The diagnostic value of magnetic resonance imaging-based texture analysis in differentiating enchondroma and chondrosarcoma.
Skeletal Radiol
; 52(5): 1039-1049, 2023 May.
Article
en En
| MEDLINE
| ID: mdl-36434265
ABSTRACT
OBJECTIVE:
To assess the diagnostic performance of MRI-based texture analysis for differentiating enchondromas and chondrosarcomas, especially on fat-suppressed proton density (FS-PD) images. MATERIALS ANDMETHODS:
The whole tumor volumes of 23 chondrosarcomas and 24 enchondromas were manually segmented on both FS-PD and T1-weighted images. A total of 861 radiomic features were extracted. SelectKBest was used to select the features. The data were randomly split into training (n = 36) and test (n = 10) for T1-weighted and training (n = 37) and test (n = 10) for FS-PD datasets. Fivefold cross-validation was performed. Fifteen machine learning models were created using the training set. The best models for T1-weighted, FS-PD, and T1-weighted + FS-PD images were selected in terms of accuracy and area under the curve (AUC).RESULTS:
There were 7 men and 16 women in the chondrosarcoma group (mean ± standard deviation age, 45.65 ± 11.24) and 7 men and 17 women in the enchondroma group (mean ± standard deviation age, 46.17 ± 11.79). Naive Bayes was the best model for accuracy and AUC for T1-weighted images (AUC = 0.76, accuracy = 80%, recall = 80%, precision = 80%, F1 score = 80%). The best model for FS-PD images was the K neighbors classifier for accuracy and AUC (AUC = 1.00, accuracy = 80%, recall = 80%, precision = 100%, F1 score = 89%). The best model for T1-weighted + FS-PD images was logistic regression for accuracy and AUC (AUC = 0.84, accuracy = 80%, recall = 60%, precision = 100%, F1 score = 75%).CONCLUSION:
MRI-based machine learning models have promising results in the discrimination of enchondroma and chondrosarcoma based on radiomic features obtained from both FS-PD and T1-weighted images.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias Óseas
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Condroma
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Condrosarcoma
Tipo de estudio:
Diagnostic_studies
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Prognostic_studies
Límite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Skeletal Radiol
Año:
2023
Tipo del documento:
Article
País de afiliación:
Turquía