Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma
Abstract Background To develop a multiclass radiomics model for differentiating chondroid bone tumors using preoperative MRI. Methods This retrospective study included 120 patients (92 enchondromas, 16 low-grade chondrosarcomas, and 12 intermediate-to-high-grade chondrosarcomas) who underwent contra...
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BMC
2025-05-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-14330-6 |
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| author | Hyerim Park Jooyeon Lee Seungeun Lee Joon-Yong Jung |
| author_facet | Hyerim Park Jooyeon Lee Seungeun Lee Joon-Yong Jung |
| author_sort | Hyerim Park |
| collection | DOAJ |
| description | Abstract Background To develop a multiclass radiomics model for differentiating chondroid bone tumors using preoperative MRI. Methods This retrospective study included 120 patients (92 enchondromas, 16 low-grade chondrosarcomas, and 12 intermediate-to-high-grade chondrosarcomas) who underwent contrast-enhanced MRI between 2009 and 2019. Tumor segmentation was manually performed by a musculoskeletal radiologist and validated by a senior radiologist. We used least absolute shrinkage and selection operator (LASSO) and random forest (RF) for feature selection and classification, with and without synthetic minority oversampling technique (SMOTE). Model performance was evaluated using five-fold cross-validation with average precision, accuracy, area under the curve (AUC), and weighted kappa statistics. Results The LASSO + RF model based on all sequences achieved the highest accuracy (0.826 ± 0.065) and AUC (0.967 ± 0.027). The highest mAP (0.750 ± 0.095) was observed in the SMOTE-enhanced T2WI-based model, highlighting the potential impact of class imbalance. Quadratic weighted kappa values ranged from 0.648 to 0.731 across models, indicating substantial agreement with pathological results. Conclusions Preoperative MRI-based radiomics provides a robust method for the classification of chondroid bone tumors, potentially enhancing clinical decision-making. |
| format | Article |
| id | doaj-art-e3689fc32d6c4f92b5303eb097fff6a7 |
| institution | OA Journals |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
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| series | BMC Cancer |
| spelling | doaj-art-e3689fc32d6c4f92b5303eb097fff6a72025-08-20T02:33:31ZengBMCBMC Cancer1471-24072025-05-012511810.1186/s12885-025-14330-6Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcomaHyerim Park0Jooyeon Lee1Seungeun Lee2Joon-Yong Jung3Department of Radiology, College of Medicine, Soonchunhyang University Cheonan Hospital, Soonchunhyang University of KoreaDepartment of Biostatistics and Data Science, UTHealth Houston School of Public HealthDepartment of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaDepartment of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaAbstract Background To develop a multiclass radiomics model for differentiating chondroid bone tumors using preoperative MRI. Methods This retrospective study included 120 patients (92 enchondromas, 16 low-grade chondrosarcomas, and 12 intermediate-to-high-grade chondrosarcomas) who underwent contrast-enhanced MRI between 2009 and 2019. Tumor segmentation was manually performed by a musculoskeletal radiologist and validated by a senior radiologist. We used least absolute shrinkage and selection operator (LASSO) and random forest (RF) for feature selection and classification, with and without synthetic minority oversampling technique (SMOTE). Model performance was evaluated using five-fold cross-validation with average precision, accuracy, area under the curve (AUC), and weighted kappa statistics. Results The LASSO + RF model based on all sequences achieved the highest accuracy (0.826 ± 0.065) and AUC (0.967 ± 0.027). The highest mAP (0.750 ± 0.095) was observed in the SMOTE-enhanced T2WI-based model, highlighting the potential impact of class imbalance. Quadratic weighted kappa values ranged from 0.648 to 0.731 across models, indicating substantial agreement with pathological results. Conclusions Preoperative MRI-based radiomics provides a robust method for the classification of chondroid bone tumors, potentially enhancing clinical decision-making.https://doi.org/10.1186/s12885-025-14330-6RadiomicsChondrosarcomaEnchondromaMagnetic resonance imagingLeast absolute shrinkage and selection operator. |
| spellingShingle | Hyerim Park Jooyeon Lee Seungeun Lee Joon-Yong Jung Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma BMC Cancer Radiomics Chondrosarcoma Enchondroma Magnetic resonance imaging Least absolute shrinkage and selection operator. |
| title | Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma |
| title_full | Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma |
| title_fullStr | Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma |
| title_full_unstemmed | Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma |
| title_short | Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma |
| title_sort | grading chondroid tumors through mri radiomics enchondroma low grade chondrosarcoma and higher grade chondrosarcoma |
| topic | Radiomics Chondrosarcoma Enchondroma Magnetic resonance imaging Least absolute shrinkage and selection operator. |
| url | https://doi.org/10.1186/s12885-025-14330-6 |
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