Radiomics analysis using MR imaging of subchondral bone for identification of knee osteoarthritis

Xue, Zhihao; Wang, Liao; Sun, Qi; Xu, Jia; Liu, Ying; Ai, Songtao; Zhang, Lichi; Liu, Chenglei

doi:10.1186/s13018-022-03314-y

Cited by 16 publications

(15 citation statements)

References 39 publications

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“…The knee joints were divided into three compartments in our study, that is, lateral and medial tibiofemoral compartments as well as patellofemoral space. An MRI radiomics study working on subchondral trabeculae developed their KOA severity assessment from four individual ROIs out of two tibiofemoral compartments of knee joints [29]. Besides diagnosis deduction issues, it is pellucid that a sole plane out of a 3-dimentional system is apt to omit necessary details, and the exact compartment(s)…”

Section: Discussionmentioning

confidence: 99%

Development of Machine Learning Models aiming at Knee Osteoarthritis Diagnosing: an MRI Radiomics Analysis

Cui

LIU

et al. 2023

Preprint

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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 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 Neighbor (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 curve (AUC) of SVM classifier were 0.871, 0.942(0.868-1.000, 95% confidence intervals [CI]) in the validation cohort, and 1.000 and 1.000(1.000–1.000, 95% CI) in the training cohort, respectively. Conclusion The MRI radiomics analysis represented promising performance in non-invasive and preoperative KOA diagnosis, especially when considering all available planes of all three compartments of knee joints.

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Section: Discussionmentioning

confidence: 99%

Development of Machine Learning Models aiming at Knee Osteoarthritis Diagnosing: an MRI Radiomics Analysis

Cui

LIU

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The knee joints were divided into three compartments in our study, that is, lateral and medial tibiofemoral compartments as well as the patellofemoral space. An MRI radiomics study working on subchondral trabeculae developed their KOA severity assessment from four individual ROIs out of two tibiofemoral compartments of knee joints [ 25 ]. Besides diagnosis deduction issues, it is pellucid that a sole plane/ROI out of a 3-dimentional system is apt to omit necessary details, and the exact compartment(s) where the pathological changes of cartilage occur varies from patients and knees [ 2 ].…”

Section: Discussionmentioning

confidence: 99%

Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis

Cui

Liu

Yang³

et al. 2023

J Orthop Surg Res

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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.

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“…Analysis of feature importance, differences in radiomic and radiographic features between two groups (IM lipomas vs. ALTs/WDLSs) were performed using the Bonferroni-corrected Mann–Whitney U test [ 22 , 38 , 39 ].…”

Section: Methodsmentioning

confidence: 99%

Tumor-to-bone distance and radiomic features on MRI distinguish intramuscular lipomas from well-differentiated liposarcomas

et al. 2023

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Background To develop a machine learning model based on tumor-to-bone distance and radiomic features derived from preoperative MRI images to distinguish intramuscular (IM) lipomas and atypical lipomatous tumors/well-differentiated liposarcomas (ALTs/WDLSs) and compared with radiologists. Methods The study included patients with IM lipomas and ALTs/WDLSs diagnosed between 2010 and 2022, and with MRI scans (sequence/field strength: T1-weighted (T1W) imaging at 1.5 or 3.0 Tesla MRI). Manual segmentation of tumors based on the three-dimensional T1W images was performed by two observers to appraise the intra- and interobserver variability. After radiomic features and tumor-to-bone distance were extracted, it was used to train a machine learning model to distinguish IM lipomas and ALTs/WDLSs. Both feature selection and classification steps were performed using Least Absolute Shrinkage and Selection Operator logistic regression. The performance of the classification model was assessed using a tenfold cross-validation strategy and subsequently evaluated using the receiver operating characteristic curve (ROC) analysis. The classification agreement of two experienced musculoskeletal (MSK) radiologists was assessed using the kappa statistics. The diagnosis accuracy of each radiologist was evaluated using the final pathological results as the gold standard. Additionally, we compared the performance of the model and two radiologists in terms of the area under the receiver operator characteristic curves (AUCs) using the Delong’s test. Results There were 68 tumors (38 IM lipomas and 30 ALTs/WDLSs). The AUC of the machine learning model was 0.88 [95% CI 0.72–1] (sensitivity, 91.6%; specificity, 85.7%; and accuracy, 89.0%). For Radiologist 1, the AUC was 0.94 [95% CI 0.87–1] (sensitivity, 97.4%; specificity, 90.9%; and accuracy, 95.0%), and as to Radiologist 2, the AUC was 0.91 [95% CI 0.83–0.99] (sensitivity, 100%; specificity, 81.8%; and accuracy, 93.3%). The classification agreement of the radiologists was 0.89 of kappa value (95% CI 0.76–1). Although the AUC of the model was lower than of two experienced MSK radiologists, there was no statistically significant difference between the model and two radiologists (all P > 0.05). Conclusions The novel machine learning model based on tumor-to-bone distance and radiomic features is a noninvasive procedure that has the potential for distinguishing IM lipomas from ALTs/WDLSs. The predictive features that suggested malignancy were size, shape, depth, texture, histogram, and tumor-to-bone distance.

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Radiomics analysis using MR imaging of subchondral bone for identification of knee osteoarthritis

Cited by 16 publications

References 39 publications

Development of Machine Learning Models aiming at Knee Osteoarthritis Diagnosing: an MRI Radiomics Analysis

Development of Machine Learning Models aiming at Knee Osteoarthritis Diagnosing: an MRI Radiomics Analysis

Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis

Tumor-to-bone distance and radiomic features on MRI distinguish intramuscular lipomas from well-differentiated liposarcomas

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