Radiomics-Based Differentiation of Glioblastoma and Metastatic Disease: Impact of Different T1-Contrast-Enhanced Sequences on Radiomics Features and Model Performance
Radiomics features derived from T1-CE MPRAGE and VIBE sequences are strongly correlated and may have similar overall classification performance for differentiating GB from BM.
Authors
- Nicholas Bradley Larson (ORCID: https://orcid.org/0000-0002-3468-4215)
- Milan Sonka (ORCID: https://orcid.org/0000-0002-9613-9968)
- Neetu Soni (ORCID: https://orcid.org/0000-0003-2082-1634)
- Camila Gadens Zamboni (ORCID: https://orcid.org/0000-0002-2365-7908)
- Girish Bathla (ORCID: https://orcid.org/0000-0003-3170-242X)
- Amit Agarwal (ORCID: https://orcid.org/0000-0001-9139-8007)
- Yanan Liu (ORCID: https://orcid.org/0000-0002-2582-7501)
- Nam H. Le (ORCID: https://orcid.org/0000-0003-2556-7612)
- Honghai Zhang (ORCID: https://orcid.org/0000-0003-0662-9865)
Institutions
- University of Iowa (US)
- Mayo Clinic (US)
- Jacksonville College (US)
- University of Iowa Hospitals and Clinics (US)
- WinnMed (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2024-08-23
- DOI
- https://doi.org/10.3174/ajnr.a8470
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.21