Development and validation of a CT-based radiomics machine learning model for differentiating immune-related interstitial pneumonia
Using the random forest method of machine learning, we successfully constructed a CT-based radiomics CIP differential diagnostic model that can accurately, non-invasively, and rapidly provide clinicians with etiological support for pneumonia diagnosis.
Authors
- Hangming Dong (ORCID: https://orcid.org/0000-0002-9573-0721)
- Wei‐Sheng Chen (ORCID: https://orcid.org/0000-0001-8672-180X)
- Hongli Du (ORCID: https://orcid.org/0000-0002-6419-0160)
- Zeyu Fu (ORCID: https://orcid.org/0000-0002-2076-7597)
- Danhui Huang (ORCID: https://orcid.org/0009-0004-8868-4511)
- Tingyue Luo
- Laiyu Liu (ORCID: https://orcid.org/0000-0001-5827-0802)
- Kaijun Chen (ORCID: https://orcid.org/0009-0000-4860-6608)
- Shuquan Wei (ORCID: https://orcid.org/0000-0003-4044-2244)
- Junjie Xi (ORCID: https://orcid.org/0000-0002-1766-7149)
- Jingze Guo
- Xiaoyu Luo (ORCID: https://orcid.org/0000-0002-8202-2822)
- Yan Wang (ORCID: https://orcid.org/0000-0001-7675-3066)
- Shaoxi Cai (ORCID: https://orcid.org/0000-0002-3141-1457)
- Qiang Xiao (ORCID: https://orcid.org/0000-0002-2841-7049)
Institutions
- The First People's Hospital of Shunde (CN)
- Nanfang Hospital (CN)
- Guangzhou First People's Hospital (CN)
- Guangdong Provincial People's Hospital (CN)
- Southern Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- International Immunopharmacology
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1016/j.intimp.2025.114681
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72