Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules

OBJECTIVE: To develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics. METHODS: This retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 2...

Descripción completa

Detalles Bibliográficos
Autores principales: Huang, Wenjun, Deng, Heng, Li, Zhaobin, Xiong, Zhanda, Zhou, Taohu, Ge, Yanming, Zhang, Jing, Jing, Wenbin, Geng, Yayuan, Wang, Xiang, Tu, Wenting, Dong, Peng, Liu, Shiyuan, Fan, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10470826/
https://www.ncbi.nlm.nih.gov/pubmed/37664069
http://dx.doi.org/10.3389/fonc.2023.1255007

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10470826/
https://www.ncbi.nlm.nih.gov/pubmed/37664069
http://dx.doi.org/10.3389/fonc.2023.1255007

Ejemplares similares