Development and Validation of a Risk Stratification Model of Pulmonary Ground-Glass Nodules Based on Complementary Lung-RADS 1.1 and Deep Learning Scores

PURPOSE: To assess the value of novel deep learning (DL) scores combined with complementary lung imaging reporting and data system 1.1 (cLung-RADS 1.1) in managing the risk stratification of ground-glass nodules (GGNs) and therefore improving the efficiency of lung cancer (LC) screening in China. MA...

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Detalles Bibliográficos
Autores principales: Meng, Qingcheng, Li, Bing, Gao, Pengrui, Liu, Wentao, Zhou, Peijin, Ding, Jia, Zhang, Jiaqi, Ge, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Public Health
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168898/
https://www.ncbi.nlm.nih.gov/pubmed/35677762
http://dx.doi.org/10.3389/fpubh.2022.891306

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168898/
https://www.ncbi.nlm.nih.gov/pubmed/35677762
http://dx.doi.org/10.3389/fpubh.2022.891306

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