Interpretable Machine Learning Model Based on Superb Microvascular Imaging for Non-Invasive Determination of Crescent Status of IgAN
The SMI radiomics ML model has the capability to accurately predict the crescentic status of IgAN patients, providing effective assistance for clinical treatment decisions.
Authors
- Xiachuan Qin (ORCID: https://orcid.org/0000-0002-8669-3308)
- Xiaoling Liu (ORCID: https://orcid.org/0000-0002-5051-4574)
- Zhou Wang (ORCID: https://orcid.org/0009-0002-5003-5757)
- Yan Tang (ORCID: https://orcid.org/0009-0001-4950-8011)
Institutions
- North Sichuan Medical University (CN)
- Anhui Medical University (CN)
- Beijing Anzhen Hospital (CN)
- Chengdu Second People's Hospital (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
- Nanchong Central Hospital (CN)
Publication Details
- Journal
- Journal of Inflammation Research
- Published
- 2024-09-01
- DOI
- https://doi.org/10.2147/jir.s476716
- Citations
- 3
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.26