Intratumor Heterogeneity Features Based on MRI Radiomics for Predicting Lung Metastasis Risk of Osteosarcoma

Motivation: Osteosarcoma is a heterogeneous tumor, and early prediction of lung metastasis is crucial for prognosis. We propose a predictive model based on intratumor heterogeneity (ITH). Goal(s): This study aims to develop an ITH index using MRI-derived radiomics features from osteosarcoma subregions and assess its predictive accuracy for lung metastasis within one year. Approach: A multicenter retrospective study of 320 osteosarcoma patients was conducted. MRI radiomics and ITH features were extracted, and multiple classifiers were tested. Results: The linear regression classifier showed the best performance, with AUC values of 0.875 and 0.843 in the internal and external test sets, respectively. Impact: This study presents an MRI-based ITH model, combined with clinical data, demonstrating significant potential for non-invasive lung metastasis risk assessment in osteosarcoma.

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Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/1414
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Intratumor Heterogeneity Features Based on MRI Radiomics for Predicting Lung Metastasis Risk of Osteosarcoma

Hao Chen, Yi Lin, Qian Yang, Chi‐Hua Tung et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

Intratumor Heterogeneity Features Based on MRI Radiomics for Predicting Lung Metastasis Risk of Osteosarcoma

Hao Chen, Yi Lin, Qian Yang, Chi‐Hua Tung, Yongfu Shao, Yinghua Zhao, Xiaoping Chen, Zhuoyao Xie, Xiaqing Chen
article en

Abstract

Motivation: Osteosarcoma is a heterogeneous tumor, and early prediction of lung metastasis is crucial for prognosis. We propose a predictive model based on intratumor heterogeneity (ITH). Goal(s): This study aims to develop an ITH index using MRI-derived radiomics features from osteosarcoma subregions and assess its predictive accuracy for lung metastasis within one year. Approach: A multicenter retrospective study of 320 osteosarcoma patients was conducted. MRI radiomics and ITH features were extracted, and multiple classifiers were tested. Results: The linear regression classifier showed the best performance, with AUC values of 0.875 and 0.843 in the internal and external test sets, respectively. Impact: This study presents an MRI-based ITH model, combined with clinical data, demonstrating significant potential for non-invasive lung metastasis risk assessment in osteosarcoma.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 63%
Radiomics and Machine Learning in Medical Imaging
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