Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules

PURPOSE: To establish and verify the ability of a radiomics prediction model to distinguish invasive adenocarcinoma (IAC) and minimal invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs). METHODS: We retrospectively analyzed 118 lung GGN images and clinical data from 106 patients...

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Detalles Bibliográficos
Autores principales: Zhao, Fen-hua, Fan, Hong-jie, Shan, Kang-fei, Zhou, Long, Pang, Zhen-zhu, Fu, Chun-long, Yang, Ze-bin, Wu, Mei-kang, Sun, Ji-hong, Yang, Xiao-ming, Huang, Zhao-hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133455/
https://www.ncbi.nlm.nih.gov/pubmed/35646675
http://dx.doi.org/10.3389/fonc.2022.872503

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133455/
https://www.ncbi.nlm.nih.gov/pubmed/35646675
http://dx.doi.org/10.3389/fonc.2022.872503

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