Liangjing Lyu
1*
Jing Ren
1*
Yang Song
2
Weiwu Yao
1*The final, formatted version of the article will be published soon.
ORIGINAL RESEARCH article
Front. Sports Act. Living
Sec. Injury Prevention and Rehabilitation
Volume 7 - 2025 |
doi: 10.3389/fspor.2025.1535519
This article is part of the Research Topic Advancing Musculoskeletal Injury Management in Sports Medicine Through MRI Innovations View all 3 articles
A Machine Learning-Based Radiomics Approach for Differentiating Patellofemoral Osteoarthritis from Non-Patellofemoral Osteoarthritis Using Q-Dixon MRI
Provisionally accepted- 1 Tongren Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- 2 Siemens Healthineers (China), Shanghai, Shanghai Municipality, China
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.
Keywords: anterior knee pain, Patellofemoral osteoarthritis, q-Dixon MRI, Radiomics, machine learning, Fat fraction, Quadriceps fat pad
Received: 27 Nov 2024; Accepted: 06 Jan 2025.
Copyright: © 2025 Lyu, Ren, Lu, Zhong, Song, Li and Yao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Liangjing Lyu, Tongren Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
Jing Ren, Tongren Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
Wenjie Lu, Tongren Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
Jingyu Zhong, Tongren Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
Yongliang Li, Tongren Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
Weiwu Yao, Tongren Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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