Imaging phenotyping using 18F-FDG PET/CT radiomics to predict micropapillary and solid pattern in lung adenocarcinoma

Imaging phenotyping using 18F-FDG PET/CT radiomics to predict micropapillary and solid pattern in lung adenocarcinoma

Abstract Objectives To develop and validate a machine learning model using 18F-FDG PET/CT radiomics signature and clinical features to predict the presence of micropapillary and solid (MP/S) components in lung adenocarcinoma. Methods Eight hundred and forty-six patients who underwent preoperative PE...

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Bibliographic Details
Main Authors: Linyi Zhou, Jinju Sun, He Long, Weicheng Zhou, Renxiang Xia, Yi Luo, Jingqin Fang, Yi Wang, Xiao Chen
Format: Article
Language:English
Published: SpringerOpen 2024-01-01
Series:Insights into Imaging
Subjects:
Positron emission tomography
Computed tomography
Radiomics
Lung adenocarcinoma
Machine learning
Online Access:https://doi.org/10.1186/s13244-023-01573-9

Internet

https://doi.org/10.1186/s13244-023-01573-9

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