Combining the prognostic values of entropy-based heterogeneity features from 18F-fluorodeoxyglucose PET and transmission computed tomography using machine learning in patients with lung adenocarcinoma undergoing curative surgery
Our preliminary results suggest that integrating prognostic values from 18 F-FDG PET and CT-based heterogeneity features with clinical prognosticators is feasible and may support personalized treatment strategies for patients with resectable lung adenocarcinoma.
Authors
- Pau-Yuan Chang
- Bee-Song Chang
- Chih-Bin Lin
- Sung-Chao Chu
- Shu-Hsin Liu
- Yu-Hung Chen
- Kun-Han Lue
Institutions
- Tzu Chi University (TW)
- Tzu Chi Foundation (TW)
- Buddhist Tzu Chi Medical Foundation (US)
Publication Details
- Journal
- Nuclear Medicine Communications
- Published
- 2025-12-25
- DOI
- https://doi.org/10.1097/mnm.0000000000002098
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00