European journal of radiology · 2026
Infrapatellar fat pad MRI radiomics and delta-radiomics for predicting pain progression in knee osteoarthritis.
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Zhang Mengdi, Dang Qin, Wang Zhiqiang, Zong Xizeng, Yu Jun, Cen Han, Cao Peihua, Wang Xiaomeng, Qi Weizhong, Huang Shiqian, Li Shengfa, Li Jia, Zhang Yan, Chen Tianyu, Ruan Guangfeng, Lu Yao, Ding Changhai
Аннотация
BACKGROUND: Pain is the hallmark of knee osteoarthritis (KOA) and a major cause of disability. Identification of individuals at high risk of pain progression may facilitate timely intervention. The infrapatellar fat pad (IPFP) has been implicated in KOA-related pain. We aimed to evaluate whether baseline and longitudinal delta MRI-derived IPFP radiomic features can predict pain progression.
METHODS: This study used the Foundation for the National Institutes of Health (FNIH) Osteoarthritis Biomarker Consortium dataset from the Osteoarthritis Initiative (OAI) for model training and hold-out validation, while the Pivotal OAI MRI Analyses (POMA) dataset was used for independent validation. A total of 919 knees with baseline and 24-month MRI examinations (1838 scans) were included. We extracted the radiomic features of IPFP and developed a baseline radiomics model as well as a longitudinal delta-radiomics model over two years. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PRC), and compared with the clinical model and a whole-joint MRI Osteoarthritis Knee Score (MOAKS) model.
RESULTS: The baseline IPFP radiomics model exhibited stable predictive performance, with AUC-ROC values of 0.731 and 0.732 in the hold-out and independent validation sets, outperforming clinical and MOAKS models. In the independent validation set, the model yielded an AUC-PRC of 0.257, substantially exceeding the positive event prevalence (0.115). Notably, the delta-radiomics model further improved performance, achieving AUC-ROC values of 0.861 and 0.806 in corresponding validation sets and an AUC-PRC of 0.434 in the independent validation set. More importantly, the IPFP radiomic score was confirmed as an independent predictor of KOA pain progression in multivariable regression.
CONCLUSION: Quantitative MRI radiomics of the IPFP provide a promising tool for predicting KOA pain progression.
METHODS: This study used the Foundation for the National Institutes of Health (FNIH) Osteoarthritis Biomarker Consortium dataset from the Osteoarthritis Initiative (OAI) for model training and hold-out validation, while the Pivotal OAI MRI Analyses (POMA) dataset was used for independent validation. A total of 919 knees with baseline and 24-month MRI examinations (1838 scans) were included. We extracted the radiomic features of IPFP and developed a baseline radiomics model as well as a longitudinal delta-radiomics model over two years. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PRC), and compared with the clinical model and a whole-joint MRI Osteoarthritis Knee Score (MOAKS) model.
RESULTS: The baseline IPFP radiomics model exhibited stable predictive performance, with AUC-ROC values of 0.731 and 0.732 in the hold-out and independent validation sets, outperforming clinical and MOAKS models. In the independent validation set, the model yielded an AUC-PRC of 0.257, substantially exceeding the positive event prevalence (0.115). Notably, the delta-radiomics model further improved performance, achieving AUC-ROC values of 0.861 and 0.806 in corresponding validation sets and an AUC-PRC of 0.434 in the independent validation set. More importantly, the IPFP radiomic score was confirmed as an independent predictor of KOA pain progression in multivariable regression.
CONCLUSION: Quantitative MRI radiomics of the IPFP provide a promising tool for predicting KOA pain progression.