Academic radiology · 2026
Development of Novel and Interpretable Automated Models for Predicting Total Knee Replacement in Knee Osteoarthritis: An International Multicenter Study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Zhao Fanfan, Chen Yao, Zhou Xiaoyue, Chen Xiaobo, Han Chaokang, Lu Xiangjun, Song Yaxuan, Lin Chunmiao, Gu Ruozhen, Yang Fan, Huang Shaoxuan, Hong Minping, He Dong
Аннотация
RATIONALE AND OBJECTIVES: Subtle changes in the infrapatellar fat pad (IPFP) enable prediction and quantification of knee osteoarthritis (KOA). This study aimed to develop a bimodal model for predicting the risk of KOA progression to total knee replacement (TKR).
MATERIALS AND METHODS: A total of 4039 eligible participants from the Osteoarthritis Initiative (OAI) database were retrospectively enrolled in this study, who were further divided into the training, testing, and internal validation cohorts. Additionally, datasets from the Center D and Center E hospitals were collected as external validation cohorts I and II. Based on the optimal radiomic features of the IPFP and KL grading factors, the Fatpad-Score, KL model, and a bimodal model integrating both were developed, respectively. The predictive performance of models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and diagnostic metrics.
RESULTS: Compared with the Fatpad-score (AUC = 0.848; 95% confidence interval [95% CI]: 0.773-0.916) and KL grading model (AUC = 0.801; 95% CI: 0.726-0.868), the bimodal model outperformed both in internal validation, achieving an AUC of 0.903 (95% CI: 0.846-0.950) and accuracy of 0.871. Pairwise comparison of AUC via the net reclassification improvement (NRI) test further confirmed that the bimodal model had enhanced performance (all P < 0.001). The bimodal model achieved accuracies of 0.918 and 1.000 in external validation cohorts I and II, respectively.
CONCLUSION: The proposed bimodal model better predicted the risk of TKR in KOA patients than did the Fatpad-score and the K-L grading model.
MATERIALS AND METHODS: A total of 4039 eligible participants from the Osteoarthritis Initiative (OAI) database were retrospectively enrolled in this study, who were further divided into the training, testing, and internal validation cohorts. Additionally, datasets from the Center D and Center E hospitals were collected as external validation cohorts I and II. Based on the optimal radiomic features of the IPFP and KL grading factors, the Fatpad-Score, KL model, and a bimodal model integrating both were developed, respectively. The predictive performance of models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and diagnostic metrics.
RESULTS: Compared with the Fatpad-score (AUC = 0.848; 95% confidence interval [95% CI]: 0.773-0.916) and KL grading model (AUC = 0.801; 95% CI: 0.726-0.868), the bimodal model outperformed both in internal validation, achieving an AUC of 0.903 (95% CI: 0.846-0.950) and accuracy of 0.871. Pairwise comparison of AUC via the net reclassification improvement (NRI) test further confirmed that the bimodal model had enhanced performance (all P < 0.001). The bimodal model achieved accuracies of 0.918 and 1.000 in external validation cohorts I and II, respectively.
CONCLUSION: The proposed bimodal model better predicted the risk of TKR in KOA patients than did the Fatpad-score and the K-L grading model.