Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis
Abstract Background To develop and assess the performance of machine learning (ML) models based on magnetic resonance imaging (MRI) radiomics analysis for knee osteoarthritis (KOA) diagnosis. Methods This retrospective study analysed 148 consecutive patients (72 with KOA and 76 without) with availab...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-05-01
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| Series: | Journal of Orthopaedic Surgery and Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13018-023-03837-y |
| _version_ | 1827943312291528704 |
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| author | Tingrun Cui Ruilong Liu Yang Jing Jun Fu Jiying Chen |
| author_facet | Tingrun Cui Ruilong Liu Yang Jing Jun Fu Jiying Chen |
| author_sort | Tingrun Cui |
| collection | DOAJ |
| description | Abstract Background To develop and assess the performance of machine learning (ML) models based on magnetic resonance imaging (MRI) radiomics analysis for knee osteoarthritis (KOA) diagnosis. Methods This retrospective study analysed 148 consecutive patients (72 with KOA and 76 without) with available MRI image data, where radiomics features in cartilage portions were extracted and then filtered. Intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features, and a threshold of 0.8 was set. The training and validation cohorts consisted of 117 and 31 cases, respectively. Least absolute shrinkage and selection operator (LASSO) regression method was employed for feature selection. The ML classifiers were logistic regression (LR), K-nearest neighbour (KNN) and support vector machine (SVM). In each algorithm, ten models derived from all available planes of three joint compartments and their various combinations were, respectively, constructed for comparative analysis. The performance of classifiers was mainly evaluated and compared by receiver operating characteristic (ROC) analysis. Results All models achieved satisfying performances, especially the Final model, where accuracy and area under ROC curve (AUC) of LR classifier were 0.968, 0.983 (0.957–1.000, 95% CI) in the validation cohort, and 0.940, 0.984 (0.969–0.995, 95% CI) in the training cohort, respectively. Conclusion The MRI radiomics analysis represented promising performance in noninvasive and preoperative KOA diagnosis, especially when considering all available planes of all three compartments of knee joints. |
| first_indexed | 2024-03-13T10:13:30Z |
| format | Article |
| id | doaj.art-52a794904d4e4812ad548cbe7affa48d |
| institution | Directory Open Access Journal |
| issn | 1749-799X |
| language | English |
| last_indexed | 2024-03-13T10:13:30Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Orthopaedic Surgery and Research |
| spelling | doaj.art-52a794904d4e4812ad548cbe7affa48d2023-05-21T11:21:42ZengBMCJournal of Orthopaedic Surgery and Research1749-799X2023-05-0118111310.1186/s13018-023-03837-yDevelopment of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysisTingrun Cui0Ruilong Liu1Yang Jing2Jun Fu3Jiying Chen4Medical School of Chinese PLADepartment of Bone and Joint Surgery, Jining No. 2 People’s HospitalHuiying Medical Technology Co. LtdDepartment of Orthopaedics, The First Medical Centre of Chinese PLA General HospitalDepartment of Orthopaedics, The First Medical Centre of Chinese PLA General HospitalAbstract Background To develop and assess the performance of machine learning (ML) models based on magnetic resonance imaging (MRI) radiomics analysis for knee osteoarthritis (KOA) diagnosis. Methods This retrospective study analysed 148 consecutive patients (72 with KOA and 76 without) with available MRI image data, where radiomics features in cartilage portions were extracted and then filtered. Intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features, and a threshold of 0.8 was set. The training and validation cohorts consisted of 117 and 31 cases, respectively. Least absolute shrinkage and selection operator (LASSO) regression method was employed for feature selection. The ML classifiers were logistic regression (LR), K-nearest neighbour (KNN) and support vector machine (SVM). In each algorithm, ten models derived from all available planes of three joint compartments and their various combinations were, respectively, constructed for comparative analysis. The performance of classifiers was mainly evaluated and compared by receiver operating characteristic (ROC) analysis. Results All models achieved satisfying performances, especially the Final model, where accuracy and area under ROC curve (AUC) of LR classifier were 0.968, 0.983 (0.957–1.000, 95% CI) in the validation cohort, and 0.940, 0.984 (0.969–0.995, 95% CI) in the training cohort, respectively. Conclusion The MRI radiomics analysis represented promising performance in noninvasive and preoperative KOA diagnosis, especially when considering all available planes of all three compartments of knee joints.https://doi.org/10.1186/s13018-023-03837-yKOA diagnosisMagnetic resonance imaging (MRI)Machine learningRadiomics |
| spellingShingle | Tingrun Cui Ruilong Liu Yang Jing Jun Fu Jiying Chen Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis Journal of Orthopaedic Surgery and Research KOA diagnosis Magnetic resonance imaging (MRI) Machine learning Radiomics |
| title | Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis |
| title_full | Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis |
| title_fullStr | Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis |
| title_full_unstemmed | Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis |
| title_short | Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis |
| title_sort | development of machine learning models aiming at knee osteoarthritis diagnosing an mri radiomics analysis |
| topic | KOA diagnosis Magnetic resonance imaging (MRI) Machine learning Radiomics |
| url | https://doi.org/10.1186/s13018-023-03837-y |
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