Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy
Significant historadiological correlations were identified between CT-based radiomic features and chronic histologic changes in native kidney biopsies. Our findings underscore the potential of CT-based radiomic features and their prediction model for the non-invasive assessment of kidney fibrosis.
Authors
- Woo Yeong Park (ORCID: https://orcid.org/0000-0003-2662-2898)
- Ro Woon Lee (ORCID: https://orcid.org/0000-0002-8678-8059)
- Yaerim Kim (ORCID: https://orcid.org/0000-0003-1596-1528)
- Jin Hyuk Paek (ORCID: https://orcid.org/0000-0001-8875-1260)
- Seungyeup Han (ORCID: https://orcid.org/0000-0002-7561-6534)
- Hyeong Chan Shin (ORCID: https://orcid.org/0000-0002-3716-7678)
- Yoon Ho Choi (ORCID: https://orcid.org/0000-0001-6831-4450)
- Kyubok Jin (ORCID: https://orcid.org/0000-0002-7836-8863)
- Misun Choe (ORCID: https://orcid.org/0000-0002-1412-5360)
- Byoung Je Kim (ORCID: https://orcid.org/0000-0003-2669-1885)
- Kipyo Kim (ORCID: https://orcid.org/0000-0003-4166-1801)
- Jieun Kim (ORCID: https://orcid.org/0000-0002-6214-3889)
Institutions
- Inha University (KR)
- Mayo Clinic in Florida (US)
- Inha University Hospital (KR)
- Keimyung University (KR)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2024-09-27
- DOI
- https://doi.org/10.1186/s12880-024-01434-x
- Citations
- 9
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.64