Integration of longitudinal load-bearing tissue MRI radiomics and neural network to predict knee osteoarthritis incidence

作者：Tianyu Chen, Jian Chen, Hao Liu, Zhengrui Liu, Bin Yu, Yang Wang, Wenbo Zhao, Yin-xiao Peng, Jun Li, Yun Yang, Huilin Wan, Xing Wang, Zhong Zhang, Deng Zhao, Lan Chen, Lili Chen, Ruyu Liao, Shanhong Liu, Guowei Zeng, Zhijia Wen, Yin Wang, Li Xu, Shengjie Wang, Haixiong Miao, Wei Chen, Yanbin Zhu, Xiaogang Wang, Changhai Ding, Ting Wang, Shengfa Li, Yingze Zhang · 发表于：Journal of Orthopaedic Translation · 年份：2025 · DOI：10.1016/j.jot.2025.01.007 · 被引用次数：7 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Osteoarthritis Treatment and Mechanisms、Total Knee Arthroplasty Outcomes

Background: Load-bearing structural degradation is crucial in knee osteoarthritis (KOA) progression, yet limited prediction models use load-bearing tissue radiomics for radiographic (structural) KOA incident. Purpose: We aim to develop and test a Load-Bearing Tissue plus Clinical variable Radiomic Model (LBTC-RM) to predict radiographic KOA incidents. Study design: Risk prediction study. Methods: The 700 knees without radiographic KOA at baseline were included from Osteoarthritis Initiative cohort. We selected 2164 knee MRIs during 4-year follow-up. LBTC-RM, which integrated MRI features of meniscus, femur, tibia, femorotibial cartilage, and clinical variables, was developed in total development cohort (n = 1082, 542 cases vs. 540 controls) using neural network algorithm. Final predictive model was tested in total test cohort (n = 1082, 534 cases vs. 548 controls), which integrated data from five visits: baseline (n = 353, 191 cases vs. 162 controls), 3 years prior KOA (n = 46, 19 cases vs. 27 controls), 2 years prior KOA (n = 143, 77 cases vs. 66 controls), 1 year prior KOA (n = 220, 105 cases vs. 115 controls), and at KOA incident (n = 320, 156 cases vs. 164 controls). Results: In total test cohort, LBTC-RM predicted KOA incident with AUC (95 % CI) of 0.85 (0.82-0.87); with LBTC-RM aid, performance of resident physicians for KOA prediction were improved, with specificity, sensitivity, and accuracy increasing from 50 %, 60 %, and 55 %-72 %, 73 %, and 72 %, respectively. The ...