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.
Authors
- Hao Chen (ORCID: https://orcid.org/0009-0009-2070-4666)
- Yi Lin (ORCID: https://orcid.org/0000-0003-2875-7542)
- Qian Yang (ORCID: https://orcid.org/0000-0003-3243-3153)
- Chi‐Hua Tung (ORCID: https://orcid.org/0000-0002-7232-312X)
- Yongfu Shao (ORCID: https://orcid.org/0000-0001-6256-1426)
- Yinghua Zhao (ORCID: https://orcid.org/0000-0002-4879-5047)
- Xiaoping Chen (ORCID: https://orcid.org/0000-0003-3679-8512)
- Zhuoyao Xie
- Xiaqing Chen
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