（FSE）。
评估
包括软骨成分、骨形态、Kellgren-Lawrence（KL）骨关节炎分类、磁共振成像髋关节骨关节炎评分（SHOMRI）、髋关节残疾和骨关节炎结果评分（HOOS）在内的多维数据分析。
统计检验
使用 TDA、柯尔莫哥洛夫-斯米尔诺夫（KS）检验和 Benjamini-Hochberg 进行分析，对 P 值结果进行排序以纠正多重比较。
结果
疾病晚期的受试者 SHOMRI 评分较高（P<0.0001）、KL 分级较高（P=0.0012）和年龄较大（P<0.0001）。健康组的受试者软骨完整，疼痛较轻。处于这两个组之间的受试者表现出各种症状。对该亚组的分析确定膝关节生物力学（P<0.0001）是疾病的初始标志物，在形态学进展和退变之前就已出现。对伴有股骨髋臼撞击（FAI）的 OA 亚组的进一步分析表明，前侧盂唇撕裂是 FAI 患者中有无 OA 症状的最显著标志物（P=0.0017）。
数据结论
通过 TDA 获得的数据驱动分析提出了这些受试者的新表型，这些表型部分与基于放射学的经典疾病状态分类重叠，也显示了进一步研究早期生物力学干预的潜力。
证据水平
2 技术功效：2 级。J. Magn. Reson. Imaging 2018;48:1046-1058.
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