膝关节骨关节炎的软骨下骨长度：一种深度学习衍生的影像学测量及其与影像学和临床结果的关系。
Subchondral Bone Length in Knee Osteoarthritis: A Deep Learning-Derived Imaging Measure and Its Association With Radiographic and Clinical Outcomes.
机构信息
Boston University, Boston, Massachusetts.
Harvard University and Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
出版信息
Arthritis Rheumatol. 2021 Dec;73(12):2240-2248. doi: 10.1002/art.41808. Epub 2021 Oct 29.
OBJECTIVE
To develop a bone shape measure that reflects the extent of cartilage loss and bone flattening in knee osteoarthritis (OA) and test it against estimates of disease severity.
METHODS
A fast region-based convolutional neural network was trained to crop the knee joints in sagittal dual-echo steady-state magnetic resonance imaging sequences obtained from the Osteoarthritis Initiative (OAI). Publicly available annotations of the cartilage and menisci were used as references to annotate the tibia and the femur in 61 knees. Another deep neural network (U-Net) was developed to learn these annotations. Model predictions were compared to radiologist-driven annotations on an independent test set (27 knees). The U-Net was applied to automatically extract the knee joint structures on the larger OAI data set (n = 9,434 knees). We defined subchondral bone length (SBL), a novel shape measure characterizing the extent of overlying cartilage and bone flattening, and examined its relationship with radiographic joint space narrowing (JSN), concurrent pain and disability (according to the Western Ontario and McMaster Universities Osteoarthritis Index), as well as subsequent partial or total knee replacement. Odds ratios (ORs) and 95% confidence intervals (95% CIs) for each outcome were estimated using relative changes in SBL from the OAI data set stratified into quartiles.
RESULTS
The mean SBL values for knees with JSN were consistently different from knees without JSN. Greater changes of SBL from baseline were associated with greater pain and disability. For knees with medial or lateral JSN, the ORs for future knee replacement between the lowest and highest quartiles corresponding to SBL changes were 5.68 (95% CI 3.90-8.27) and 7.19 (95% CI 3.71-13.95), respectively.
CONCLUSION
SBL quantified OA status based on JSN severity and shows promise as an imaging marker in predicting clinical and structural OA outcomes.
目的
开发一种反映膝关节骨关节炎（OA）软骨丢失和骨面扁平程度的骨形态测量方法，并对其进行疾病严重程度的评估。
方法
利用快速区域卷积神经网络对来自骨关节炎倡议（OAI）的矢状双回波稳态磁共振成像序列中的膝关节进行裁剪。利用公共的软骨和半月板注释作为参考，对 61 个膝关节的胫骨和股骨进行注释。另一个深度神经网络（U-Net）被开发用于学习这些注释。模型预测与独立测试集（27 个膝关节）上的放射科医生驱动注释进行比较。U-Net 应用于自动提取更大的 OAI 数据集（n=9434 个膝关节）上的膝关节结构。我们定义了软骨下骨长度（SBL），这是一种新的形态测量方法，用于描述覆盖软骨和骨面扁平的程度，并研究了其与放射学关节间隙狭窄（JSN）、并发疼痛和残疾（根据西安大略大学和麦克马斯特大学骨关节炎指数）以及随后的部分或全膝关节置换的关系。使用 OAI 数据集分层为四分位数的 SBL 相对变化，估计每个结果的比值比（OR）和 95%置信区间（95%CI）。
结果
有 JSN 的膝关节的平均 SBL 值与没有 JSN 的膝关节明显不同。SBL 从基线的变化越大，疼痛和残疾程度越大。对于内侧或外侧 JSN 的膝关节，最低和最高四分位数对应的 SBL 变化的未来膝关节置换的 OR 分别为 5.68（95%CI 3.90-8.27）和 7.19（95%CI 3.71-13.95）。
结论
SBL 根据 JSN 严重程度量化 OA 状态，并有望成为预测临床和结构 OA 结局的影像学标志物。
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