Prognostic value of combining clinical factors, 18 F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study.
Kun-Han LueYu-Hung ChenSung-Chao ChuChih-Bin LinTso-Fu WangShu-Hsin LiuPublished in: Annals of nuclear medicine (2024)
F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.
Keyphrases
- pet ct
- positron emission tomography
- pet imaging
- tyrosine kinase
- computed tomography
- small cell lung cancer
- deep learning
- epidermal growth factor receptor
- end stage renal disease
- ejection fraction
- high intensity
- prognostic factors
- advanced non small cell lung cancer
- artificial intelligence
- magnetic resonance imaging
- machine learning
- quality improvement
- wild type
- image quality