European journal of radiology · 2024
Radiomics-based nomogram for predicting total knee replacement in knee osteoarthritis patients.
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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
Аннотация
BACKGROUND: 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.
PURPOSE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
PURPOSE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.