Stable and discriminating OCT‐derived radiomics features for predicting anti‐VEGF treatment response in diabetic macular edema
Our study suggests that incorporating both stable and discriminatory texture-based radiomic features extracted from fluid and retinal tissue compartments of OCT scans, a more generalized radiomic classifier can be developed to predict therapeutic response in DME. Also, the feature stability was found to be a function of the spatial location within the eye from where the features were extracted.
Authors
- Sudeshna Sil Kar (ORCID: https://orcid.org/0000-0001-5790-5132)
- Anant Madabhushi (ORCID: https://orcid.org/0000-0002-5741-0399)
- Hasan Cetin (ORCID: https://orcid.org/0000-0001-9492-4957)
- Sunil K. Srivastava (ORCID: https://orcid.org/0000-0002-0398-8806)
- Justis P. Ehlers (ORCID: https://orcid.org/0000-0001-6763-7768)
Institutions
- Novartis (Switzerland) (CH)
- Cleveland Clinic (US)
- Emory University (US)
- Atlanta Medical Center (US)
- Novartis (United States) (US)
- Advanced Imaging Research (United States) (US)
- Atlanta VA Medical Center (US)
- Cleveland Eye Clinic (US)
- Center for Excellence in Education (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-03-14
- DOI
- https://doi.org/10.1002/mp.17695
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- National Cancer Institute
- National Institute of Biomedical Imaging and Bioengineering
- National Center for Research Resources
- DOD Prostate Cancer Research Program
- DOD Peer Reviewed Cancer Research Program