A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-Dixon MRI
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 retrospecti...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Sports and Active Living |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspor.2025.1535519/full |
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| author | Liangjing Lyu Jing Ren Wenjie Lu Jingyu Zhong Yang Song Yongliang Li Weiwu Yao |
| author_facet | Liangjing Lyu Jing Ren Wenjie Lu Jingyu Zhong Yang Song Yongliang Li Weiwu Yao |
| author_sort | Liangjing Lyu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5dcbfd1ed944427983390a42081c9c3d |
| institution | Kabale University |
| issn | 2624-9367 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Sports and Active Living |
| spelling | doaj-art-5dcbfd1ed944427983390a42081c9c3d2025-01-17T06:51:05ZengFrontiers Media S.A.Frontiers in Sports and Active Living2624-93672025-01-01710.3389/fspor.2025.15355191535519A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-Dixon MRILiangjing Lyu0Jing Ren1Wenjie Lu2Jingyu Zhong3Yang Song4Yongliang Li5Weiwu Yao6Department of Radiology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaMR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, ChinaDepartment of Radiology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaThis 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.https://www.frontiersin.org/articles/10.3389/fspor.2025.1535519/fullanterior knee painpatellofemoral osteoarthritisQ-Dixon MRIradiomicsmachine learningfat fraction |
| spellingShingle | Liangjing Lyu Jing Ren Wenjie Lu Jingyu Zhong Yang Song Yongliang Li Weiwu Yao A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-Dixon MRI Frontiers in Sports and Active Living anterior knee pain patellofemoral osteoarthritis Q-Dixon MRI radiomics machine learning fat fraction |
| title | A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-Dixon MRI |
| title_full | A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-Dixon MRI |
| title_fullStr | A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-Dixon MRI |
| title_full_unstemmed | A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-Dixon MRI |
| title_short | A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-Dixon MRI |
| title_sort | machine learning based radiomics approach for differentiating patellofemoral osteoarthritis from non patellofemoral osteoarthritis using q dixon mri |
| topic | anterior knee pain patellofemoral osteoarthritis Q-Dixon MRI radiomics machine learning fat fraction |
| url | https://www.frontiersin.org/articles/10.3389/fspor.2025.1535519/full |
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