Jansen Mylène P, Wirth Wolfgang, Bacardit Jaume, van Helvoort Eefje M, Marijnissen Anne C A, Kloppenburg Margreet, Blanco Francisco J, Haugen Ida K, Berenbaum Francis, Ladel Cristoph H, Loef Marieke, Lafeber Floris P J G, Welsing Paco M, Mastbergen Simon C, Roemer Frank W
Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands.
Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria.
Quant Imaging Med Surg. 2023 May 1;13(5):3298-3306. doi: 10.21037/qims-22-949. Epub 2023 Mar 10.
In the Innovative Medicine's Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee osteoarthritis (OA) study, machine learning models were trained to predict the probability of structural progression (s-score), predefined as >0.3 mm/year joint space width (JSW) decrease and used as inclusion criterion. The current objective was to evaluate predicted and observed structural progression over 2 years according to different radiographic and magnetic resonance imaging (MRI)-based structural parameters. Radiographs and MRI scans were acquired at baseline and 2-year follow-up. Radiographic (JSW, subchondral bone density, osteophytes), MRI quantitative (cartilage thickness), and MRI semiquantitative [SQ; cartilage damage, bone marrow lesions (BMLs), osteophytes] measurements were obtained. The number of progressors was calculated based on a change exceeding the smallest detectable change (SDC) for quantitative measures or a full SQ-score increase in any feature. Prediction of structural progression based on baseline s-scores and Kellgren-Lawrence (KL) grades was analyzed using logistic regression. Among 237 participants, around 1 in 6 participants was a structural progressor based on the predefined JSW-threshold. The highest progression rate was seen for radiographic bone density (39%), MRI cartilage thickness (38%), and radiographic osteophyte size (35%). Baseline s-scores could only predict JSW progression parameters (most P>0.05), while KL grades could predict progression of most MRI-based and radiographic parameters (P<0.05). In conclusion, between 1/6 and 1/3 of participants showed structural progression during 2-year follow-up. KL scores were observed to outperform the machine-learning-based s-scores as progression predictor. The large amount of data collected, and the wide range of disease stage, can be used for further development of more sensitive and successful (whole joint) prediction models. Trial Registration: Clinicaltrials.gov number NCT03883568.
在创新医学倡议应用公私合作研究促进骨关节炎临床进展（IMI - APPROACH）膝关节骨关节炎（OA）研究中，训练机器学习模型来预测结构进展的概率（s评分），其预定义为关节间隙宽度（JSW）每年减少>0.3毫米，并用作纳入标准。当前目标是根据不同的基于X线和磁共振成像（MRI）的结构参数，评估2年内预测和观察到的结构进展。在基线和2年随访时采集X线片和MRI扫描。获取X线片（JSW、软骨下骨密度、骨赘）、MRI定量（软骨厚度）和MRI半定量[SQ；软骨损伤、骨髓病变（BML）、骨赘]测量值。根据定量测量超过最小可检测变化（SDC）或任何特征的完整SQ评分增加来计算进展者数量。使用逻辑回归分析基于基线s评分和凯尔格伦 - 劳伦斯（KL）分级对结构进展的预测。在237名参与者中，根据预定义的JSW阈值，约六分之一的参与者为结构进展者。X线片骨密度（39%）、MRI软骨厚度（38%）和X线片骨赘大小（35%）的进展率最高。基线s评分仅能预测JSW进展参数（大多数P>0.05），而KL分级能预测大多数基于MRI和X线片的参数进展（P<0.05）。总之，在2年随访期间，六分之一至三分之一的参与者出现结构进展。观察到KL评分作为进展预测指标优于基于机器学习的s评分。收集的大量数据以及广泛的疾病阶段可用于进一步开发更敏感和成功的（全关节）预测模型。试验注册：Clinicaltrials.gov编号NCT03883568。
