Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach
A radiomics-based multiclass RF model could effectively differentiate three types of pulmonary GGN, which enabled early diagnosis of GGO pulmonary adenocarcinoma.
Authors
- Siqi Tao (ORCID: https://orcid.org/0000-0002-6258-0173)
- Mei‐Hwan Wu (ORCID: https://orcid.org/0000-0002-7074-8087)
- Yuli Yang (ORCID: https://orcid.org/0009-0001-6485-6583)
- Xianqiong Shi
- Junjie Bin
- Meiyun Huang
- Yuguang Liao
Institutions
- Guangdong Medical College (CN)
- Guangzhou First People's Hospital (CN)
- Huizhou Central People's Hospital (CN)
- Guangzhou Medical University (CN)
- Huizhou University (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2024-09-13
- DOI
- https://doi.org/10.1186/s12880-024-01421-2
- Citations
- 7
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.83
Funders
- Guangzhou Municipal Science and Technology Bureau
- Huizhou Science and Technology Bureau