Prediction of early recurrence of adult‐type diffuse gliomas following radiotherapy using multi‐modal magnetic resonance images
We developed and evaluated a radiomics-based predictive model for early tumor response to radiotherapy, demonstrating excellent performance supported by high AUC values. This model, harnessing radiomic features from multi-modal MRI, showed superior predictive performance compared to single-modal MRI approaches. These results underscore the potential of radiomics in clinical decision support for this disease process.
Authors
- Jacob Wynne
- Hui‐Kuo G. Shu (ORCID: https://orcid.org/0000-0002-4060-0874)
- Richard L. J. Qiu (ORCID: https://orcid.org/0000-0002-7877-1900)
- Xuxin Chen (ORCID: https://orcid.org/0000-0001-8872-8992)
- Elahheh Salari (ORCID: https://orcid.org/0000-0002-0848-8157)
- Xiaofeng Yang (ORCID: https://orcid.org/0000-0001-9023-5855)
- Justin Roper
Institutions
- Emory University (US)
- Piedmont Cancer Institute (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-09-02
- DOI
- https://doi.org/10.1002/mp.17382
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- National Institutes of Health