Morphological and functional alterations in type 2 diabetes pancreata assessed with MRI-based metrics and [18F]FP-(+)-DTBZ PET
We combined PET imaging of BCM and MRI morphology metrics with a robust machine learning-based variable selection method to extract useful PET- and MRI-based metrics for predicting acute and maximum insulin responses. This synergistic approach offers a novel combination of biomarkers for staging disease and evaluating therapeutic interventions.
Authors
- Yuan Huang (ORCID: https://orcid.org/0000-0003-1751-9039)
- Faranak Ebrahimian Sadabad (ORCID: https://orcid.org/0000-0002-6649-7211)
- Rui Ren (ORCID: https://orcid.org/0000-0001-7506-8438)
- Seyed Faraz Nejati (ORCID: https://orcid.org/0000-0001-5623-543X)
- Jason Bini (ORCID: https://orcid.org/0000-0003-4047-6575)
Institutions
- Yale University (US)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-12-18
- DOI
- https://doi.org/10.3389/fendo.2025.1724340
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00