Predicting knee osteoarthritis progression using neural network with longitudinal MRI radiomics, and biochemical biomarkers: A modeling study

作者：Ting Wang, Hao Liu, Wenbo Zhao, Peihua Cao, Jia Li, Tianyu Chen, Guangfeng Ruan, Yan Zhang, Xiaoshuai Wang, Qin Dang, Mengdi Zhang, Alexander Tack, David J. Hunter, Changhai Ding, Shengfa Li · 发表于：PLoS Medicine · 年份：2025 · DOI：10.1371/journal.pmed.1004665 · 被引用次数：14 · 研究领域：Osteoarthritis Treatment and Mechanisms、Radiomics and Machine Learning in Medical Imaging、Total Knee Arthroplasty Outcomes

BACKGROUND: Knee osteoarthritis (KOA) worsens both structurally and symptomatically, yet no model predicts KOA progression using Magnetic Resonance Image (MRI) radiomics and biomarkers. This study aimed to develop and test the longitudinal Load-Bearing Tissue Radiomic plus Biochemical biomarker and Clinical variable Model (LBTRBC-M) to predict KOA progression. METHODS AND FINDINGS: Data from the Foundation of the National Institutes of Health Osteoarthritis Biomarkers Consortium were used. We selected 594 participants with Kellgren-Lawrence grades 1-3 and complete biomarker data. The mean age was 61.6 ± 8.9 years, 58.8% were female, and the racial distribution was 79.3% White or White, 18.0% Black or African American, and 2.7% Asian or other non-White. A total of 1,753 knee MRIs were included across the study period, comprising 594 at baseline, 575 at 1-year follow-up, and 584 at 2-year follow-up. Outcomes included (1) both Joint Space Narrowing (JSN) and pain progression (n = 567), (2) only JSN progression (n = 303), (3) only pain progression (n = 295), and (4) non-progression (JSN or pain) (n = 588), corresponding to an approximate ratio of 2:1:1:2. JSN progression was defined as a minimum joint space width (JSW) loss of ≥0.7 mm, and pain progression as a sustained (≥2 time points) increase of ≥9 points on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale (0-100 scale). Using the eXtreme Gradient BOOSTing (XGBOOST) algorithm, the model...