Predicting Malignancy and Invasiveness of Pulmonary Subsolid Nodules on CT Images Using Deep Learning

BACKGROUND: To develop and validate a deep learning–based model on CT images for the malignancy and invasiveness prediction of pulmonary subsolid nodules (SSNs). MATERIALS AND METHODS: This study retrospectively collected patients with pulmonary SSNs treated by surgery in our hospital from 2012 to 2...

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Detalles Bibliográficos
Autores principales: Shen, Tianle, Hou, Runping, Ye, Xiaodan, Li, Xiaoyang, Xiong, Junfeng, Zhang, Qin, Zhang, Chenchen, Cai, Xuwei, Yu, Wen, Zhao, Jun, Fu, Xiaolong
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/PMC8351466/
https://www.ncbi.nlm.nih.gov/pubmed/34381723
http://dx.doi.org/10.3389/fonc.2021.700158

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351466/
https://www.ncbi.nlm.nih.gov/pubmed/34381723
http://dx.doi.org/10.3389/fonc.2021.700158

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