The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma

Abstract Background This study aims to construct radiomics models based on [18F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models. Method...

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Главные авторы:	Jianxiong Gao, Rong Niu, Yunmei Shi, Xiaoliang Shao, Zhenxing Jiang, Xinyu Ge, Yuetao Wang, Xiaonan Shao
Формат:	Artigo
Язык:	Inglês
Опубликовано:	SpringerOpen 2023-04-01
Серии:	EJNMMI Research
Предметы:	lung adenocarcinoma [18F]FDG PET/CT Radiomics Epidermal growth factor receptor Prediction
Online-ссылка:	https://doi.org/10.1186/s13550-023-00977-4
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