Predicting higher-risk growth patterns in invasive lung adenocarcinoma with multiphase multidetector computed tomography and 18F-fluorodeoxyglucose PET radiomics
The combined Rad-Clin model can predict the higher-risk growth patterns of invasive adenocarcinoma (IAC). This approach could help determine individual therapeutic strategies for IAC patients by distinguishing predominant growth patterns with high risk.
Authors
- Yi Luo (ORCID: https://orcid.org/0000-0003-2519-5900)
- Jingqin Fang
- Peng Zhong (ORCID: https://orcid.org/0000-0002-3199-2319)
- Huan Liu (ORCID: https://orcid.org/0009-0004-2569-3736)
- Xiaoguang Li (ORCID: https://orcid.org/0000-0002-3345-1313)
- Jinju Sun (ORCID: https://orcid.org/0000-0002-4277-5686)
- Xiao Chen
- Suihan Liu
Institutions
- Army Medical University (CN)
- Daping Hospital (CN)
- United Imaging Healthcare (China) (CN)
Publication Details
- Journal
- Nuclear Medicine Communications
- Published
- 2024-11-21
- DOI
- https://doi.org/10.1097/mnm.0000000000001931
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00