Predicting pathological grade of stage I pulmonary adenocarcinoma: a CT radiomics approach
All three models performed well in predicting pathological grade, especially the combined model, showing CT radiomics combined with radiological features had the potential to distinguish the pathological grade of early-stage IPA.
Authors
- Huibin Tan (ORCID: https://orcid.org/0000-0002-4426-3991)
- Yuanliang Jiang (ORCID: https://orcid.org/0000-0002-5278-8384)
- Wencai Huang (ORCID: https://orcid.org/0000-0001-7977-8349)
- Jun Chen (ORCID: https://orcid.org/0000-0002-2362-5359)
- Xue Yang
- Bing Deng (ORCID: https://orcid.org/0000-0003-4893-9087)
- Jia-Ni Zou
- Xiaoni Huang
Institutions
- Bayer (United States) (US)
- Wuhan University of Science and Technology (CN)
- General Hospital of Central Theater Command
- Southern Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-09-27
- DOI
- https://doi.org/10.3389/fonc.2024.1406166
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Hubei Province
- Chinese People’s Liberation Army