Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer

Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer

Abstract Background The main problem of positron emission tomography/computed tomography (PET/CT) for lymph node (LN) staging is the high false positive rate (FPR). Thus, we aimed to explore a clinico-biological-radiomics (CBR) model via machine learning (ML) to reduce FPR and improve the accuracy f...

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Bibliographic Details
Main Authors: Caiyue Ren, Fuquan Zhang, Jiangang Zhang, Shaoli Song, Yun Sun, Jingyi Cheng
Format: Article
Language:English
Published: BMC 2023-12-01
Series:European Journal of Medical Research
Subjects:
Clinico-biological-radiomics
Machine learning
[18F]FDG-PET/CT
False positive rate
Mediastinal–hilar lymph nodes
Online Access:https://doi.org/10.1186/s40001-023-01497-6

Internet

https://doi.org/10.1186/s40001-023-01497-6

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