Machine-Learning-Derived Nomogram Based on 3D Radiomic Features and Clinical Factors Predicts Progression-Free Survival in Lung Adenocarcinoma

BACKGROUND: To establish a machine-learning-derived nomogram based on radiomic features and clinical factors to predict post-surgical 2-year progression-free survival (PFS) in patients with lung adenocarcinoma. METHODS: Patients with >2 years post-surgical prognosis results of lung adenocarcinoma...

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Detalles Bibliográficos
Autores principales: Liu, Guixue, Xu, Zhihan, Zhang, Yaping, Jiang, Beibei, Zhang, Lu, Wang, Lingyun, de Bock, Geertruida H., Vliegenthart, Rozemarijn, Xie, Xueqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8260977/
https://www.ncbi.nlm.nih.gov/pubmed/34249741
http://dx.doi.org/10.3389/fonc.2021.692329

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8260977/
https://www.ncbi.nlm.nih.gov/pubmed/34249741
http://dx.doi.org/10.3389/fonc.2021.692329

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