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25.11.2022 | Scientific Article

The diagnostic value of magnetic resonance imaging-based texture analysis in differentiating enchondroma and chondrosarcoma

verfasst von: Atilla Hikmet Cilengir, Sehnaz Evrimler, Tekin Ahmet Serel, Engin Uluc, Ozgur Tosun

Erschienen in: Skeletal Radiology

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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 and methods

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.
Literatur
1.
Zurück zum Zitat WHO Classification of Tumours Editorial Board eds. World Health Organization classification of soft tissue and bone tumours, 5th ed. Lyon: IARC Press, 2020. WHO Classification of Tumours Editorial Board eds. World Health Organization classification of soft tissue and bone tumours, 5th ed. Lyon: IARC Press, 2020.
Metadaten
Titel
The diagnostic value of magnetic resonance imaging-based texture analysis in differentiating enchondroma and chondrosarcoma
verfasst von
Atilla Hikmet Cilengir
Sehnaz Evrimler
Tekin Ahmet Serel
Engin Uluc
Ozgur Tosun
Publikationsdatum
25.11.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
Skeletal Radiology
Print ISSN: 0364-2348
Elektronische ISSN: 1432-2161
DOI
https://doi.org/10.1007/s00256-022-04242-y

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