Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Objectives: To predict the anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients non-invasively with machine learning models that combine clinical, conventional CT and radiomic features. Methods: This retrospective study included 335 lung adenocarcinoma patients who were randoml...

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Detalles Bibliográficos
Autores principales: Song, Lan, Zhu, Zhenchen, Mao, Li, Li, Xiuli, Han, Wei, Du, Huayang, Wu, Huanwen, Song, Wei, Jin, Zhengyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7099003/
https://www.ncbi.nlm.nih.gov/pubmed/32266148
http://dx.doi.org/10.3389/fonc.2020.00369

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7099003/
https://www.ncbi.nlm.nih.gov/pubmed/32266148
http://dx.doi.org/10.3389/fonc.2020.00369

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