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

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 for predi...

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Detalles Bibliográficos
Autores principales: Ren, Caiyue, Zhang, Fuquan, Zhang, Jiangang, Song, Shaoli, Sun, Yun, Cheng, Jingyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693151/
https://www.ncbi.nlm.nih.gov/pubmed/38042812
http://dx.doi.org/10.1186/s40001-023-01497-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693151/
https://www.ncbi.nlm.nih.gov/pubmed/38042812
http://dx.doi.org/10.1186/s40001-023-01497-6

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