Sun Hongbiao, You Yi, Jiang Qinling, Ma Yanqing, Huang Chencui, Liu Xiaoqing, Xu Shaochun, Wang Wenwen, Wang Zhenhuan, Wang Xiang, Xue Ting, Liu Shiyuan, Zhu Lei, Xiao Yi
Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai 200003, China.
Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing 100089, China.
Eur J Radiol. 2025 Jan;182:111854. doi: 10.1016/j.ejrad.2024.111854. Epub 2024 Nov 28.
The incidence of total knee replacement (TKR) surgeries has increased, partly attributed to healthcare policies that cause premature and potentially unwarranted interventions. This has raised concerns regarding a potential trend of excessive surgeries.
This study aimed to propose a predictive model based on digital radiography (DR) radiomics to objectively assess the need for TKR surgery in patients with knee osteoarthritis (KOA) and to improve risk stratification, thereby avoiding unnecessary surgeries.
A retrospective study was conducted on 1,785 KOA patients from January 2017 to December 2022. Radiomics features were extracted from DR images to quantify lesion phenotypes, followed by a two-step feature selection to derive robust signatures. Multiple models were constructed using independent risk factors and radiomics features, and these models were validated using logistic regression. The performance of the models was evaluated via receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curves, and decision curve analysis. A multivariable Cox regression-derived nomogram was used to predict operation-free survival (OFS), and the patients were categorized into high- or low-risk groups based on risk stratification. Kaplan-Meier curves were used to compare OFS between the two groups.
During a follow-up period of at least one year, 962 of 1785 (53.89 %) patients underwent TKR. Age, presence of radiographic KOA (RKOA), and Kellgren-Lawrence (KL) grading were identified as independent risk factors for OFS. The combined RKOA model (including age, presence of RKOA, and Radscore; AUC = 0.969) and combined KL model (including age, KL grading, and Radscore; AUC = 0.968) showed similar performance, with both significantly outperforming other models (p < 0.001). The 1-, 2-, and 3-year AUCs for the RKOA nomogram were 0.891, 0.916, and 0.920, respectively, whereas those for the KL nomogram were 0.890, 0.914, and 0.931. The thresholds of 68.92 (RKOA nomogram) and 64.41 (KL nomogram) were derived from the median nomogram scores and used to stratify patients into high- and low-risk groups. K-M curves demonstrated that the risk stratification system effectively distinguished between high- and low-risk groups, with the high-risk group being more likely to require TKR.
Two nomograms incorporating age, RKOA (or KL grading), and Radscore were developed to predict 3-years OFS for KOA patients and establish risk thresholds, potentially guiding personalized non-surgical treatments during the OFS period.
全膝关节置换术（TKR）手术的发生率有所上升，部分原因是医疗政策导致了过早且可能不必要的干预。这引发了人们对手术过度的潜在趋势的担忧。
本研究旨在提出一种基于数字X线摄影（DR）影像组学的预测模型，以客观评估膝关节骨关节炎（KOA）患者进行TKR手术的必要性，并改善风险分层，从而避免不必要的手术。
对2017年1月至2022年12月期间的1785例KOA患者进行回顾性研究。从DR图像中提取影像组学特征以量化病变表型，然后进行两步特征选择以得出可靠的特征。使用独立危险因素和影像组学特征构建多个模型，并使用逻辑回归对这些模型进行验证。通过受试者工作特征（ROC）曲线、曲线下面积（AUC）、校准曲线和决策曲线分析来评估模型的性能。使用多变量Cox回归得出的列线图来预测无手术生存期（OFS），并根据风险分层将患者分为高风险或低风险组。采用Kaplan-Meier曲线比较两组之间的OFS。
在至少一年的随访期内，1785例患者中有962例（53.89%）接受了TKR。年龄、影像学KOA（RKOA）的存在以及Kellgren-Lawrence（KL）分级被确定为OFS的独立危险因素。联合RKOA模型（包括年龄、RKOA的存在和Radscore；AUC = 0.969）和联合KL模型（包括年龄、KL分级和Radscore；AUC = 0.968）表现出相似的性能，两者均显著优于其他模型（p < 0.001）。RKOA列线图的1年、2年和3年AUC分别为0.891、0.916和0.920，而KL列线图的AUC分别为0.890、0.914和0.931。68.92（RKOA列线图）和64.41（KL列线图）的阈值来自列线图分数的中位数，用于将患者分为高风险和低风险组。K-M曲线表明，风险分层系统有效地区分了高风险和低风险组，高风险组更有可能需要TKR。
开发了两个包含年龄、RKOA（或KL分级）和Radscore的列线图，以预测KOA患者的3年OFS并建立风险阈值，这可能在OFS期间指导个性化的非手术治疗。
