Key result
Machine learning-based quadriceps fat pad radiomics using Q-Dixon MRI accurately distinguished patellofemoral osteoarthritis from non-patellofemoral osteoarthritis, achieving an AUC of 0.885.
Why the study?
To assess the utility of machine learning-based quadriceps fat pad radiomics in distinguishing patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-Dixon MRI in patients presenting with anterior knee pain.
Does machine learning-based QFP radiomics using Q-Dixon MRI accurately distinguish PFOA from non-PFOA in patients with anterior knee pain?
Cross-Sectional (n=215)
No
Does machine learning-based QFP radiomics using Q-Dixon MRI accurately distinguish PFOA from non-PFOA in patients with anterior knee pain?
Effect estimate: AUC 0.885 (95% CI 0.768-1.000)
p-value: p=<0.001
Machine learning-based QFP radiomics using Q-Dixon MRI provides a highly accurate, non-invasive diagnostic approach for distinguishing PFOA from non-PFOA in patients with anterior knee pain.
This prospective diagnostic study aimed to assess the utility of machine learning-based quadriceps fat pad (QFP) radiomics in distinguishing patellofemoral osteoarthritis (PFOA) from non-PFOA using Q-Dixon MRI in patients presenting with anterior knee pain. This diagnostic accuracy study retrospectively analyzed data from 215 patients (mean age: 54.2 ± 11.3 years; 113 women). Three predictive models were evaluated: a proton density-weighted image model, a fat fraction model, and a merged model. Feature selection was conducted using analysis of variance, and logistic regression was applied for classification. Data were collected from training, internal, and external test cohorts. Radiomics features were extracted from Q-Dixon MRI sequences to distinguish PFOA from non-PFOA. The diagnostic performance of the three models was compared using the area under the curve (AUC) values analyzed with the Delong test. In the training set (109 patients) and internal test set (73 patients), the merged model exhibited optimal performance, with AUCs of 0.836 [95% confidence interval (CI): 0.762–0.910] and 0.826 (95% CI: 0.722–0.929), respectively. In the external test set (33 patients), the model achieved an AUC of 0.885 (95% CI: 0.768–1.000), with sensitivity and specificity values of 0.833 and 0.933, respectively ( p < 0.001). Fat fraction features exhibited a stronger predictive value than shape-related features. Machine learning-based QFP radiomics using Q-Dixon MRI accurately distinguishes PFOA from non-PFOA, providing a non-invasive diagnostic approach for patients with anterior knee pain.
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Lyu et al. (2025) conducted a cross-sectional in Anterior knee pain and Patellofemoral osteoarthritis (n=215). Machine learning-based QFP radiomics using Q-Dixon MRI (merged model) vs. Conventional PDWI model was evaluated on Differentiation of PFOA from non-PFOA (Area Under the Curve) (AUC 0.885, 95% CI 0.768-1.000, p=<0.001). Machine learning-based quadriceps fat pad radiomics using Q-Dixon MRI accurately distinguished patellofemoral osteoarthritis from non-patellofemoral osteoarthritis, achieving an AUC of 0.885.
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