Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study

BACKGROUND: Risk prediction models of lung nodules have been built to alleviate the heavy interpretative burden on clinicians. However, the malignancy scores output by those models can be difficult to interpret in a clinically meaningful manner. In contrast, the modeling of lung nodule growth may be...

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Detalles Bibliográficos
Autores principales: Tao, Guangyu, Zhu, Li, Chen, Qunhui, Yin, Lekang, Li, Yamin, Yang, Jiancheng, Ni, Bingbing, Zhang, Zheng, Koo, Chi Wan, Patil, Pradnya D., Chen, Yinan, Yu, Hong, Xu, Yi, Ye, Xiaodan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8902095/
https://www.ncbi.nlm.nih.gov/pubmed/35280310
http://dx.doi.org/10.21037/tlcr-22-59

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8902095/
https://www.ncbi.nlm.nih.gov/pubmed/35280310
http://dx.doi.org/10.21037/tlcr-22-59

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